Self Calibrated Galaxy Groups from the DESI DR1 Bright Galaxy Survey

summary

The gist

The gist: This work presents a galaxy group catalog built from Data Release 1 of the Dark Energy Spectroscopic Instrument (DESI) Bright Galaxy Survey, using the Self Calibrated Group Finder.

In short

This work presents a galaxy group catalog from DESI DR1 using a self-calibrated group finder. The method uses observed clustering data to calibrate the finder's parameters instead of simulations. The catalog includes over 2 million galaxies, allowing researchers to study galaxy properties like satellite fractions and halo mass relations, revealing how quenching depends on halo mass.

Key concepts

Self Calibrated Group Finder (SCGF): This algorithm identifies galaxy groups by using clustering data from the observed sample to tune its parameters. Instead of relying on computer simulations, it uses real galaxy data to calibrate how well it finds groups, ensuring a more accurate catalog.
Halo Occupation Distribution (HOD): HODs describe how many galaxies of different luminosities reside within dark matter halos of a specific mass. The study found that these non-parametric HODs can be fitted by standard models, with the satellite power-law index ($\alpha$) differing between star-forming and quiescent galaxies.
Hybrid Nearest Neighbor (HNN): This new technique helps fix errors caused by missing data in the survey. HNN uses photometric redshifts and multiple nearby neighbors to assign redshifts to galaxies that were not observed directly, improving the accuracy of halo statistics.
Luminosity-to-Halo Mass Relation (LHMR): The LHMR shows how the luminosity of a galaxy relates to the mass of its host dark matter halo. The study found that abundance matching on group luminosity underestimated this scatter, suggesting a need for more complex modeling.

Terminology used across episodes

Reinforcement Learning (RL) — explained in 25 episodes
Sample Complexity — explained in 20 episodes
Retrieval-Augmented Generation (RAG) — explained in 19 episodes
Flow Matching — explained in 18 episodes
Federated Learning — explained in 17 episodes
Quantum Fisher Information (QFI) — explained in 17 episodes

This episode discusses

The paper

Self Calibrated Galaxy Groups from the DESI DR1 Bright Galaxy Survey · Read on arXiv

I. Williams, J. Tinker, Kai Wang, J. Aguilar, S. Ahlen, F. Beutler, D. Bianchi, D Brooks, A Carnero Rosell, F J Castander Gontcho A Gontcho Satya Gontcho A Gontcho Gutierrez C Hahn Ishak Juneau R Kehoe Kremin M Landriau Le Guillou Leauthaud Manera Meisner Miquel Nadathur Paillas Percival Prada Perez-Rafols Ravoux Ross Rossi Ruggeri Samushia Sanchez Saulder Schlegel Silber Siudek Tarle Vaisakh Weaver, B A Weaver

New York University · Institute for Computational Cosmology at Durham University · Lawrence Berkeley National Laboratory · Department of Physics at Boston University · Institute for Astronomy at the University of Edinburgh · Dipartimento di Fisica Aldo Pontremoli at the Universita degli Studi di Milano, INAF-Osservatorio Astronomico di Brera, Department of Physics & Astronomy at University College London, Departamento de Astrofísica at Universidad de La Laguna (ULL), Instituto de Astrofísica de Canarias, Institut d’Estudis Espacials de Catalunya (IEEC), Institute of Space Sciences at ICE-CSIC, Instituto de Física at the Universidad Nacional Autónoma de México, Departamento de Física at the University of los Andes, Observatorio Astronómico at the Universidad de los Andes, Max Planck Institute for Extraterrestrial Physics · Department of Physics and Astronomy at The Ohio State University · Sejong University · Queensland University of Technology School of Chemistry & Physics · Abastumani Astrophysical Observatory Georgia Department of Physics at Kansas State University, CIEMAT, Max Planck Institute for Extraterrestrial Physics, Department of Physics and Astronomy at Siena University, Instituto de Estudios Astrofísicos at the Universidad Diego Portales, Department of Physics and Astronomy at The Ohio State University · Department of Physics and Astronomy at Southern Methodist University · Perimeter Institute for Theoretical Physics at the Waterloo Centre for Astrophysics at the University of Waterloo

