%0 Electronic Article %T Non-parametric Lagrangian biasing from the insights of neural nets %A Wu, Xiaohan %A Munoz, Julian B. %A Eisenstein, Daniel J. %J arXiv e-prints %D 2022 %8 December 01, 2022 %P arXiv:2212.08095 %K Astrophysics - Cosmology and Nongalactic Astrophysics %U https://ui.adsabs.harvard.edu/abs/2022arXiv221208095W %X We present a Lagrangian model of galaxy clustering bias in which we train a neural net using the local properties of the smoothed initial density field to predict the late-time mass-weighted halo field. By fitting the mass-weighted halo field in the AbacusSummit simulations at z=0.5, we find that including three coarsely spaced smoothing scales gives the best recovery of the halo power spectrum. Adding more smoothing scales may lead to 2-5% underestimation of the large-scale power and can cause the neural net to overfit. We find that the fitted halo-to-mass ratio can be well described by two directions in the original high-dimension feature space. Projecting the original features into these two principal components and re-training the neural net either reproduces the original training result, or outperforms it with a better match of the halo power spectrum. The elements of the principal components are unlikely to be assigned physical meanings, partly owing to the features being highly correlated between different smoothing scales. Our work illustrates a potential need to include multiple smoothing scales when studying galaxy bias, and this can be done easily with machine-learning methods that can take in high dimensional input feature space. %R 10.48550/arXiv.2212.08095 %= eprint: arXiv:2212.08095 %0 Electronic Article %T Spectroscopy of four metal-poor galaxies beyond redshift ten %A Curtis-Lake, Emma %A Carniani, Stefano %A Cameron, Alex %A Charlot, Stephane %A Jakobsen, Peter %A Maiolino, Roberto %A Bunker, Andrew %A Witstok, Joris %A Smit, Renske %A Chevallard, Jacopo %A Willott, Chris %A Ferruit, Pierre %A Arribas, Santiago %A Bonaventura, Nina %A Curti, Mirko %A D'Eugenio, Francesco %A Franx, Marijn %A Giardino, Giovanna %A Looser, Tobias J. %A Lützgendorf, Nora %A Maseda, Michael V. %A Rawle, Tim %A Rix, Hans-Walter %A Rodriguez del Pino, Bruno %A Übler, Hannah %A Sirianni, Marko %A Dressler, Alan %A Egami, Eiichi %A Eisenstein, Daniel J. %A Endsley, Ryan %A Hainline, Kevin %A Hausen, Ryan %A Johnson, Benjamin D. %A Rieke, Marcia %A Robertson, Brant %A Shivaei, Irene %A Stark, Daniel P. %A Tacchella, Sandro %A Williams, Christina C. %A Willmer, Christopher N. A. %A Bhatawdekar, Rachana %A Bowler, Rebecca %A Boyett, Kristan %A Chen, Zuyi %A de Graaff, Anna %A Helton, Jakob M. %A Hviding, Raphael E. %A Jones, Gareth C. %A Kumari, Nimisha %A Lyu, Jianwei %A Nelson, Erica %A Perna, Michele %A Sandles, Lester %A Saxena, Aayush %A Suess, Katherine A. %A Sun, Fengwu %A Topping, Michael W. %A Wallace, Imaan E. B. %A Whitler, Lily %J arXiv e-prints %D 2022 %8 December 01, 2022 %P arXiv:2212.04568 %K Astrophysics - Astrophysics of Galaxies %U https://ui.adsabs.harvard.edu/abs/2022arXiv221204568C %X Finding and characterising the first galaxies that illuminated the early Universe at cosmic dawn is pivotal to understand the physical conditions and the processes that led to the formation of the first stars. In the first few months of operations, imaging from the James Webb Space Telescope (JWST) have been used to identify tens of candidates of galaxies at redshift (z) greater than 10, less than 450 million years after the Big Bang. However, none of these candidates has yet been confirmed spectroscopically, leaving open the possibility that they are actually low-redshift interlopers. Here we present spectroscopic confirmation and analysis of four galaxies unambiguously detected at redshift 10.312$ systems both discovered and spectroscopically confirmed by JWST. Using stellar population modelling, we find the galaxies typically contain a hundred million solar masses in stars, in stellar populations that are less than one hundred million years old. The moderate star formation rates and compact sizes suggest elevated star formation rate surface densities, a key indicator of their formation pathways. Taken together, these measurements show that the first galaxies contributing to cosmic reionisation formed rapidly and with intense internal radiation fields. %R 10.48550/arXiv.2212.04480 %= eprint: arXiv:2212.04480 %0 Electronic Article %T Constraining accuracy of pairwise velocities using scale-free models %A Maleubre, Sara %A Eisenstein, Daniel J. %A Garrison, Lehman H. %A Joyce, Michael %J arXiv e-prints %D 2022 %8 November 01, 2022 %P arXiv:2211.07607 %K Astrophysics - Cosmology and Nongalactic Astrophysics %U https://ui.adsabs.harvard.edu/abs/2022arXiv221107607M %X We present a continuation of an analysis that aims to quantify resolution of $N$-body simulations by exploiting large (up to $N=4096^3$) simulations of scale-free cosmologies run using \Abacus. Here we focus on pairwise velocities of the matter field and of halo centres selected with both the Rockstar and CompaSO algorithms. For the matter field, we find that convergence at the $1\%$ level of the mean relative pairwise velocity can be demonstrated over a range of scales, evolving from a few times the grid spacing at early times to slightly below this scale at late times. Down to scales of order the force smoothing, convergence is obtained at $\sim5\%$ precision, and shows a behaviour indicating asymptotic stable clustering. We also infer for LCDM simulations conservative estimates on the evolution of the lower cut-off to resolution (at $1\%$ and $5\%$ precision) as a function of redshift. For the halos, we establish convergence, for both Rockstar and CompaSO, of mass functions at the $1\%$ precision level and of the mean pair-wise velocities (and also 2PCF) at the $2\%$ level. We find that of the two halo finders, Rockstar exhibits greater self-similarity, specially on small scales and small masses. We also give resolution limits expressed as a minimum particle number per halo in a form that can be directly extrapolated to LCDM. %R 10.48550/arXiv.2211.07607 %= eprint: arXiv:2211.07607 %0 Journal Article %T Overview of the Instrumentation for the Dark Energy Spectroscopic Instrument %A Abareshi, B. %A Aguilar, J. %A Ahlen, S. %A Alam, Shadab %A Alexander, David M. %A Alfarsy, R. %A Allen, L. %A Allende Prieto, C. %A Alves, O. %A Ameel, J. %A Armengaud, E. %A Asorey, J. %A Aviles, Alejandro %A Bailey, S. %A Balaguera-Antolínez, A. %A Ballester, O. %A Baltay, C. %A Bault, A. %A Beltran, S. F. %A Benavides, B. %A BenZvi, S. %A Berti, A. %A Besuner, R. %A Beutler, Florian %A Bianchi, D. %A Blake, C. %A Blanc, P. %A Blum, R. %A Bolton, A. %A Bose, S. %A Bramall, D. %A Brieden, S. %A Brodzeller, A. %A Brooks, D. %A Brownewell, C. %A Buckley-Geer, E. %A Cahn, R. N. %A Cai, Z. %A Canning, R. %A Capasso, R. %A Carnero Rosell, A. %A Carton, P. %A Casas, R. %A Castander, F. J. %A Cervantes-Cota, J. L. %A Chabanier, S. %A Chaussidon, E. %A Chuang, C. %A Circosta, C. %A Cole, S. %A Cooper, A. P. %A da Costa, L. %A Cousinou, M. -C. %A Cuceu, A. %A Davis, T. M. %A Dawson, K. %A de la Cruz-Noriega, R. %A de la Macorra, A. %A de Mattia, A. %A Della Costa, J. %A Demmer, P. %A Derwent, M. %A Dey, A. %A Dey, B. %A Dhungana, G. %A Ding, Z. %A Dobson, C. %A Doel, P. %A Donald-McCann, J. %A Donaldson, J. %A Douglass, K. %A Duan, Y. %A Dunlop, P. %A Edelstein, J. %A Eftekharzadeh, S. %A Eisenstein, D. J. %A Enriquez-Vargas, M. %A Escoffier, S. %A Evatt, M. %A Fagrelius, P. %A Fan, X. %A Fanning, K. %A Fawcett, V. A. %A Ferraro, S. %A Ereza, J. %A Flaugher, B. %A Font-Ribera, A. %A Forero-Romero, J. E. %A Frenk, C. S. %A Fromenteau, S. %A Gänsicke, B. T. %A Garcia-Quintero, C. %A Garrison, L. %A Gaztañaga, E. %A Gerardi, F. %A Gil-Marín, H. %A Gontcho, S. Gontcho A. %A Gonzalez-Morales, Alma X. %A Gonzalez-de-Rivera, G. %A Gonzalez-Perez, V. %A Gordon, C. %A Graur, O. %A Green, D. %A Grove, C. %A Gruen, D. %A Gutierrez, G. %A Guy, J. %A Hahn, C. %A Harris, S. %A Herrera, D. %A Herrera-Alcantar, Hiram K. %A Honscheid, K. %A Howlett, C. %A Huterer, D. %A Iršič, V. %A Ishak, M. %A Jelinsky, P. %A Jiang, L. %A Jimenez, J. %A Jing, Y. P. %A Joyce, R. %A Jullo, E. %A Juneau, S. %A Karaçaylı, N. G. %A Karamanis, M. %A Karcher, A. %A Karim, T. %A Kehoe, R. %A Kent, S. %A Kirkby, D. %A Kisner, T. %A Kitaura, F. %A Koposov, S. E. %A Kovács, A. %A Kremin, A. %A Krolewski, Alex %A L'Huillier, B. %A Lahav, O. %A Lambert, A. %A Lamman, C. %A Lan, Ting-Wen %A Landriau, M. %A Lane, S. %A Lang, D. %A Lange, J. U. %A Lasker, J. %A Guillou, L. Le %A Leauthaud, A. %A Le Van Suu, A. %A Levi, Michael E. %A Li, T. S. %A Magneville, C. %A Manera, M. %A Manser, Christopher J. %A Marshall, B. %A Martini, Paul %A McCollam, W. %A McDonald, P. %A Meisner, Aaron M. %A Mena-Fernández, J. %A Meneses-Rizo, J. %A Mezcua, M. %A Miller, T. %A Miquel, R. %A Montero-Camacho, P. %A Moon, J. %A Moustakas, J. %A Mueller, E. %A Muñoz-Gutiérrez, Andrea %A Myers, Adam D. %A Nadathur, S. %A Najita, J. %A Napolitano, L. %A Neilsen, E. %A Newman, Jeffrey A. %A Nie, J. D. %A Ning, Y. %A Niz, G. %A Norberg, P. %A Noriega, Hernán E. %A O'Brien, T. %A Obuljen, A. %A Palanque-Delabrouille, N. %A Palmese, A. %A Zhiwei, P. %A Pappalardo, D. %A Peng, X. %A Percival, W. J. %A Perruchot, S. %A Pogge, R. %A Poppett, C. %A Porredon, A. %A Prada, F. %A Prochaska, J. %A Pucha, R. %A Pérez-Fernández, A. %A Pérez-Ràfols, I. %A Rabinowitz, D. %A Raichoor, A. %A Ramirez-Solano, S. %A Ramírez-Pérez, César %A Ravoux, C. %A Reil, K. %A Rezaie, M. %A Rocher, A. %A Rockosi, C. %A Roe, N. A. %A Roodman, A. %A Ross, A. J. %A Rossi, G. %A Ruggeri, R. %A Ruhlmann-Kleider, V. %A Sabiu, C. G. %A Safonova, S. %A Said, K. %A Saintonge, A. %A Catonga, Javier Salas %A Samushia, L. %A Sanchez, E. %A Saulder, C. %A Schaan, E. %A Schlafly, E. %A Schlegel, D. %A Schmoll, J. %A Scholte, D. %A Schubnell, M. %A Secroun, A. %A Seo, H. %A Serrano, S. %A Sharples, Ray M. %A Sholl, Michael J. %A Silber, Joseph Harry %A Silva, D. R. %A Sirk, M. %A Siudek, M. %A Smith, A. %A Sprayberry, D. %A Staten, R. %A Stupak, B. %A Tan, T. %A Tarlé, Gregory %A Tie, Suk Sien %A Tojeiro, R. %A Ureña-López, L. A. %A Valdes, F. %A Valenzuela, O. %A Valluri, M. %A Vargas-Magaña, M. %A Verde, L. %A Walther, M. %A Wang, B. %A Wang, M. S. %A Weaver, B. A. %A Weaverdyck, C. %A Wechsler, R. %A Wilson, Michael J. %A Yang, J. %A Yu, Y. %A Yuan, S. %A Yèche, Christophe %A Zhang, H. %A Zhang, K. %A Zhao, Cheng %A Zhou, Rongpu %A Zhou, Zhimin %A Zou, H. %A Zou, J. %A Zou, S. %A Zu, Y. %+ AA(National Optical Astronomy Observatory, Arizona), AB(Lawrence Berkeley National Laboratory, California), AC(Physics Dept., Boston University, 590 Commonwealth Avenue, Boston, MA 02215, USA), AD(Royal Observatory Edinburgh), AE(Durham University, Department of Physics; Durham University, Department of Physics), AF(University of Portsmouth, Institute of Cosmology and Gravitation), AG(National Optical Astronomy Observatory, Arizona), AH(Instituto de Astrofísica de Canarias, C/ Vía Láctea, s/n, E-38205 San Cristóbal de La Laguna, Santa Cruz de Tenerife, Spain), AI(University of Michigan, Department of Physics), AJ(University of Michigan, Department of Physics; University of Michigan, Department of Physics), AK(Institut de Recherche sur les Lois Fondamentales de l'Universe), AL(Centro de Investigaciones Energeticas Medioambientales y Tecnologicas, Spain), AM(Consejo Nacional de Ciencia y Tecnología, Av. 