The DESI One-Percent Survey: Evidence for Assembly Bias from Low-Redshift Counts-in-Cylinders Measurements
Alan N. Pearl, Andrew R. Zentner, Jeffrey A. Newman, Rachel Bezanson,, Kuan Wang, John Moustakas, Jessica N. Aguilar, Steven Ahlen, David Brooks,, Todd Claybaugh, Shaun Cole, Kyle Dawson, Axel de la Macorra, Peter Doel,, Jamie E. Forero-Romero, Satya Gontcho A Gontcho

TL;DR
This paper uses low-redshift DESI data and counts-in-cylinders measurements to tightly constrain galaxy-halo models, providing strong evidence for assembly bias, especially at lower luminosities, and demonstrating a fast, GPU-compatible modeling approach.
Contribution
The study introduces a novel application of counts-in-cylinders in HOD modeling with DESI data, revealing significant assembly bias evidence and improving computational efficiency with galtab.
Findings
Strong evidence for assembly bias at low luminosities.
HOD parameters are tightly constrained by CiC measurements.
Models favor positive central assembly bias at certain redshifts.
Abstract
We explore the galaxy-halo connection information that is available in low-redshift samples from the early data release of the Dark Energy Spectroscopic Instrument (DESI). We model the halo occupation distribution (HOD) from z=0.1-0.3 using Survey Validation 3 (SV3; a.k.a., the One-Percent Survey) data of the DESI Bright Galaxy Survey (BGS). In addition to more commonly used metrics, we incorporate counts-in-cylinders (CiC) measurements, which drastically tighten HOD constraints. Our analysis is aided by the Python package, galtab, which enables the rapid, precise prediction of CiC for any HOD model available in halotools. This methodology allows our Markov chains to converge with much fewer trial points, and enables even more drastic speedups due to its GPU portability. Our HOD fits constrain characteristic halo masses tightly and provide statistical evidence for assembly bias,…
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Taxonomy
TopicsIndustrial Vision Systems and Defect Detection · Manufacturing Process and Optimization