We present a galaxy group catalog built from Data Release 1 of the Dark Energy Spectroscopic Instrument (DESI) Bright Galaxy Survey, using the Self Calibrated Group Finder. The algorithm uses clustering data from the observed sample to calibrate the Group Finder's free parameters, rather than simulations. The catalog contains 2,229,741 galaxies from 2680 deg squared of sky with 3-pass coverage and r-band fluxes 19.5. The catalog classifies all galaxies in the flux-limited sample as centrals or satellites and estimates host dark matter halo masses. We show the satellite fraction f sat, luminosity halo mass relation, stellar halo mass relation and halo occupation distributions (HODs) from the catalog. We find these non-parametric HODs can be fit by standard HOD models, with satellite power-law index α values around 0.75 for star-forming galaxies and around 1.1 for quiescent objects. In agreement with recent results, we find that the quenched fraction of central galaxies depends mostly on halo mass and is nearly independent of central galaxy stellar mass for galaxies more massive than 10 10.3 M h-1 and halos more massive than 10 12.2 M h-1. In an appendix, we introduce a new Hybrid Nearest Neighbor (HNN) technique to mitigate systematics from fiber incompleteness. HNN uses photometric redshifts and multiple on-sky neighbors to assign redshifts to unobserved targets. Calibrated on DESI Survey Validation 3 data, our technique yields correct redshifts about 50% of the time and produces unbiased halo occupation statistics.

Transcript

Introduction to the show: ident: Astrophysics Radio. Generated commentary on the latest astrophysics papers.

Vera: I'm Vera.

Jocelyn: And I'm Jocelyn, here with Subrahmanyan.

Vera: We're looking at the paper: "Self Calibrated Galaxy Groups from the DESI DR1 Bright Galaxy Survey".

Jocelyn: The gist: This work presents a galaxy group catalog built from Data Release 1 of the Dark Energy Spectroscopic Instrument (DESI) Bright Galaxy Survey, using the Self Calibrated Group Finder.

Vera: First, who's behind it and why it matters.

Paper summary: Vera: So looking at this paper, "Self Calibrated Galaxy Groups from the DESI DR1 Bright Galaxy Survey," it seems like they successfully built a robust catalog using a self-calibrated group finder on DESI DR1 data.

Jocelyn: The authors are showing that their method of calibrating the finder using observed clustering data is a valid way to model these galaxy groups compared to simulations.

Subrahmanyan: This work provides constraints on the luminosity halo mass relation and stellar halo mass relation, and it also gives us some insights into how quenching happens in central galaxies depending on the dark matter halo mass.

Vera: It’s important that they show how their non-parametric HODs can be fitted by standard HOD models with specific values for the satellite power-law index alpha around zero point seventy-five and one point one.

Jocelyn: The implication is that we have a better handle on the galaxy-halo connection because they are using actual observed distributions rather than just assuming a fixed functional form for the relationship.

Subrahmanyan: This sets up future work to go beyond just halo masses, aiming to assign more properties like halo age and angular momentum, which will require augmenting the abundance matching step in the group finder.

Vera: Ultimately, this catalog is a valuable resource for understanding galaxy populations across different mass scales in the context of large-scale structure.

Conclusion: Vera: So we've looked at this paper, "Self Calibrated Galaxy Groups from the DESI DR1 Bright Galaxy Survey," which is about building a group catalog using a self-calibrated finder on Data Release one of the Dark Energy Spectroscopic Instrument Bright Galaxy Survey.

Jocelyn: The authors are showing that tuning their group finder using actual clustering data from the sample, instead of just simulations, gives them a better way to map out these galaxy groups.

Subrahmanyan: That means they can get tighter constraints on how galaxies sit within dark matter halos across a wide range of halo masses, which is crucial for structure formation models.

Vera: They found that the non-parametric halo occupation distributions they derived actually fit standard HOD models, and they found specific power-law indices for satellite populations, alpha around zero point seventy-five for star-forming galaxies and around one point one for quiescent ones.

Jocelyn: So the implication there is that we have a more reliable handle on the galaxy-halo connection because they're using observed distributions instead of just assuming a fixed shape to that relationship.

Subrahmanyan: They also pointed out how quenching happens in central galaxies depends mostly on the halo mass, especially for halos above ten ten point three solar masses per h-one and it’s nearly independent of the central galaxy's stellar mass there.

Vera: And to make sure their data was solid, they introduced a new Hybrid Nearest Neighbor technique called HNN to fix errors from missing fibers, using photometric redshifts and multiple neighbors.

Jocelyn: That HNN technique gets the redshifts right about fifty point one percent of the time, which is a good check on how reliable those halo statistics are.

Subrahmanyan: It sets up future work where they plan to look at things like halo age and angular momentum, which means they'll need to change their abundance matching step in the finder itself.

Vera: Ultimately, this catalog is a big resource for understanding how galaxies are organized in large-scale structure across many mass scales.

Jocelyn: It’s a big picture effort to bridge what we see in galaxy surveys with the dark matter structure they live inside.

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