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San Marcos 11-Atenas 202 . Magdalena Contreras, 10720. Ciudad de México, México), GW(Durham University, Department of Physics; Durham University, Department of Physics), GX(UNAM, Institute of Physics), GY(The Ohio State University, Department of Astronomy), GZ(University of Waterloo, Canada; University of Zurich, Switzerland), HA(Lawrence Berkeley National Laboratory, California; Institut de Recherche sur les Lois Fondamentales de l'Universe), HB(University of California, Berkeley, 110 Sproul Hall #5800 Berkeley, CA 94720, USA), HC(Kavli Institute for Astronomy and Astrophysics at Peking University, PKU, 5 Yiheyuan Road, Haidian District, Beijing 100871, People's Republic of China), HD(The Ohio State University, Department of Astronomy), HE(CAS, National Astronomical Observatories), HF(University of Waterloo, Department of Physics and Astronomy; Perimeter Institute for Theoretical Physics, Canada; University of Waterloo, Canada), HG(Aix Marseille Univ, CNRS, OHP, F-04870 Saint-Michel-l'Observatoire, France), HH(The Ohio State University, Department of Astronomy), HI(Lawrence Berkeley National Laboratory, California; University of California, Berkeley, Space Sciences Laboratory; -), HJ(The Ohio State University, Department of Astronomy; The Ohio State University, Department of Physics), HK(Institute of Astrophysics of Andalusia), HL(Department of Astronomy and Astrophysics, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95065, USA), HM(University of Arizona, Department of Astronomy and Steward Observatory), HN(UNAM, Institute of Physics), HO(Institute for High Energy Physics, Barcelona; Laboratoire de Physique Nucleaire et de Hautes Energies), HP(Physics Department, Yale University, P.O. Box 208120, New Haven, CT 06511, USA), HQ(Lawrence Berkeley National Laboratory, California), HR(UNAM, Institute of Physics), HS(Institute for High Energy Physics, Barcelona), HT(Institut de Recherche sur les Lois Fondamentales de l'Universe), HU(Stanford University, Department of Physics; Stanford Linear Accelerator Center; -), HV(Ohio University, Department of Physics and Astronomy), HW(Institut de Recherche sur les Lois Fondamentales de l'Universe), HX(Department of Astronomy and Astrophysics, University of California, Santa Cruz, 1156 High Street, Santa Cruz, CA 95065, USA; University of California Observatories, 1156 High Street, Sana Cruz, CA 95065, USA), HY(Lawrence Berkeley National Laboratory, California), HZ(Stanford University, Department of Physics; Stanford Linear Accelerator Center; -), IA(The Ohio State University, Department of Astronomy; The Ohio State University, Department of Physics; The Ohio State University, Department of Astronomy), IB(Department of Physics and Astronomy, Sejong University, Seoul, 143-747, Republic of Korea), IC(Swinburne University of Technology, Center for Astrophysics and Supercomputing; -), ID(Institut de Recherche sur les Lois Fondamentales de l'Universe), IE(Natural Science Research Institute, University of Seoul, 163 Seoulsiripdae-ro, Dongdaemun-gu, Seoul, Republic of Korea), IF(Physics Department, Yale University, P.O. Box 208120, New Haven, CT 06511, USA), IG(School of Mathematics and Physics, University of Queensland, 4072, Australia), IH(University College London, Department of Physics and Astronomy), II(UNAM, Institute of Physics), IJ(Abastumani Astrophysical Observatory; Kansas State University, Department of Physics; Ilia State University, Georgia), IK(Centro de Investigaciones Energeticas Medioambientales y Tecnologicas, Spain), IL(Korea Astronomy and Space Science Institute), IM(Lawrence Berkeley National Laboratory, California), IN(Lawrence Livermore National Laboratory, California), IO(Lawrence Berkeley National Laboratory, California), IP(Durham University, Department of Physics), IQ(University College London, Department of Physics and Astronomy), IR(University of Michigan, Department of Physics; University of Michigan, Department of Physics), IS(Center for Particle Physics, Marseille), IT(Ohio University, Department of Physics and Astronomy), IU(Institute of Space Sciences, ICE-CSIC, Campus UAB, Carrer de Can Magrans s/n, E-08913 Bellaterra, Barcelona, Spain), IV(Durham University, Department of Physics; Durham University, Department of Physics), IW(Space Exploration Technologies Corp., 1 Rocket Road, Hawthorne, CA 90250, USA), IX(Lawrence Berkeley National Laboratory, California), IY(University of Texas, San Antonio), IZ(University of California, Berkeley, Space Sciences Laboratory; -), JA(Institute for High Energy Physics, Barcelona), JB(Durham University, Department of Physics), JC(National Optical Astronomy Observatory, Arizona), JD(Southern Methodist University, Texas), JE(National Optical Astronomy Observatory, Arizona), JF(Laboratoire de Physique Nucleaire et de Hautes Energies), JG(University of Michigan, Department of Physics), JH(The Ohio State University, Department of Astronomy; University of California, Santa Barbara, Department of Physics and Astronomy), JI(Scottish Universities Physics Alliance), JJ(Departamento de Física, Universidad de Guanajuato-DCI, C.P. 37150, Leon, Guanajuato, México), JK(National Optical Astronomy Observatory, Arizona), JL(UNAM, Institute of Physics), JM(University of Michigan, Department of Physics; University of Michigan, Department of Astronomy), JN(UNAM, Institute of Physics), JO(Instituto de Cìencias del Cosmoc, (ICCUB) Universidad de Barcelona (IEEC-UB), Martí i Franquès 1, E-08028 Barcelona, Spain; Institució Catalana de Recerca i Estudis Avançats, Passeig de Lluís Companys, 23, E-08010 Barcelona, Spain), JP(University Observatory, Faculty of Physics, Ludwig-Maximilians-Universität, Scheinerstr. 1, D-81677 München, Germany; Excellence Cluster ORIGINS, Boltzmannstrasse 2, D-85748 Garching, Germany), JQ(Department of Astronomy, Tsinghua University, 30 Shuangqing Road, Haidian District, Beijing, 100190, People's Republic of China), JR(Royal Observatory Edinburgh), JS(National Optical Astronomy Observatory, Arizona), JT(University of Michigan, Department of Physics; University of Michigan, Department of Physics), JU(Stanford University, Department of Physics; Stanford Linear Accelerator Center; -), JV(Durham University, Department of Physics), JW(University of Arizona, Department of Astronomy and Steward Observatory), JX(Shanghai Jiaotong University, China), JY(Stanford Linear Accelerator Center), JZ(Institut de Recherche sur les Lois Fondamentales de l'Universe), KA(Kansas State University, Department of Physics), KB(Lawrence Berkeley National Laboratory, California), KC(Department of Astronomy, Tsinghua University, 30 Shuangqing Road, Haidian District, Beijing, 100190, People's Republic of China; Ecole Polytechnique Fédérale de Lausanne, CH-1015 Lausanne, Switzerland), KD(Lawrence Berkeley National Laboratory, California), KE(CAS, National Astronomical Observatories), KF(CAS, National Astronomical Observatories), KG(Department of Astronomy, Tsinghua University, 30 Shuangqing Road, Haidian District, Beijing, 100190, People's Republic of China), KH(Kavli Institute for Astronomy and Astrophysics at Peking University, PKU, 5 Yiheyuan Road, Haidian District, Beijing 100871, People's Republic of China), KI(Shanghai Jiaotong University, China; Shanghai Jiaotong University, China) %J The Astronomical Journal %V 164 %D 2022 %8 November 01, 2022 %P 207 %K Dark energy; Spectroscopy; Astronomical instrumentation; Spectrometers; Optical telescopes; 351; 1558; 799; 1554; 1174; Astrophysics - Instrumentation and Methods for Astrophysics; Astrophysics - Cosmology and Nongalactic Astrophysics %U https://ui.adsabs.harvard.edu/abs/2022AJ....164..207A %X The Dark Energy Spectroscopic Instrument (DESI) embarked on an ambitious 5 yr survey in 2021 May to explore the nature of dark energy with spectroscopic measurements of 40 million galaxies and quasars. DESI will determine precise redshifts and employ the baryon acoustic oscillation method to measure distances from the nearby universe to beyond redshift z > 3.5, and employ redshift space distortions to measure the growth of structure and probe potential modifications to general relativity. We describe the significant instrumentation we developed to conduct the DESI survey. This includes: a wide-field, 3.°2 diameter prime-focus corrector; a focal plane system with 5020 fiber positioners on the 0.812 m diameter, aspheric focal surface; 10 continuous, high-efficiency fiber cable bundles that connect the focal plane to the spectrographs; and 10 identical spectrographs. Each spectrograph employs a pair of dichroics to split the light into three channels that together record the light from 360-980 nm with a spectral resolution that ranges from 2000-5000. We describe the science requirements, their connection to the technical requirements, the management of the project, and interfaces between subsystems. DESI was installed at the 4 m Mayall Telescope at Kitt Peak National Observatory and has achieved all of its performance goals. Some performance highlights include an rms positioner accuracy of better than 0.″1 and a median signal-to-noise ratio of 7 of the [O II] doublet at 8 × 10-17 erg s-1 cm-2 in 1000 s for galaxies at z = 1.4-1.6. We conclude with additional highlights from the on-sky validation and commissioning, key successes, and lessons learned. %R 10.3847/1538-3881/ac882b %= eprint: arXiv:2205.10939 %@ 0004-6256 %0 Electronic Article %T The MillenniumTNG Project: An improved two-halo model for the galaxy-halo connection of red and blue galaxies %A Hadzhiyska, Boryana %A Eisenstein, Daniel %A Hernquist, Lars %A Pakmor, Rüdiger %A Bose, Sownak %A Delgado, Ana Maria %A Contreras, Sergio %A Kannan, Rahul %A White, Simon D. M. %A Springel, Volker %A Frenk, Carlos %A Hernández-Aguayo, César %A Ferlito, Fulvio %A Barrera, Monica %J arXiv e-prints %D 2022 %8 October 01, 2022 %P arXiv:2210.10072 %K Astrophysics - Cosmology and Nongalactic Astrophysics; Astrophysics - Astrophysics of Galaxies %U https://ui.adsabs.harvard.edu/abs/2022arXiv221010072H %X Approximate methods to populate dark matter halos with galaxies are of great utility to large galaxy surveys. However, the limitations of simple halo occupation models (HODs) preclude a full use of small-scale galaxy clustering data and call for more sophisticated models. We study two galaxy populations, luminous red galaxies (LRGs) and star-forming emission-line galaxies (ELGs), at two epochs, $z=1$ and $z=0$, in the large volume, high-resolution hydrodynamical simulation of the MillenniumTNG project. In a partner study we concentrated on the small-scale, one-halo regime down to $r\sim 0.1{\rm Mpc}/h$, while here we focus on modeling galaxy assembly bias in the two-halo regime, $rrsim 1{\rm Mpc}/h$. Interestingly, the ELG signal exhibits scale dependence out to relatively large scales ($r\sim 20{\rm Mpc}/h$), implying that the linear bias approximation for this tracer is invalid on these scales, contrary to common assumptions. The 10-15\% discrepancy present in the standard halo model prescription is only reconciled when we augment our halo occupation model with a dependence on extrinsic halo properties ("shear" being the best-performing one) rather than intrinsic ones (e.g., concentration, peak mass). We argue that this fact constitutes evidence for two-halo galaxy conformity. Including tertiary assembly bias (i.e. a property beyond mass and "shear") is not an essential requirement for reconciling the galaxy assembly bias signal of LRGs, but the combination of external and internal properties is beneficial for recovering ELG the clustering. We find that centrals in low-mass haloes dominate the assembly bias signal of both populations. Finally, we explore the predictions of our model for higher-order statistics such as nearest-neighbor counts. The latter supplies additional information about galaxy assembly bias and can be used to break degeneracies between halo model parameters. %R 10.48550/arXiv.2210.10072 %= eprint: arXiv:2210.10072 %0 Electronic Article %T The MillenniumTNG Project: Refining the one-halo model of red and blue galaxies at different redshifts %A Hadzhiyska, Boryana %A Hernquist, Lars %A Eisenstein, Daniel %A Delgado, Ana Maria %A Bose, Sownak %A Kannan, Rahul %A Pakmor, Rüdiger %A Springel, Volker %A Contreras, Sergio %A Barrera, Monica %A Ferlito, Fulvio %A Hernández-Aguayo, César %A White, Simon D. M. %A Frenk, Carlos %J arXiv e-prints %D 2022 %8 October 01, 2022 %P arXiv:2210.10068 %K Astrophysics - Cosmology and Nongalactic Astrophysics; Astrophysics - Astrophysics of Galaxies %U https://ui.adsabs.harvard.edu/abs/2022arXiv221010068H %X Luminous red galaxies (LRGs) and blue star-forming emission-line galaxies (ELGs) are key tracers of large-scale structure used by cosmological surveys. Theoretical predictions for such data are often done via simplistic models for the galaxy-halo connection. In this work, we use the large, high-fidelity hydrodynamical simulation of the MillenniumTNG project (MTNG) to inform a new phenomenological approach for obtaining an accurate and flexible galaxy-halo model on small scales. Our aim is to study LRGs and ELGs at two distinct epochs, $z = 1$ and $z = 0$, and recover their clustering down to very small scales, $r \sim 0.1 \ {\rm Mpc}/h$, i.e. the one-halo regime, while a companion paper extends this to a two-halo model for larger distances. The occupation statistics of ELGs in MTNG inform us that: (1) the satellite occupations exhibit a slightly super-Poisson distribution, contrary to commonly made assumptions, and (2) that haloes containing at least one ELG satellite are twice as likely to host a central ELG. We propose simple recipes for modeling these effects, each of which calls for the addition of a single free parameter to simpler halo occupation models. To construct a reliable satellite population model, we explore the LRG and ELG satellite radial and velocity distributions and compare them with those of subhalos and particles in the simulation. We find that ELGs are anisotropically distributed within halos, which together with our occupation results provides strong evidence for cooperative galaxy formation (manifesting itself as one-halo galaxy conformity); i.e.~galaxies with similar properties form in close proximity to each other. Our refined galaxy-halo model represents a useful improvement of commonly used analysis tools and thus can be of help to increase the constraining power of large-scale structure surveys. %R 10.48550/arXiv.2210.10068 %= eprint: arXiv:2210.10068 %0 Electronic Article %T The Spectroscopic Data Processing Pipeline for the Dark Energy Spectroscopic Instrument %A Guy, J. %A Bailey, S. %A Kremin, A. %A Alam, Shadab %A Alexander, D. M. %A Allende Prieto, C. %A BenZvi, S. %A Bolton, A. S. %A Brooks, D. %A Chaussidon, E. %A Cooper, A. P. %A Dawson, K. %A de la Macorra, A. %A Dey, A. %A Dey, Biprateep %A Dhungana, G. %A Eisenstein, D. J. %A Font-Ribera, A. %A Forero-Romero, J. E. %A Gaztañaga, E. %A Gontcho, S. Gontcho A %A Green, D. %A Honscheid, K. %A Ishak, M. %A Kehoe, R. %A Kirkby, D. %A Kisner, T. %A Koposov, Sergey E. %A Lan, Ting-Wen %A Landriau, M. %A Le Guillou, L. %A Levi, Michael E. %A Magneville, C. %A Manser, Christopher J. %A Martini, P. %A Meisner, Aaron M. %A Miquel, R. %A Moustakas, J. %A Myers, Adam D. %A Newman, Jeffrey A. %A Nie, Jundan %A Palanque-Delabrouille, N. %A Percival, W. J. %A Poppett, C. %A Prada, F. %A Raichoor, A. %A Ravoux, C. %A Ross, A. J. %A Schlafly, E. F. %A Schlegel, D. %A Schubnell, M. %A Sharples, Ray M. %A Tarlé, Gregory %A Weaver, B. A. %A Yèche, Christophe %A Zhou, Rongpu %A Zhou, Zhimin %A Zou, H. %J arXiv e-prints %D 2022 %8 September 01, 2022 %P arXiv:2209.14482 %K Astrophysics - Instrumentation and Methods for Astrophysics; Astrophysics - Cosmology and Nongalactic Astrophysics %U https://ui.adsabs.harvard.edu/abs/2022arXiv220914482G %X We describe the spectroscopic data processing pipeline of the Dark Energy Spectroscopic Instrument (DESI), which is conducting a redshift survey of about 40 million galaxies and quasars using a purpose-built instrument on the 4-m Mayall Telescope at Kitt Peak National Observatory. The main goal of DESI is to measure with unprecedented precision the expansion history of the Universe with the Baryon Acoustic Oscillation technique and the growth rate of structure with Redshift Space Distortions. Ten spectrographs with three cameras each disperse the light from 5000 fibers onto 30 CCDs, covering the near UV to near infrared (3600 to 9800 Angstrom) with a spectral resolution ranging from 2000 to 5000. The DESI data pipeline generates wavelength- and flux-calibrated spectra of all the targets, along with spectroscopic classifications and redshift measurements. Fully processed data from each night are typically available to the DESI collaboration the following morning. We give details about the pipeline's algorithms, and provide performance results on the stability of the optics, the quality of the sky background subtraction, and the precision and accuracy of the instrumental calibration. This pipeline has been used to process the DESI Survey Validation data set, and has exceeded the project's requirements for redshift performance, with high efficiency and a purity greater than 99 percent for all target classes. %R 10.48550/arXiv.2209.14482 %= eprint: arXiv:2209.14482 %0 Electronic Article %T Intrinsic Alignment as an RSD Contaminant in the DESI Survey %A Lamman, Claire %A Eisenstein, Daniel %A Aguilar, Jessica Nicole %A Brooks, David %A de la Macorra, Axel %A Doel, Peter %A Font-Ribera, Andreu %A Gontcho, Satya Gontcho A %A Honscheid, Klaus %A Kehoe, Robert %A Kisner, Theodore %A Kremin, Anthony %A Landriau, Martin %A Levi, Michael %A Miquel, Ramon %A Moustakas, John %A Palanque-Delabrouille, Nathalie %A Poppett, Claire %A Schubnell, Michael %A Tarlé, Gregory %J arXiv e-prints %D 2022 %8 September 01, 2022 %P arXiv:2209.03949 %K Astrophysics - Cosmology and Nongalactic Astrophysics %U https://ui.adsabs.harvard.edu/abs/2022arXiv220903949L %X We measure the tidal alignment of the major axes of Luminous Red Galaxies (LRGs) from the Legacy Imaging Survey and use it to infer the artificial redshift-space distortion signature that will arise from an orientation-dependent, surface-brightness selection in the Dark Energy Spectroscopic Instrument (DESI) survey. Using photometric redshifts to down-weight the shape-density correlations due to weak lensing, we measure the intrinsic tidal alignment of LRGs. Separately, we estimate the net polarization of LRG orientations from DESI's fiber-magnitude target selection to be of order 10^-2 along the line of sight. Using these measurements and a linear tidal model, we forecast a 0.5% fractional decrease on the quadrupole of the 2-point correlation function for projected separations of 40-80 Mpc/h. We also use a halo catalog from the Abacus Summit cosmological simulation suite to reproduce this false quadrupole. %R 10.48550/arXiv.2209.03949 %= eprint: arXiv:2209.03949 %0 Electronic Article %T First Sample of H$\alpha$+[O III] $\lambda$5007 Line Emitters at $z > 6$ through JWST/NIRCam Slitless Spectroscopy: Physical Properties and Line Luminosity Functions %A Sun, Fengwu %A Egami, Eiichi %A Pirzkal, Nor %A Rieke, Marcia %A Baum, Stefi %A Boyer, Martha %A Boyett, Kristan %A Bunker, Andrew J. %A Cameron, Alex J. %A Curti, Mirko %A Eisenstein, Daniel J. %A Gennaro, Mario %A Greene, Thomas P. %A Jaffe, Daniel %A Kelly, Doug %A Koekemoer, Anton M. %A Kumari, Nimisha %A Maiolino, Roberto %A Maseda, Michael %A Perna, Michele %A Rest, Armin %A Robertson, Brant E. %A Schlawin, Everett %A Smit, Renske %A Stansberry, John %A Sunnquist, Ben %A Tacchella, Sandro %A Williams, Christina C. %A Willmer, Christopher N. A. %J arXiv e-prints %D 2022 %8 September 01, 2022 %P arXiv:2209.03374 %K Astrophysics - Astrophysics of Galaxies %U https://ui.adsabs.harvard.edu/abs/2022arXiv220903374S %X We present a sample of four emission-line galaxies at $z=6.11-6.35$ that were serendipitously discovered using the commissioning data for the JWST/NIRCam wide-field slitless spectroscopy (WFSS) mode. One of them (at $z=6.11$) has been reported previously while the others are new discoveries. These sources are selected by the secure detections of both [O III] $\lambda$5007 and H$\alpha$ lines with other fainter lines tentatively detected in some cases (e.g., [O II] $\lambda$3727, [O III] $\lambda$4959 and [N II] $\lambda$6583). In the [O III]/H$\beta$ - [N II]/H$\alpha$ Baldwin-Phillips-Terlevich diagram, these galaxies occupy the same parameter space as that of $z\sim2$ star-forming galaxies, indicating that they have been enriched rapidly to sub-solar metallicities ($\sim$0.6 $Z_{\odot}$), similar to galaxies with comparable stellar masses at much lower redshifts. The detection of strong H$\alpha$ lines suggests a higher ionizing photon production efficiency within galaxies in the early Universe. We find brightening of the [O III] $\lambda$5007 line luminosity function (LF) from $z=3$ to 6, and no or weak redshift evolution of the H$\alpha$ line LF from $z=2$ to 6. Both LFs are under-predicted at $z\sim6$ by a factor of $\sim$10 in certain cosmological simulations. This further indicates a global Ly$\alpha$ photon escape fraction of 5-7% at $z\sim6$, much lower than previous estimates through the comparison of the UV-derived star-formation rate density and Ly$\alpha$ luminosity density. Our sample recovers $88^{+164}_{-57}$% of $z=6.0-6.6$ galaxies in the survey volume with stellar masses greater than $5\times10^8$ $M_{\odot}$, suggesting the ubiquity of strong H$\alpha$ and [O III] line emitters in the Epoch of Reionization, which will be further uncovered in the era of JWST. %R 10.48550/arXiv.2209.03374 %= eprint: arXiv:2209.03374 %0 Journal Article %T The DESI N-body simulation project - I. Testing the robustness of simulations for the DESI dark time survey %A Grove, Cameron %A Chuang, Chia-Hsun %A Devi, Ningombam Chandrachani %A Garrison, Lehman %A L'Huillier, Benjamin %A Feng, Yu %A Helly, John %A Hernández-Aguayo, César %A Alam, Shadab %A Zhang, Hanyu %A Yu, Yu %A Cole, Shaun %A Eisenstein, Daniel %A Norberg, Peder %A Wechsler, Risa %A Brooks, David %A Dawson, Kyle %A Landriau, Martin %A Meisner, Aaron %A Poppett, Claire %A Tarlé, Gregory %A Valenzuela, Octavio %+ AA(Durham University, Department of Physics), AB(Kavli Institute for Particle Astrophysics and Cosmology, California; University of Utah, Department of Physics and Astronomy), AC(Instituto de Astronomia, Universidad Nacional Autónoma de México, Apartado Postal 20-364, México; Department of Physics, Manipur University, Canchipur, Manipur 795003, India), AD(Center for Computational Astrophysics, Flatiron Institute, New York), AE(Sejong University, Department of Astronomy), AF(University of California, Berkeley, Space Sciences Laboratory), AG(Durham University, Department of Physics), AH(Max-Planck-Institute for Astrophysics, Garching; Max Planck Society for the Advancement of Science, Germany), AI(Royal Observatory Edinburgh), AJ(Kansas State University, Department of Physics), AK(Shanghai Jiaotong University, China), AL(Durham University, Department of Physics), AM(Harvard Smithsonian Center for Astrophysics), AN(Durham University, Department of Physics; Durham University, Department of Physics), AO(Kavli Institute for Particle Astrophysics and Cosmology, California), AP(University College London, Department of Physics and Astronomy), AQ(University of Utah, Department of Physics and Astronomy), AR(Lawrence Berkeley National Laboratory, California), AS(National Optical Astronomy Observatory, Arizona), AT(Space Sciences Laboratory (SSL), University of California Berkeley, CA 94720, USA), AU(University of Michigan, Department of Physics), AV(Instituto de Astronomia, Universidad Nacional Autónoma de México, Apartado Postal 20-364, México) %J Monthly Notices of the Royal Astronomical Society %V 515 %D 2022 %8 September 01, 2022 %P 1854-1870 %K methods: numerical; galaxies: haloes; large-scale structure of Universe; cosmology: theory; Astrophysics - Cosmology and Nongalactic Astrophysics %U https://ui.adsabs.harvard.edu/abs/2022MNRAS.515.1854G %X Analysis of large galaxy surveys requires confidence in the robustness of numerical simulation methods. The simulations are used to construct mock galaxy catalogues to validate data analysis pipelines and identify potential systematics. We compare three N-body simulation codes, ABACUS, GADGET-2, and SWIFT, to investigate the regimes in which their results agree. We run N-body simulations at three different mass resolutions, 6.25 × 108, 2.11 × 109, and 5.00 × 109 h-1 M⊙, matching phases to reduce the noise within the comparisons. We find systematic errors in the halo clustering between different codes are smaller than the Dark Energy Spectroscopic Instrument (DESI) statistical error for $s\ \ 20\ h^{-1}$ Mpc in the correlation function in redshift space. Through the resolution comparison we find that simulations run with a mass resolution of 2.1 × 109 h-1 M⊙ are sufficiently converged for systematic effects in the halo clustering to be smaller than the DESI statistical error at scales larger than $20\ h^{-1}$ Mpc. These findings show that the simulations are robust for extracting cosmological information from large scales which is the key goal of the DESI survey. Comparing matter power spectra, we find the codes agree to within 1 per cent for k ≤ 10 h Mpc-1. We also run a comparison of three initial condition generation codes and find good agreement. In addition, we include a quasi-N-body code, FastPM, since we plan use it for certain DESI analyses. The impact of the halo definition and galaxy-halo relation will be presented in a follow-up study. %R 10.1093/mnras/stac1947 %= eprint: arXiv:2112.09138 %@ 0035-8711 %0 Journal Article %T Stringent σ8 constraints from small-scale galaxy clustering using a hybrid MCMC + emulator framework %A Yuan, Sihan %A Garrison, Lehman H. %A Eisenstein, Daniel J. %A Wechsler, Risa H. %+ AA(Kavli Institute for Particle Astrophysics and Cosmology, California; Stanford Linear Accelerator Center; Harvard Smithsonian Center for Astrophysics), AB(University of Bamako, Mali), AC(Harvard Smithsonian Center for Astrophysics), AD(Kavli Institute for Particle Astrophysics and Cosmology, California; Stanford Linear Accelerator Center; Stanford University, Department of Physics) %J Monthly Notices of the Royal Astronomical Society %V 515 %D 2022 %8 September 01, 2022 %P 871-896 %K methods: numerical; methods: statistical; galaxies: haloes; cosmological parameters; large-scale structure of Universe; Astrophysics - Cosmology and Nongalactic Astrophysics %U https://ui.adsabs.harvard.edu/abs/2022MNRAS.515..871Y %X We present a novel simulation-based hybrid emulator approach that maximally derives cosmological and Halo Occupation Distribution (HOD) information from non-linear galaxy clustering, with sufficient precision for DESI Year 1 (Y1) analysis. Our hybrid approach first samples the HOD space on a fixed cosmological simulation grid to constrain the high-likelihood region of cosmology + HOD parameter space, and then constructs the emulator within this constrained region. This approach significantly reduces the parameter volume emulated over, thus achieving much smaller emulator errors with fixed number of training points. We demonstrate that this combined with state-of-the-art simulations result in tight emulator errors comparable to expected DESI Y1 LRG sample variance. We leverage the new ABACUSSUMMIT simulations and apply our hybrid approach to CMASS non-linear galaxy clustering data. We infer constraints on σ8 = 0.762 ± 0.024 and fσ8(zeff = 0.52) = 0.444 ± 0.016, the tightest among contemporary galaxy clustering studies. We also demonstrate that our fσ8 constraint is robust against secondary biases and other HOD model choices, a critical first step towards showcasing the robust cosmology information accessible in non-linear scales. We speculate that the additional statistical power of DESI Y1 should tighten the growth rate constraints by at least another 50-60 ${{\ \rm per\ cent}}$, significantly elucidating any potential tension with Planck. We also address the 'lensing is low' tension, which we find to be in the same direction as a potential tension in fσ8. We show that the combined effect of a lower fσ8 and environment-based bias accounts for approximately $50{{\ \rm per\ cent}}$ of the discrepancy. %R 10.1093/mnras/stac1830 %= eprint: arXiv:2203.11963 %@ 0035-8711 %0 Journal Article %T VizieR Online Data Catalog: SEGUE-2 updated stellar parameter pipeline (Rockosi+, 2022) %A Rockosi, C. M. %A Sun, Lee Y. %A Morrison, H. L. %A Yanny, B. %A Johnson, J. A. %A Lucatello, S. %A Sobeck, J. %A Beers, T. C. %A Allende Prieto, C. %A An, D. %A Bizyaev, D. %A Blanton, M. R. %A Casagrande, L. %A Eisenstein, D. J. %A Gould, A. %A Gunn, J. E. %A Harding, P. %A Ivans, I. I. %A Jacobson, H. R. %A Janesh, W. %A Knapp, G. R. %A Kollmeier, J. A. %A Lepine, S. %A Lopez-Corredoira, M. %A Ma, Z. %A Newberg, H. J. %A Pan, K. %A Prchlik, J. %A Sayers, C. %A Schlesinger, K. J. %A Simmerer, J. %A Weinberg, D. H. %J VizieR Online Data Catalog %D 2022 %8 August 01, 2022 %P J/ApJS/259/60 %K Spectra: optical; Abundances: [Fe/H]; Radial velocities; Clusters: globular; Surveys; Milky Way %U https://ui.adsabs.harvard.edu/abs/2022yCat..22590060R %Z table5.dat 128x77 Comparison of parameters from high-resolution and; SEGUE Stellar Parameter Pipeline (SSPP) analysis ; table6.dat 778x74 *List of cluster members; table9.dat 549x99 Comparison of SSPP and the APOGEE Stellar Parameter; and Chemical Abundances Pipeline (ASPCAP; ; Garcia Perez+ 2016, J/AJ/151/144) parameters %X The Sloan Extension for Galactic Understanding and Exploration 2 (SEGUE-2) survey used the 2.5m Sloan Foundation Telescope and the two original Sloan Digital Sky Survey fiber spectrographs, as did SEGUE-1 and the SDSS. SEGUE-2 observed for the first year of SDSS-III, from 2008 August through 2009 July. The survey obtained 128,288 spectra of 118,184 unique stars on 211 plates, 204 of which were unique and 7 repeated.

In addition to the new observations, SEGUE-2 updated the SEGUE Stellar Parameter Pipeline (SSPP) to improve the estimates of the stellar parameters [Fe/H], Teff, and logg for all stellar spectra obtained with the original SDSS spectrographs. The updates to the SSPP, the new temperature scale, and the calibration of this scale are described in Section 5 (for Table 5, see Section 5.4.1. and for Table 6, see Section 5.4.2.).

As an additional external evaluation, we compare the SSPP values to those measured by the APOGEE survey (Majewski+ 2017AJ....154...94M) using the subset of SDSS/SEGUE stars observed by both surveys as of Data Release 14 (Abolfathi+ 2018ApJS..235...42A). See Section 6.

(3 data files). %0 Electronic Article %T Overview of the DESI Milky Way Survey %A Cooper, Andrew P. %A Koposov, Sergey E. %A Allende Prieto, Carlos %A Manser, Christopher J. %A Kizhuprakkat, Namitha %A Myers, Adam D. %A Dey, Arjun %A Gaensicke, Boris T. %A Li, Ting S. %A Rockosi, Constance %A Valluri, Monica %A Najita, Joan %A Deason, Alis %A Raichoor, Anand %A Wang, Mei-Yu %A Ting, Yuan-Sen %A Kim, Bokyoung %A Carrillo, Andreia %A Wang, Wenting %A Beraldo e Silva, Leandro %A Han, Jiwon Jesse %A Ding, Jiani %A Sanchez-Conde, Miguel %A Aguilar, Jessica N. %A Ahlen, Steven %A Bailey, Stephen %A Belokurov, Vasily %A Brooks, David %A Cunha, Katia %A Dawson, Kyle %A de la Macorra, Axel %A Doel, Peter %A Eisenstein, Daniel J. %A Fagrelius, Parker %A Fanning, Kevin %A Font-Ribera, Andreu %A Forero-Romero, Jaime E. %A Gaztanaga, Enrique %A Gontcho, Satya Gontcho A %A Guy, Julien %A Honscheid, Klaus %A Kehoe, Robert %A Kisner, Theodore %A Kremin, Anthony %A Landriau, Martin %A Levi, Michael E. %A Martini, Paul %A Meisner, Aaron M. %A Miquel, Ramon %A Moustakas, John %A Nie, Jundan %A Palanque-Delabrouille, Nathalie %A Percival, Will J. %A Poppett, Claire %A Prada, Francisco %A Rehemtulla, Nabeel %A Schlafly, Edward %A Schlegel, David %A Schubnell, Michael %A Sharples, Ray M. %A Tarle, Gregory %A Wechsler, Risa H. %A Weinberg, David H. %A Zhou, Zhimin %A Zou, Hu %J arXiv e-prints %D 2022 %8 August 01, 2022 %P arXiv:2208.08514 %K Astrophysics - Astrophysics of Galaxies; Astrophysics - Cosmology and Nongalactic Astrophysics %U https://ui.adsabs.harvard.edu/abs/2022arXiv220808514C %X We describe the Milky Way Survey (MWS) that will be undertaken with the Dark Energy Spectroscopic Instrument (DESI) on the Mayall 4m Telescope at the Kitt Peak National Observatory. Over the next 5 years DESI MWS will observe approximately 7 million stars at Galactic latitudes |b|>20 deg, with an inclusive target selection scheme focused on the thick disk and stellar halo. MWS will also include several high-completeness samples of rare stellar types, including white dwarfs, low-mass stars within 100pc of the Sun, and horizontal branch stars. We summarize the potential of DESI to advance understanding of Galactic structure and stellar evolution. We introduce the final definitions of the main MWS target classes and estimate the number of stars in each class that will be observed. We describe our pipelines to derive radial velocities, atmospheric parameters and chemical abundances. We use ~500,000 spectra of unique stellar targets from the DESI Survey Validation program (SV) to demonstrate that our pipelines can measure radial velocities to approximately 1 km/s and [Fe/H] accurate to approximately 0.2 dex for typical stars in our main sample. We find stellar parameter distributions from 100 sq. deg. of SV observations with >90% completeness on our main sample are in good agreement with expectations from mock catalogues and previous surveys. %R 10.48550/arXiv.2208.08514 %= eprint: arXiv:2208.08514 %0 Electronic Article %T Target Selection and Validation of DESI Emission Line Galaxies %A Raichoor, A. %A Moustakas, J. %A Newman, Jeffrey A. %A Karim, T. %A Ahlen, S. %A Alam, Shadab %A Bailey, S. %A Brooks, D. %A Dawson, K. %A de la Macorra, A. %A de Mattia, A. %A Dey, A. %A Dey, Biprateep %A Dhungana, G. %A Eftekharzadeh, S. %A Eisenstein, D. J. %A Fanning, K. %A Font-Ribera, A. %A Garcia-Bellido, J. %A Gaztanaga, E. %A Gontcho, S. Gontcho A %A Guy, J. %A Honscheid, K. %A Ishak, M. %A Kehoe, R. %A Kisner, T. %A Kremin, A. %A Lan, Ting-Wen %A Landriau, M. %A Le Guillou, L. %A Levi, Michael E. %A Magneville, C. %A Martini, P. %A Meisner, Aaron M. %A Myers, Adam D. %A Nie, Jundan %A Palanque-Delabrouille, N. %A Percival, W. J. %A Poppett, C. %A Prada, F. %A Ross, A. J. %A Ruhlmann-Kleider, V. %A Sabiu, C. G. %A Schlafly, E. F. %A Schlegel, D. %A Tarle, Gregory %A Weaver, B. A. %A Yeche, Christophe %A Zhou, Rongpu %A Zhou, Zhimin %A Zou, H. %J arXiv e-prints %D 2022 %8 August 01, 2022 %P arXiv:2208.08513 %K Astrophysics - Cosmology and Nongalactic Astrophysics %U https://ui.adsabs.harvard.edu/abs/2022arXiv220808513R %X The Dark Energy Spectroscopic Instrument (DESI) will precisely constrain cosmic expansion and the growth of structure by collecting $\sim$40 million extra-galactic redshifts across $\sim$80\% of cosmic history and one third of the sky. The Emission Line Galaxy (ELG) sample, which will comprise about one-third of all DESI tracers, will be used to probe the Universe over the $0.6 < z < 1.6$ range, which includes the $1.110 million galaxies over 14,000 deg$^2$. In this work, we present and validate the final BGS target selection and survey design. From the Legacy Surveys, BGS will target a $r < 19.5$ magnitude-limited sample (BGS Bright); a fainter $19.5 < r < 20.175$ sample, color-selected to have high redshift efficiency (BGS Faint); and a smaller low-z quasar sample. BGS will observe these targets using exposure times, scaled to achieve uniform completeness, and visit each point on the footprint three times. We use observations from the Survey Validation programs conducted prior to the main survey along with realistic simulations to show that BGS can complete its strategy and make optimal use of `bright' time. We demonstrate that BGS targets have stellar contamination <1% and that their densities do not depend strongly on imaging properties. We also confirm that BGS Bright will achieve >80% fiber assignment efficiency. Finally, we show that BGS Bright and Faint will achieve >95% redshift success rates with no significant dependence on observing conditions. BGS meets the requirements for an extensive range of scientific applications. BGS will yield the most precise Baryon Acoustic Oscillations and Redshift-Space Distortions measurements at $z < 0.4$. It also presents opportunities to exploit new methods that require highly complete and dense galaxy samples (e.g. N-point statistics, multi-tracers). BGS further provides a powerful tool to study galaxy populations and the relations between galaxies and dark matter. %R 10.48550/arXiv.2208.08512 %= eprint: arXiv:2208.08512 %0 Electronic Article %T Target Selection and Validation of DESI Quasars %A Chaussidon, Edmond %A Yèche, Christophe %A Palanque-Delabrouille, Nathalie %A Alexander, David M. %A Yang, Jinyi %A Ahlen, Steven %A Bailey, Stephen. %A Brooks, David %A Cai, Zheng %A Chabanier, Solène %A Davis, Tamara M. %A Dawson, Kyle %A de la Macorra, Axel %A Dey, Arjun %A Dey, Biprateep %A Eftekharzadeh, Sarah %A Eisenstein, Daniel J. %A Fanning, Kevin %A Font-Ribera, Andreu %A Gaztañaga, Enrique %A Gontcho, Satya Gontcho A %A Gonzalez-Morales, Alma X. %A Guy, Julien %A Herrera-Alcantar, Hiram K. %A Honscheid, Klaus %A Ishak, Mustapha %A Jiang, Linhua %A Juneau, Stephanie %A Kehoe, Robert %A Kisner, Theodore %A Kovács, Andras %A Kremin, Anthony %A Lan, Ting-Wen %A Landriau, Martin %A Le Guillou, Laurent %A Levi, Michael E. %A Magneville, Christophe %A Martini, Paul %A Meisner, Aaron M. %A Moustakas, John %A Muñoz-Gutiérrez, Andrea %A Myers, Adam D. %A Newman, Jeffrey A. %A Nie, Jundan %A Percival, Will J. %A Poppett, Claire %A Prada, Francisco %A Raichoor, Anand %A Ravoux, Corentin %A Ross, Ashley J. %A Schlafly, Edward %A Schlegel, David %A Tan, Ting %A Tarlé, Gregory %A Zhou, Rongpu %A Zhou, Zhimin %A Zou, Hu %J arXiv e-prints %D 2022 %8 August 01, 2022 %P arXiv:2208.08511 %K Astrophysics - Cosmology and Nongalactic Astrophysics %U https://ui.adsabs.harvard.edu/abs/2022arXiv220808511C %X The Dark Energy Spectroscopic Instrument (DESI) survey will measure large-scale structures using quasars as direct tracers of dark matter in the redshift range 0.92.1. We present several methods to select candidate quasars for DESI, using input photometric imaging in three optical bands (g, r, z) from the DESI Legacy Imaging Surveys and two infrared bands (W1, W2) from the Wide-field Infrared Explorer (WISE). These methods were extensively tested during the Survey Validation of DESI. In this paper, we report on the results obtained with the different methods and present the selection we optimized for the DESI main survey. The final quasar target selection is based on a Random Forest algorithm and selects quasars in the magnitude range 16.599% purity for a nominal effective exposure time of ~1000s. With a 310 per sq. deg. target density, the main selection allows DESI to select more than 200 QSOs per sq. deg. (including 60 quasars with z>2.1), exceeding the project requirements by 20%. The redshift distribution of the selected quasars is in excellent agreement with quasar luminosity function predictions. %R 10.48550/arXiv.2208.08511 %= eprint: arXiv:2208.08511 %0 Electronic Article %T JWST NIRCam+NIRSpec: Interstellar medium and stellar populations of young galaxies with rising star formation and evolving gas reservoirs %A Tacchella, Sandro %A Johnson, Benjamin D. %A Robertson, Brant E. %A Carniani, Stefano %A D'Eugenio, Francesco %A Kumar, Nimisha %A Maiolino, Roberto %A Nelson, Erica J. %A Suess, Katherine A. %A Übler, Hannah %A Williams, Christina C. %A Adebusola, Alabi %A Alberts, Stacey %A Arribas, Santiago %A Bhatawdekar, Rachana %A Bonaventura, Nina %A Bowler, Rebecca A. A. %A Bunker, Andrew J. %A Cameron, Alex J. %A Curti, Mirko %A Egami, Eiichi %A Eisenstein, Daniel J. %A Frye, Brenda %A Hainline, Kevin %A Helton, Jakob M. %A Ji, Zhiyuan %A Looser, Tobias J. %A Lyu, Jianwei %A Perna, Michele %A Rawle, Timothy %A Rieke, George %A Rieke, Marcia %A Saxena, Aayush %A Sandles, Lester %A Shivaei, Irene %A Simmonds, Charlotte %A Sun, Fengwu %A Willmer, Christopher N. A. %A Willott, Chris J. %A Witstok, Joris %J arXiv e-prints %D 2022 %8 August 01, 2022 %P arXiv:2208.03281 %K Astrophysics - Astrophysics of Galaxies %U https://ui.adsabs.harvard.edu/abs/2022arXiv220803281T %X We present an interstellar medium and stellar population analysis of three spectroscopically confirmed $z>7$ galaxies in the ERO JWST/NIRCam and JWST/NIRSpec data of the SMACS J0723.3-7327 cluster. We use the Bayesian spectral energy distribution (SED) fitting code Prospector with a flexible star-formation history (SFH), a variable dust attenuation law, and a self-consistent model of nebular emission (continuum and emission lines). Importantly, we self-consistently fit both the emission line fluxes from JWST/NIRSpec and the broad-band photometry from JWST/NIRCam, taking into account slit-loss effects. We find that these three $z=7.6-8.5$ galaxies ($M_{\star}\approx10^{8}~M_{\odot}$) are young with rising SFHs and mass-weighted ages of 3-7 Myr, though we find indications for underlying older stellar populations. The inferred gas-phase metallicities broadly agree with the direct metallicity estimates from the auroral lines. The galaxy with the lowest gas-phase metallicity ($\mathrm{Z}_{\rm gas}=0.06~\mathrm{Z}_{\odot}$) has a steeply rising SFH, is very compact ($<0.2~\mathrm{kpc}$) and has a high star-formation rate surface density ($\Sigma_{\rm SFR}\approx38~\mathrm{M}_{\odot}~\mathrm{yr}^{-1}~\mathrm{kpc}^{-2}$), consistent with rapid gas accretion. The two other objects with higher gas-phase metallicity show more complex multi-component morphologies on kpc scales, indicating that their recent increase in star-formation rate is driven by mergers or internal, gravitational instabilities. We discuss effects of assuming different SFH priors or only fitting the photometric data. Our analysis highlights the strength and importance of combining JWST imaging and spectroscopy for fully assessing the nature of galaxies at the earliest epochs. %R 10.48550/arXiv.2208.03281 %= eprint: arXiv:2208.03281 %0 Electronic Article %T JWST reveals a population of ultra-red, flattened disk galaxies at 23) performed with ABACUS, of scale-free models with a range of spectral indices n, to better understand and quantify convergence of the matter power spectrum. Using self-similarity to identify converged regions, we show that the maximal wavenumber resolved at a given level of accuracy increases monotonically as a function of time. At 1 per cent level it starts at early times from a fraction of $k_\Lambda$, the Nyquist wavenumber of the initial grid, and reaches at most, if the force softening is sufficiently small, ${\sim}2{-}3 k_\Lambda$ at the very latest times we evolve to. At the $5{{\ \rm per\ cent}}$ level, accuracy extends up to wavenumbers of order $5k_\Lambda$ at late times. Expressed as a suitable function of the scale-factor, accuracy shows a very simple n-dependence, allowing a extrapolation to place conservative bounds on the accuracy of N-body simulations of non-scale-free models like LCDM. We note that deviations due to discretization in the converged range are not well modelled by shot noise, and subtracting it in fact degrades accuracy. Quantitatively our findings are broadly in line with the conservative assumptions about resolution adopted by recent studies using large cosmological simulations (e.g. Euclid Flagship) aiming to constrain the mildly non-linear regime. On the other hand, we remark that conclusions about small-scale clustering (e.g. concerning the validity of stable clustering) obtained using PS data at wavenumbers larger than a few $k_\Lambda$ may need revision in light of our convergence analysis. %R 10.1093/mnras/stac578 %= eprint: arXiv:2109.04397 %@ 0035-8711 %0 Journal Article %T Constructing high-fidelity halo merger trees in ABACUSSUMMIT %A Bose, Sownak %A Eisenstein, Daniel J. %A Hadzhiyska, Boryana %A Garrison, Lehman H. %A Yuan, Sihan %+ AA(Harvard Smithsonian Center for Astrophysics; Durham University, Department of Physics), AB(Harvard Smithsonian Center for Astrophysics), AC(Harvard Smithsonian Center for Astrophysics), AD(University of Bamako, Mali), AE(Harvard Smithsonian Center for Astrophysics; Kavli Institute for Particle Astrophysics and Cosmology, California) %J Monthly Notices of the Royal Astronomical Society %V 512 %D 2022 %8 May 01, 2022 %P 837-854 %K methods: numerical; cosmology: theory; large-scale structure of the Universe; Astrophysics - Cosmology and Nongalactic Astrophysics %U https://ui.adsabs.harvard.edu/abs/2022MNRAS.512..837B %X Tracking the formation and evolution of dark matter haloes is a critical aspect of any analysis of cosmological N-body simulations. In particular, the mass assembly of a halo and its progenitors, encapsulated in the form of its merger tree, serves as a fundamental input for constructing semi-analytic models of galaxy formation and, more generally, for building mock catalogues that emulate galaxy surveys. We present an algorithm for constructing halo merger trees from ABACUSSUMMIT, the largest suite of cosmological N-body simulations performed to date consisting of nearly 60 trillion particles, and which has been designed to meet the Cosmological Simulation Requirements of the Dark Energy Spectroscopic Instrument (DESI) survey. Our method tracks the cores of haloes to determine associations between objects across multiple time slices, yielding lists of halo progenitors and descendants for the several tens of billions of haloes identified across the entire suite. We present an application of these merger trees as a means to enhance the fidelity of ABACUSSUMMIT halo catalogues by flagging and 'merging' haloes deemed to exhibit non-monotonic past merger histories. We show that this cleaning technique identifies portions of the halo population that have been deblended due to choices made by the halo finder, but which could have feasibly been part of larger aggregate systems. We demonstrate that by cleaning halo catalogues in this post-processing step, we remove potentially unphysical features in the default halo catalogues, leaving behind a more robust halo population that can be used to create highly accurate mock galaxy realizations from ABACUSSUMMIT. %R 10.1093/mnras/stac555 %= eprint: arXiv:2110.11409 %@ 0035-8711 %0 Journal Article %T A Conditional Autoencoder for Galaxy Photometric Parameter Estimation %A Yin, Jun E. %A Eisenstein, Daniel J. %A Finkbeiner, Douglas P. %A Protopapas, Pavlos %+ AA(Harvard University, Department of Physics), AB(Harvard Smithsonian Center for Astrophysics), AC(Harvard University, Department of Physics; Harvard Smithsonian Center for Astrophysics), AD(Harvard University, Engineering and Applied Sciences) %J Publications of the Astronomical Society of the Pacific %V 134 %D 2022 %8 April 01, 2022 %P 044502 %K Photometry; Galaxy photometry; Galaxy classification systems; 1234; 611; 582 %U https://ui.adsabs.harvard.edu/abs/2022PASP..134d4502Y %X Astronomical photometric surveys routinely image billions of galaxies, and traditionally infer the parameters of a parametric model for each galaxy. This approach has served us well, but the computational expense of deriving a full posterior probability distribution function is a challenge for increasingly ambitious surveys. In this paper, we use deep learning methods to characterize galaxy images, training a conditional autoencoder on mock data. The autoencoder can reconstruct and denoise galaxy images via a latent space engineered to include semantically meaningful parameters, such as brightness, location, size, and shape. Our model recovers galaxy fluxes and shapes on mock data with a lower variance than the Hyper Suprime-Cam photometry pipeline, and returns reasonable answers even for inputs outside the range of its training data. When applied to data in the training range, the regression errors on all extracted parameters are nearly unbiased with a variance near the Cramr-Rao bound. %R 10.1088/1538-3873/ac5847 %@ 0004-6280 %0 Journal Article %T SEGUE-2: Old Milky Way Stars Near and Far %A Rockosi, Constance M. %A Lee, Young Sun %A Morrison, Heather L. %A Yanny, Brian %A Johnson, Jennifer A. %A Lucatello, Sara %A Sobeck, Jennifer %A Beers, Timothy C. %A Allende Prieto, Carlos %A An, Deokkeun %A Bizyaev, Dmitry %A Blanton, Michael R. %A Casagrande, Luca %A Eisenstein, Daniel J. %A Gould, Andrew %A Gunn, James E. %A Harding, Paul %A Ivans, Inese I. %A Jacobson, H. R. %A Janesh, William %A Knapp, Gillian R. %A Kollmeier, Juna A. %A Lépine, Sébastien %A López-Corredoira, Martín %A Ma, Zhibo %A Newberg, Heidi J. %A Pan, Kaike %A Prchlik, Jakub %A Sayers, Conor %A Schlesinger, Katharine J. %A Simmerer, Jennifer %A Weinberg, David H. %+ AA(University of California Observatories and Department of Astronomy and Astrophysics, University of California Santa Cruz, Santa Cruz, CA 95064, USA), AB(Chungnam National University, Department of Astronomy and Space Science), AC(Case Western Reserve University, Department of Astronomy), AD(Fermi National Accelerator Laboratory, Illinois), AE(The Ohio State University, Department of Astronomy; The Ohio State University, Department of Astronomy), AF(Astronomical Observatory of Padua), AG(University of Washington, Department of Astronomy), AH(Department of Physics and JINA Center for the Evolution of the Elements, University of Notre Dame, South Bend, IN 46556, USA), AI(Astrophysical Institute of the Canaries; University of La Laguna, Department of Astrophysics), AJ(Ewha Womans University, Korea), AK(New Mexico State University, Department of Astronomy; MSU, Sternberg Astronomical Institute), AL(New York University, Department of Physics/CCPP), AM(ANU, Research School of Astronomy and Astrophysics), AN(Harvard Smithsonian Center for Astrophysics), AO(The Ohio State University, Department of Astronomy), AP(Princeton University, Department of Astrophysical Sciences), AQ(Case Western Reserve University, Department of Astronomy), AR(University of Utah, Department of Physics and Astronomy), AS(MIT, Center for Space Research/Kavli Institute), AT(Case Western Reserve University, Department of Astronomy), AU(Princeton University, Department of Astrophysical Sciences), AV(Carnegie Institution of Washington, Observatories, California), AW(Georgia State University, Department of Physics and Astronomy), AX(Astrophysical Institute of the Canaries; University of La Laguna, Department of Astrophysics), AY(TBanic Data, 39 Jianwai Street, Bldg 15-1106, Beijing, People's Republic of China), AZ(RPI, Department of Applied Physics and Astronomy), BA(New Mexico State University, Department of Astronomy), BB(Case Western Reserve University, Department of Astronomy), BC(University of Washington, Department of Astronomy), BD(ANU, Research School of Astronomy and Astrophysics), BE(University of Utah, Department of Physics and Astronomy), BF(The Ohio State University, Department of Astronomy; The Ohio State University, Department of Astronomy) %J The Astrophysical Journal Supplement Series %V 259 %D 2022 %8 April 01, 2022 %P 60 %K Milky Way Galaxy; Radial velocity; Sky surveys; Spectroscopy; Chemical abundances; 1054; 1332; 1464; 1558; 224 %U https://ui.adsabs.harvard.edu/abs/2022ApJS..259...60R %X The Sloan Extension for Galactic Understanding and Exploration 2 (SEGUE-2) obtained 128,288 low-resolution spectra (R ~ 1800) of 118,958 unique stars in the first year of the Sloan Digital Sky Survey III (2008-2009). SEGUE-2 targeted prioritized distant halo tracers (blue horizontal-branch stars, K giants, and M giants) and metal-poor or kinematically hot populations. The main goal of SEGUE-2 was to target stars in the distant halo and measure their kinematics and chemical abundances to learn about the formation and evolution of the Milky Way. We present the SEGUE-2 field placement and target selection strategies. We discuss the success rate of the targeting based on the SEGUE-2 spectra and other spectroscopic and astrometric surveys. We describe the final SEGUE-2/SDSS-III improvements to the stellar parameter determinations based on the SEGUE Stellar Parameter Pipeline. We report a (g - i) color-effective temperature relation calibrated to the IRFM. We evaluate the accuracy and uncertainties associated with these stellar parameters by comparing with fundamental parameters, a sample of high-resolution spectra of SEGUE stars analyzed homogeneously, stars in well-studied clusters, and stars observed in common by the APOGEE survey. The final SEGUE spectra, calibration data, and derived parameters described here were released in SDSS-III Data Release 9 and continue to be included in all subsequent SDSS Data Releases. Because of its faint limiting magnitude and emphasis on the distant halo, the public SEGUE-2 data remain an important resource for the spectroscopy of stars in the Milky Way. %R 10.3847/1538-4365/ac5323 %@ 0067-0049 %0 Journal Article %T The Seventeenth Data Release of the Sloan Digital Sky Surveys: Complete Release of MaNGA, MaStar, and APOGEE-2 Data %A Abdurro'uf %A Accetta, Katherine %A Aerts, Conny %A Silva Aguirre, Víctor %A Ahumada, Romina %A Ajgaonkar, Nikhil %A Filiz Ak, N. %A Alam, Shadab %A Allende Prieto, Carlos %A Almeida, Andrés %A Anders, Friedrich %A Anderson, Scott F. %A Andrews, Brett H. %A Anguiano, Borja %A Aquino-Ortíz, Erik %A Aragón-Salamanca, Alfonso %A Argudo-Fernández, Maria %A Ata, Metin %A Aubert, Marie %A Avila-Reese, Vladimir %A Badenes, Carles %A Barbá, Rodolfo H. %A Barger, Kat %A Barrera-Ballesteros, Jorge K. %A Beaton, Rachael L. %A Beers, Timothy C. %A Belfiore, Francesco %A Bender, Chad F. %A Bernardi, Mariangela %A Bershady, Matthew A. %A Beutler, Florian %A Bidin, Christian Moni %A Bird, Jonathan C. %A Bizyaev, Dmitry %A Blanc, Guillermo A. %A Blanton, Michael R. %A Boardman, Nicholas Fraser %A Bolton, Adam S. %A Boquien, Médéric %A Borissova, Jura %A Bovy, Jo %A Brandt, W. N. %A Brown, Jordan %A Brownstein, Joel R. %A Brusa, Marcella %A Buchner, Johannes %A Bundy, Kevin %A Burchett, Joseph N. %A Bureau, Martin %A Burgasser, Adam %A Cabang, Tuesday K. %A Campbell, Stephanie %A Cappellari, Michele %A Carlberg, Joleen K. %A Wanderley, Fábio Carneiro %A Carrera, Ricardo %A Cash, Jennifer %A Chen, Yan-Ping %A Chen, Wei-Huai %A Cherinka, Brian %A Chiappini, Cristina %A Choi, Peter Doohyun %A Chojnowski, S. Drew %A Chung, Haeun %A Clerc, Nicolas %A Cohen, Roger E. %A Comerford, Julia M. %A Comparat, Johan %A da Costa, Luiz %A Covey, Kevin %A Crane, Jeffrey D. %A Cruz-Gonzalez, Irene %A Culhane, Connor %A Cunha, Katia %A Dai, Y. Sophia %A Damke, Guillermo %A Darling, Jeremy %A Davidson, James W., Jr. %A Davies, Roger %A Dawson, Kyle %A De Lee, Nathan %A Diamond-Stanic, Aleksandar M. %A Cano-Díaz, Mariana %A Sánchez, Helena Domínguez %A Donor, John %A Duckworth, Chris %A Dwelly, Tom %A Eisenstein, Daniel J. %A Elsworth, Yvonne P. %A Emsellem, Eric %A Eracleous, Mike %A Escoffier, Stephanie %A Fan, Xiaohui %A Farr, Emily %A Feng, Shuai %A Fernández-Trincado, José G. %A Feuillet, Diane %A Filipp, Andreas %A Fillingham, Sean P. %A Frinchaboy, Peter M. %A Fromenteau, Sebastien %A Galbany, Lluís %A García, Rafael A. %A García-Hernández, D. A. %A Ge, Junqiang %A Geisler, Doug %A Gelfand, Joseph %A Géron, Tobias %A Gibson, Benjamin J. %A Goddy, Julian %A Godoy-Rivera, Diego %A Grabowski, Kathleen %A Green, Paul J. %A Greener, Michael %A Grier, Catherine J. %A Griffith, Emily %A Guo, Hong %A Guy, Julien %A Hadjara, Massinissa %A Harding, Paul %A Hasselquist, Sten %A Hayes, Christian R. %A Hearty, Fred %A Hernández, Jesús %A Hill, Lewis %A Hogg, David W. %A Holtzman, Jon A. %A Horta, Danny %A Hsieh, Bau-Ching %A Hsu, Chin-Hao %A Hsu, Yun-Hsin %A Huber, Daniel %A Huertas-Company, Marc %A Hutchinson, Brian %A Hwang, Ho Seong %A Ibarra-Medel, Héctor J. %A Chitham, Jacob Ider %A Ilha, Gabriele S. %A Imig, Julie %A Jaekle, Will %A Jayasinghe, Tharindu %A Ji, Xihan %A Johnson, Jennifer A. %A Jones, Amy %A Jönsson, Henrik %A Katkov, Ivan %A Khalatyan, Arman, Dr. %A Kinemuchi, Karen %A Kisku, Shobhit %A Knapen, Johan H. %A Kneib, Jean-Paul %A Kollmeier, Juna A. %A Kong, Miranda %A Kounkel, Marina %A Kreckel, Kathryn %A Krishnarao, Dhanesh %A Lacerna, Ivan %A Lane, Richard R. %A Langgin, Rachel %A Lavender, Ramon %A Law, David R. %A Lazarz, Daniel %A Leung, Henry W. %A Leung, Ho-Hin %A Lewis, Hannah M. %A Li, Cheng %A Li, Ran %A Lian, Jianhui %A Liang, Fu-Heng %A Lin, Lihwai %A Lin, Yen-Ting %A Lin, Sicheng %A Lintott, Chris %A Long, Dan %A Longa-Peña, Penélope %A López-Cobá, Carlos %A Lu, Shengdong %A Lundgren, Britt F. %A Luo, Yuanze %A Mackereth, J. Ted %A de la Macorra, Axel %A Mahadevan, Suvrath %A Majewski, Steven R. %A Manchado, Arturo %A Mandeville, Travis %A Maraston, Claudia %A Margalef-Bentabol, Berta %A Masseron, Thomas %A Masters, Karen L. %A Mathur, Savita %A McDermid, Richard M. %A Mckay, Myles %A Merloni, Andrea %A Merrifield, Michael %A Meszaros, Szabolcs %A Miglio, Andrea %A Di Mille, Francesco %A Minniti, Dante %A Minsley, Rebecca %A Monachesi, Antonela %A Moon, Jeongin %A Mosser, Benoit %A Mulchaey, John %A Muna, Demitri %A Muñoz, Ricardo R. %A Myers, Adam D. %A Myers, Natalie %A Nadathur, Seshadri %A Nair, Preethi %A Nandra, Kirpal %A Neumann, Justus %A Newman, Jeffrey A. %A Nidever, David L. %A Nikakhtar, Farnik %A Nitschelm, Christian %A O'Connell, Julia E. %A Garma-Oehmichen, Luis %A Luan Souza de Oliveira, Gabriel %A Olney, Richard %A Oravetz, Daniel %A Ortigoza-Urdaneta, Mario %A Osorio, Yeisson %A Otter, Justin %A Pace, Zachary J. %A Padilla, Nelson %A Pan, Kaike %A Pan, Hsi-An %A Parikh, Taniya %A Parker, James %A Peirani, Sebastien %A Peña Ramírez, Karla %A Penny, Samantha %A Percival, Will J. %A Perez-Fournon, Ismael %A Pinsonneault, Marc %A Poidevin, Frédérick %A Poovelil, Vijith Jacob %A Price-Whelan, Adrian M. %A Bárbara de Andrade Queiroz, Anna %A Raddick, M. Jordan %A Ray, Amy %A Rembold, Sandro Barboza %A Riddle, Nicole %A Riffel, Rogemar A. %A Riffel, Rogério %A Rix, Hans-Walter %A Robin, Annie C. %A Rodríguez-Puebla, Aldo %A Roman-Lopes, Alexandre %A Román-Zúñiga, Carlos %A Rose, Benjamin %A Ross, Ashley J. %A Rossi, Graziano %A Rubin, Kate H. R. %A Salvato, Mara %A Sánchez, Sebástian F. %A Sánchez-Gallego, José R. %A Sanderson, Robyn %A Santana Rojas, Felipe Antonio %A Sarceno, Edgar %A Sarmiento, Regina %A Sayres, Conor %A Sazonova, Elizaveta %A Schaefer, Adam L. %A Schiavon, Ricardo %A Schlegel, David J. %A Schneider, Donald P. %A Schultheis, Mathias %A Schwope, Axel %A Serenelli, Aldo %A Serna, Javier %A Shao, Zhengyi %A Shapiro, Griffin %A Sharma, Anubhav %A Shen, Yue %A Shetrone, Matthew %A Shu, Yiping %A Simon, Joshua D. %A Skrutskie, M. F. %A Smethurst, Rebecca %A Smith, Verne %A Sobeck, Jennifer %A Spoo, Taylor %A Sprague, Dani %A Stark, David V. %A Stassun, Keivan G. %A Steinmetz, Matthias %A Stello, Dennis %A Stone-Martinez, Alexander %A Storchi-Bergmann, Thaisa %A Stringfellow, Guy S. %A Stutz, Amelia %A Su, Yung-Chau %A Taghizadeh-Popp, Manuchehr %A Talbot, Michael S. %A Tayar, Jamie %A Telles, Eduardo %A Teske, Johanna %A Thakar, Ani %A Theissen, Christopher %A Tkachenko, Andrew %A Thomas, Daniel %A Tojeiro, Rita %A Hernandez Toledo, Hector %A Troup, Nicholas W. %A Trump, Jonathan R. %A Trussler, James %A Turner, Jacqueline %A Tuttle, Sarah %A Unda-Sanzana, Eduardo %A Vázquez-Mata, José Antonio %A Valentini, Marica %A Valenzuela, Octavio %A Vargas-González, Jaime %A Vargas-Magaña, Mariana %A Alfaro, Pablo Vera %A Villanova, Sandro %A Vincenzo, Fiorenzo %A Wake, David %A Warfield, Jack T. %A Washington, Jessica Diane %A Weaver, Benjamin Alan %A Weijmans, Anne-Marie %A Weinberg, David H. %A Weiss, Achim %A Westfall, Kyle B. %A Wild, Vivienne %A Wilde, Matthew C. %A Wilson, John C. %A Wilson, Robert F. %A Wilson, Mikayla %A Wolf, Julien %A Wood-Vasey, W. 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-), HQ(Bates College, Maine), HR(University of La Serena, Chile; University of La Serena, Chile), HS(Sejong University, Department of Astronomy), HT(Observatoire de Paris, Laboratoire d'Etudes Spatiales et d'Instrumentation en Astrophysique), HU(Carnegie Institution of Washington, Observatories, California), HV(The Ohio State University, Department of Astronomy), HW(University of Chile, Department of Astronomy), HX(University of Wyoming, Department of Physics and Astronomy), HY(Texas Christian University), HZ(University College London, Department of Physics and Astronomy), IA(University of Alabama, Department of Physics and Astronomy), IB(Max-Planck-Institute for Extraterrestrial Physics, Garching), IC(University of Portsmouth, Institute of Cosmology and Gravitation), ID(University of Pittsburgh, Department of Physics and Astronomy), IE(Montana State University, Bozeman), IF(University of Pennsylvania, Department of Astronomy), IG(University of Antofagasta, Chile), IH(Texas Christian University; 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Bourgogne Franche-Comt é, F-25000 Besançon, France), JN(UNAM, Institute of Astronomy), JO(University of La Serena, Chile), JP(UNAM, Institute of Astronomy), JQ(Joint Institute for Nuclear Astrophysics), JR(The Ohio State University, Department of Physics), JS(Sejong University, Department of Astronomy), JT(University of California, San Diego, Center for Astrophysics and Space Science; San Diego State University, California), JU(Max-Planck-Institute for Extraterrestrial Physics, Garching), JV(UNAM, Institute of Astronomy), JW(University of Washington, Department of Astronomy), JX(University of Pennsylvania, Department of Astronomy; University of Bamako, Mali), JY(University of Chile, Department of Astronomy), JZ(Bates College, Maine), KA(Astrophysical Institute of the Canaries; University of La Laguna, Department of Astrophysics), KB(University of Washington, Department of Astronomy), KC(Johns Hopkins University, Department of Physics and Astronomy), KD(Max-Planck-Institute for Astrophysics, Garching), KE(Liverpool John Moores University, Astrophysics Research Institute), KF(Lawrence Berkeley National Laboratory, California), KG(Pennsylvania State University, Department of Astronomy; 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University of New South Wales, School of Physics), LC(New Mexico State University, Department of Astronomy), LD(National Laboratory of Astrophysics, Brazil; -), LE(University of Colorado, Boulder, Department of Astrophysical and Planetary Sciences), LF(University of Concepcion, Department of Astronomy), LG(Institute of Astronomy and Astrophysics Academia Sinica; National Taiwan University, Department of Physics), LH(Johns Hopkins University, Department of Physics and Astronomy), LI(University of Utah, Department of Physics and Astronomy), LJ(University of Hawaii, Manoa, Institute for Astronomy), LK(National Astronomy Observatory, Brazil), LL(Carnegie Institution of Washington), LM(Johns Hopkins University, Department of Physics and Astronomy), LN(University of California, San Diego, Center for Astrophysics and Space Science), LO(Katholieke University of Leuven, Astronomical Institute), LP(University of Portsmouth, Institute of Cosmology and Gravitation), LQ(Saint Andrews University, School of Physics and Astronomy), LR(UNAM, Institute of Astronomy), LS(Department of Physics, Salisbury University, 1101 Camden Avenue, Salisbury, MD 21804, USA), LT(University of Connecticut), LU(University of Cambridge, Institute of Astronomy; Kavli Institute for Cosmology, UK), LV(Haverford College, Pennsylvania), LW(University of Washington, Department of Astronomy), LX(University of Antofagasta, Chile), LY(UNAM, Institute of Astronomy; -), LZ(Leibniz Institute for Astrophysics, Potsdam), MA(UNAM, Institute of Astronomy), MB(University of Hertfordshire, School of Physics, Astronomy and Mathematics), MC(Instituto de Física Universidad Nacional Autónoma de México, Cd. de México 04510, México), MD(University of La Serena, Chile), ME(University of Concepcion, Department of Astronomy), MF(The Ohio State University, Department of Astronomy), MG(Department of Physics and Astronomy, University of North Carolina Asheville, One University Heights, Asheville, NC 28804, USA), MH(University of Virginia, Department of Astronomy), MI(Wellesley College, Massachusetts), MJ(National Optical Astronomy Observatory, Arizona), MK(Saint Andrews University, School of Physics and Astronomy), ML(The Ohio State University, Department of Astronomy), MM(Max-Planck-Institute for Astrophysics, Garching), MN(University of California, Santa Cruz, Department of Astronomy and Astrophysics), MO(Saint Andrews University, School of Physics and Astronomy), MP(University of Washington, Department of Astronomy), MQ(University of Virginia, Department of Astronomy), MR(University of Virginia, Department of Astronomy), MS(Texas Christian University), MT(Max-Planck-Institute for Extraterrestrial Physics, Garching; Max Planck Society for the Advancement of Science, Germany), MU(University of Pittsburgh, Department of Physics and Astronomy), MV(University of Kentucky, Department of Astronomy; Chinese University of Hong Kong), MW(Astrophysical Institute of the Canaries), MX(University of Utah, Department of Physics and Astronomy), MY(Lawrence Berkeley National Laboratory, California), MZ(Ecole Polytechnique Federale de Lausanne, Laboratoire d'Astrophysique), NA(University of Utah, Department of Physics and Astronomy), NB(CAS, National Astronomical Observatories), NC(CAS, National Astronomical Observatories) %J The Astrophysical Journal Supplement Series %V 259 %D 2022 %8 April 01, 2022 %P 35 %K Astronomy data acquisition; Astronomy databases; Surveys; 1860; 83; 1671; Astrophysics - Astrophysics of Galaxies; Astrophysics - Instrumentation and Methods for Astrophysics %U https://ui.adsabs.harvard.edu/abs/2022ApJS..259...35A %X This paper documents the seventeenth data release (DR17) from the Sloan Digital Sky Surveys; the fifth and final release from the fourth phase (SDSS-IV). DR17 contains the complete release of the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, which reached its goal of surveying over 10,000 nearby galaxies. The complete release of the MaNGA Stellar Library accompanies this data, providing observations of almost 30,000 stars through the MaNGA instrument during bright time. DR17 also contains the complete release of the Apache Point Observatory Galactic Evolution Experiment 2 survey that publicly releases infrared spectra of over 650,000 stars. The main sample from the Extended Baryon Oscillation Spectroscopic Survey (eBOSS), as well as the subsurvey Time Domain Spectroscopic Survey data were fully released in DR16. New single-fiber optical spectroscopy released in DR17 is from the SPectroscipic IDentification of ERosita Survey subsurvey and the eBOSS-RM program. Along with the primary data sets, DR17 includes 25 new or updated value-added catalogs. This paper concludes the release of SDSS-IV survey data. SDSS continues into its fifth phase with observations already underway for the Milky Way Mapper, Local Volume Mapper, and Black Hole Mapper surveys. %R 10.3847/1538-4365/ac4414 %= eprint: arXiv:2112.02026 %@ 0067-0049 %0 Conference Paper %T Cosmological Constraints from Cross-Correlation of Planck CMB lensing and DESI-like Emission-Line Galaxies in Legacy Surveys %A Karim, Tanveer %A Singh, Sukhdeep %A Rezaie, Mehdi %A Hadzhiyska, Boryana %A Eisenstein, Daniel %J APS April Meeting Abstracts %V 2022 %D 2022 %8 April 01, 2022 %P H13.006 %U https://ui.adsabs.harvard.edu/abs/2022APS..APRH13006K %X Understanding the cause of the cosmic acceleration is one of the outstanding questions in physics. While dark energy is the leading explanation, its exact nature is unknown, and theories such as modified gravity also offer alternative explanations. A complementary probe to studying dark energy and modified gravity theories is via measuring the growth of structure. The high redshift emission-line galaxies (ELGs) in particular trace the growth of structure in an era when the dark energy density was not significant and consequently can serve as an ideal testing ground for modified gravity theories. In this project, we select DESI-like ELGs from the Legacy Surveys DR9 imaging data and cross-correlate their positions with the Planck 2018 CMB lensing map to measure the growth of structure, linear bias, and matter density in tomographic bins 0.6 < z < 1.1 and 1.1 < z < 1.6. We conduct a full 3 X 2 analysis (Cgg, Cκκ, Cκg) and account for imaging systematics in the final cosmological inferences. We further calibrate the ELG photometric redshifts using the DESI Survey Validation dataset. In the talk, I will specifically discuss our final measurements and how the same framework can be used by the upcoming galaxy and CMB lensing surveys to provide robust cosmological constraints. %0 Journal Article %T ABACUSHOD: a highly efficient extended multitracer HOD framework and its application to BOSS and eBOSS data %A Yuan, Sihan %A Garrison, Lehman H. %A Hadzhiyska, Boryana %A Bose, Sownak %A Eisenstein, Daniel J. %+ AA(Harvard Smithsonian Center for Astrophysics; Kavli Institute for Particle Astrophysics and Cosmology, California), AB(University of Bamako, Mali), AC(Harvard Smithsonian Center for Astrophysics), AD(Harvard Smithsonian Center for Astrophysics; Durham University, Department of Physics), AE(Harvard Smithsonian Center for Astrophysics) %J Monthly Notices of the Royal Astronomical Society %V 510 %D 2022 %8 March 01, 2022 %P 3301-3320 %K gravitational lensing: weak; methods: numerical; methods: statistical; galaxies: haloes; dark matter; large-scale structure of Universe; Astrophysics - Cosmology and Nongalactic Astrophysics %U https://ui.adsabs.harvard.edu/abs/2022MNRAS.510.3301Y %X We introduce the ABACUSHOD model and present two applications of ABACUSHOD and the ABACUSSUMMIT simulations to observations. ABACUSHOD is a Halo Occupation Distribution (HOD) framework written in PYTHON that is particle-based, multitracer, highly generalized, and highly efficient. It is designed specifically with multitracer/cosmology analyses for next-generation large-scale structure surveys in mind, and takes advantage of the volume and precision offered by the new state-of-the-art ABACUSSUMMIT cosmological simulations. The model is also highly customizable and should be broadly applicable to any upcoming surveys and a diverse range of cosmological analyses. In this paper, we demonstrate the capabilities of the ABACUSHOD framework through two example applications. The first example demonstrates the high efficiency and the large HOD extension feature set through an analysis of full-shape redshift-space clustering of BOSS galaxies at intermediate to small scales ($ 30\, h^{-1}$ Mpc), assessing the necessity of introducing secondary galaxy biases (assembly bias). We find strong evidence for using halo environment instead of concentration to trace secondary galaxy bias, a result which also leads to a moderate reduction in the 'lensing is low' tension. The second example demonstrates the multitracer capabilities of the ABACUSHOD package through an analysis of the extended Baryon Oscillation Spectroscopic Survey cross-correlation measurements between three different galaxy tracers: luminous red galaxies, emission-line galaxies, and quasi-stellar objects. We expect the ABACUSHOD framework, in combination with the ABACUSSUMMIT simulation suite, to play an important role in a simulation-based analysis of the upcoming Dark Energy Spectroscopic Instrument data sets. %R 10.1093/mnras/stab3355 %= eprint: arXiv:2110.11412 %@ 0035-8711 %0 Journal Article %T A fully Lagrangian, non-parametric bias model for dark matter halos %A Wu, Xiaohan %A Muñoz, Julian B. %A Eisenstein, Daniel %+ AA(Harvard Smithsonian Center for Astrophysics), AB(Harvard Smithsonian Center for Astrophysics), AC(Harvard Smithsonian Center for Astrophysics) %J Journal of Cosmology and Astroparticle Physics %V 2022 %D 2022 %8 February 01, 2022 %P 002 %K cosmic web; cosmological simulations; power spectrum; Astrophysics - Cosmology and Nongalactic Astrophysics %U https://ui.adsabs.harvard.edu/abs/2022JCAP...02..002W %X We present a non-parametric Lagrangian biasing model and fit the ratio of the halo and mass densities at the field level using the mass-weighted halo field in the ABACUSSUMMIT simulations at z=0.5. Unlike the perturbative halo bias model that has been widely used in interpreting the observed large-scale structure traced by galaxies, we find a non-negative halo-to-mass ratio that increases monotonically with the linear overdensity δ1 in the initial Lagrangian space. The bias expansion, however, does not guarantee non-negativity of the halo counts, and may lead to rising halo number counts at negative overdensities. The shape of the halo-to-mass ratio is unlikely to be described by a polynomial function of δ1 and other quantities. Especially for massive halos with 6×1012 h-1 M⊙, the halo-to-mass ratio starts soaring up at δ1>0, substantially different from the predictions of the bias expansion. We show that for the halo masses we consider (M>3×1011 h-1 M⊙) a non-parametric halo-to-mass ratio as a function of δ1 and its local derivative ∇^2δ1 can recover the halo power spectra to sub-percent level accuracy for wavenumbers k=0.01-0.1 h Mpc-1 given a proper smoothing scale to filter the initial density field, even though we do not fit the power spectrum directly. However, there is mild dependence of the recovery of the halo power spectrum on the smoothing scale and other input parameters. At k<0.01 h Mpc-1 and for massive halos with M>6×1012 h-1 M⊙, our non-parametric model leads to a few percent overestimation of the halo power spectrum, indicating the need for larger or multiple smoothing scales. The halo-to-mass ratios obtained qualitatively agree with intuitions from extended Press-Schechter theory. We compare our framework to the bias expansion and discuss possible extensions. %R 10.1088/1475-7516/2022/02/002 %= eprint: arXiv:2109.13948 %@ 1475-7516 %0 Journal Article %T ENCORE: an O (Ng2) estimator for galaxy N-point correlation functions %A Philcox, Oliver H. E. %A Slepian, Zachary %A Hou, Jiamin %A Warner, Craig %A Cahn, Robert N. %A Eisenstein, Daniel J. %+ AA(Princeton University, Department of Astrophysical Sciences; Princeton Institute for Advanced Study, New Jersey), AB(University of Florida, Department of Astronomy; Lawrence Berkeley National Laboratory, Physics Division), AC(University of Florida, Department of Astronomy), AD(University of Florida, Department of Astronomy), AE(Lawrence Berkeley National Laboratory, Physics Division), AF(Harvard Smithsonian Center for Astrophysics) %J Monthly Notices of the Royal Astronomical Society %V 509 %D 2022 %8 January 01, 2022 %P 2457-2481 %K methods: numerical; methods: statistical; galaxies: statistics; large-scale structure of Universe; cosmology: theory; Astrophysics - Instrumentation and Methods for Astrophysics; Astrophysics - Cosmology and Nongalactic Astrophysics; General Relativity and Quantum Cosmology; Physics - Computational Physics %U https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.2457P %X We present a new algorithm for efficiently computing the N-point correlation functions (NPCFs) of a 3D density field for arbitrary N. This can be applied both to a discrete spectroscopic galaxy survey and a continuous field. By expanding the statistics in a separable basis of isotropic functions built from spherical harmonics, the NPCFs can be estimated by counting pairs of particles in space, leading to an algorithm with complexity $\mathcal {O}(N_\mathrm{g}^2)$ for Ng particles, or $\mathcal {O}(N_\mathrm{FFT}\log N_\mathrm{FFT})$ when using a Fast Fourier Transform with NFFT grid-points. In practice, the rate-limiting step for N > 3 will often be the summation of the histogrammed spherical harmonic coefficients, particularly if the number of radial and angular bins is large. In this case, the algorithm scales linearly with Ng. The approach is implemented in the ENCORE code, which can compute the 3PCF, 4PCF, 5PCF, and 6PCF of a BOSS-like galaxy survey in ${\sim}100$ CPU-hours, including the corrections necessary for non-uniform survey geometries. We discuss the implementation in depth, along with its GPU acceleration, and provide practical demonstration on realistic galaxy catalogues. Our approach can be straightforwardly applied to current and future data sets to unlock the potential of constraining cosmology from the higher point functions. %R 10.1093/mnras/stab3025 %= eprint: arXiv:2105.08722 %@ 0035-8711 %0 Journal Article %T Self-similarity of k-nearest neighbour distributions in scale-free simulations %A Garrison, Lehman H. %A Abel, Tom %A Eisenstein, Daniel J. %+ AA(Center for Computational Astrophysics, Flatiron Institute, New York), AB(Kavli Institute for Particle Astrophysics and Cosmology, California; Stanford University, Department of Physics; Stanford Linear Accelerator Center), AC(Harvard Smithsonian Center for Astrophysics) %J Monthly Notices of the Royal Astronomical Society %V 509 %D 2022 %8 January 01, 2022 %P 2281-2288 %K methods: numerical; cosmology: theory; Astrophysics - Cosmology and Nongalactic Astrophysics %U https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.2281G %X We use the k-nearest neighbour probability distribution function (kNN-PDF; Banerjee & Abel 2021a) to assess convergence in a scale-free N-body simulation. Compared to our previous two-point analysis, the kNN-PDF allows us to quantify our results in the language of haloes and numbers of particles, while also incorporating non-Gaussian information. We find good convergence for 32 particles and greater at densities typical of haloes, while 16 particles and fewer appear unconverged. Halving the softening length extends convergence to higher densities, but not to fewer particles. Our analysis is less sensitive to voids, but we analyse a limited range of underdensities and find evidence for convergence at 16 particles and greater even in sparse voids. %R 10.1093/mnras/stab3160 %= eprint: arXiv:2109.06991 %@ 0035-8711 %0 Journal Article %T The halo light-cone catalogues of ABACUSSUMMIT %A Hadzhiyska, Boryana %A Garrison, Lehman H. %A Eisenstein, Daniel %A Bose, Sownak %+ AA(Harvard Smithsonian Center for Astrophysics), AB(University of Bamako, Mali), AC(Harvard Smithsonian Center for Astrophysics), AD(Harvard Smithsonian Center for Astrophysics) %J Monthly Notices of the Royal Astronomical Society %V 509 %D 2022 %8 January 01, 2022 %P 2194-2208 %K methods: data analysis; methods: N-body simulations; galaxies: formation; galaxies: haloes; cosmology: theory; large-scale structure of Universe; Astrophysics - Cosmology and Nongalactic Astrophysics %U https://ui.adsabs.harvard.edu/abs/2022MNRAS.509.2194H %X We describe a method for generating halo catalogues on the light-cone using the ABACUSSUMMIT suite of N-body simulations. The main application of these catalogues is the construction of realistic mock galaxy catalogues and weak lensing maps on the sky. Our algorithm associates the haloes from a set of coarsely spaced snapshots with their positions at the time of light-cone crossing by matching halo particles to on-the-fly light-cone particles. It then records the halo and particle information into an easily accessible product, which we call the ABACUSSUMMIT halo light-cone catalogues. Our recommended use of this product is in the halo mass regime of Mhalo > 2.1 × 1011 M⊙ h-1 for the base resolution simulations, i.e. haloes containing at least 100 particles, where the interpolated halo properties are most reliable. To test the validity of the obtained catalogues, we perform various visual inspections and consistency checks. In particular, we construct galaxy mock catalogues of emission-line galaxies (ELGs) at z ~ 1 by adopting a modified version of the ABACUSHOD script, which builds on the standard halo occupation distribution (HOD) method by including various extensions. We find that the multipoles of the autocorrelation function are consistent with the predictions from the full-box snapshot, implicitly validating our algorithm. In addition, we compute and output CMB convergence maps and find that the auto- and cross-power spectrum agrees with the theoretical prediction at the sub-per-cent level. %R 10.1093/mnras/stab3066 %= eprint: arXiv:2110.11413 %@ 0035-8711 %0 Journal Article %T COMPASO: A new halo finder for competitive assignment to spherical overdensities %A Hadzhiyska, Boryana %A Eisenstein, Daniel %A Bose, Sownak %A Garrison, Lehman H. %A Maksimova, Nina %+ AA(Harvard Smithsonian Center for Astrophysics), AB(Harvard Smithsonian Center for Astrophysics), AC(Harvard Smithsonian Center for Astrophysics), AD(University of Bamako, Mali), AE(Harvard Smithsonian Center for Astrophysics) %J Monthly Notices of the Royal Astronomical Society %V 509 %D 2022 %8 January 01, 2022 %P 501-521 %K methods: data analysis; galaxies: haloes; cosmology: theory; large- scale structure of Universe; Astrophysics - Cosmology and Nongalactic Astrophysics; Astrophysics - Instrumentation and Methods for Astrophysics %U https://ui.adsabs.harvard.edu/abs/2022MNRAS.509..501H %X We describe a new method (COMPASO) for identifying groups of particles in cosmological N-body simulations. COMPASO builds upon existing spherical overdensity (SO) algorithms by taking into consideration the tidal radius around a smaller halo before competitively assigning halo membership to the particles. In this way, the COMPASO finder allows for more effective deblending of haloes in close proximity as well as the formation of new haloes on the outskirts of larger ones. This halo-finding algorithm is used in the ABACUSSUMMIT suite of N-body simulations, designed to meet the cosmological simulation requirements of the Dark Energy Spectroscopic Instrument (DESI) survey. COMPASO is developed as a highly efficient on-the-fly group finder, which is crucial for enabling good load-balancing between the GPU and CPU and the creation of high-resolution merger trees. In this paper, we describe the halo-finding procedure and its particular implementation in ABACUS, accompanying it with a qualitative analysis of the finder. We test the robustness of the COMPASO catalogues before and after applying the cleaning method described in an accompanying paper and demonstrate its effectiveness by comparing it with other validation techniques. We then visualize the haloes and their density profiles, finding that they are well fit by the NFW formalism. Finally, we compare other properties such as radius-mass relationships and two-point correlation functions with that of another widely used halo finder, ROCKSTAR. %R 10.1093/mnras/stab2980 %= eprint: arXiv:2110.11408 %@ 0035-8711