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arxiv: 2508.05467 · v2 · submitted 2025-08-07 · 🌌 astro-ph.CO

Combined tracer analysis for DESI 2024 BAO

D. Valcin , M. Rashkovetskyi , H. Seo , F. Beutler , P. McDonald , A. de Mattia , A. J. Rosado-Mar\'in , A. J. Ross
show 59 more authors
N. Padmanabhan J. Aguilar S. Ahlen U. Andrade D. Bianchi D. Brooks E. Chaussidon S. Chen X. Chen T. Claybaugh A. Cuceu K. S. Dawson A. de la Macorra Biprateep Dey Z. Ding P. Doel S. Ferraro A. Font-Ribera J. E. Forero-Romero E. Gazta\~naga S. Gontcho A Gontcho G. Gutierrez C. Hahn K. Honscheid C. Howlett M. Ishak R. Kehoe D. Kirkby T. Kisner A. Kremin O. Lahav A. Lambert M. Landriau M. E. Levi M. Manera A. Meisner J. Mena-Fern\'andez R. Miquel J. Moustakas S. Nadathur E. Paillas N. Palanque-Delabrouille W. J. Percival F. Prada I. P\'erez-R\`afols G. Rossi R. Ruggeri L. Samushia E. Sanchez C. Saulder D. Schlegel M. Schubnell J. Silber D. Sprayberry G. Tarl\'e B. A. Weaver J. Yu R. Zhou H. Zou
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Pith reviewed 2026-05-19 00:21 UTC · model grok-4.3

classification 🌌 astro-ph.CO
keywords baryon acoustic oscillationsDESItracer combinationgalaxy biaslarge scale structureredshift surveyscosmology
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The pith

Merging LRG and ELG tracers with galaxy bias weights improves BAO constraints from DESI DR1 by 11 percent for the isotropic scale.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows how to combine luminous red galaxies and emission line galaxies in the same redshift bin to reduce shot noise in baryon acoustic oscillation measurements. By creating a unified catalog and weighting by galaxy bias, the analysis increases the effective number density of tracers. Testing on simulations confirms the method, and applying it to real DESI data yields tighter constraints on the BAO parameters. This approach was used for the most precise BAO measurement in the 0.8 to 1.1 redshift range from the first data release.

Core claim

By developing a pipeline that merges overlapping tracers using galaxy bias as an approximately optimal weight, the combination of LRGs and ELGs in the 0.8 < z < 1.1 range increases the number density and reduces shot noise, leading to an 11% improvement in α_iso and 7% in α_AP constraints when applied to DESI DR1 catalog, consistent with mock tests, and producing the most precise BAO measurement with 0.86% constraint on the distance scale.

What carries the argument

The pipeline that merges overlapping tracers using galaxy bias as an approximately optimal weight, tested on Abacus simulations.

Load-bearing premise

Galaxy bias serves as an approximately optimal weight for merging the tracers, with the assumption that the bias model and mock realism accurately capture real-data covariance and selection effects.

What would settle it

If the improvement in BAO constraints from the combined catalog on real DESI data does not match the 11% and 7% seen in the Abacus mock catalogs, the optimality of the bias weighting would be called into question.

read the original abstract

This paper demonstrates how the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1) and future baryon acoustic oscillations (BAO) analyses can optimally combine overlapping tracers (galaxies of distinct types) in the same redshift range. We make a unified catalog of Luminous Red Galaxies (LRGs) and Emission Line Galaxies (ELGs) in the redshift range 0.8 < z < 1.1 and investigate the impact on the BAO constraints. DESI DR1 contains ~30% of the final DESI LRG sample and less than 25% of the final ELG sample, and the combination of LRGs and ELGs increases the number density and reduces the shot noise. We developed a pipeline to merge the overlapping tracers using galaxy bias as an approximately optimal weight and tested the pipeline on a suite of Abacus simulations, calibrated on the final version of the DESI Early Data Release. When applying our pipeline to the DESI DR1 catalog, we find an improvement in the BAO constraints of 11% for $\alpha_\mathrm{iso}$ and ~7.0% for $\alpha_\mathrm{AP}$ consistent with our findings in mock catalogs. Our analysis was integrated into the DESI DR1 BAO analysis to produce the LRG+ELG result in the 0.8 < z < 1.1 redshift bin, which provided the most precise BAO measurement from DESI DR1 with a 0.86% constraint on the BAO distance scale and a $9.1\sigma$ detection of the isotropic BAO feature.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. This paper presents a pipeline for combining LRG and ELG tracers in the 0.8 < z < 1.1 bin of DESI DR1 using galaxy bias as approximately optimal weights to reduce shot noise and improve BAO constraints. Tested on Abacus mocks calibrated to the Early Data Release, the method yields an 11% improvement in α_iso and ~7% in α_AP when applied to DR1 data, consistent with mock results. The combined catalog was integrated into the official DESI DR1 BAO analysis, producing the most precise measurement in the bin (0.86% on the distance scale, 9.1σ isotropic BAO detection).

Significance. If the result holds, the work provides a practical multi-tracer technique that enhances BAO precision for DESI and similar surveys by increasing effective number density. The mock-to-data consistency and official integration are strengths; the approach is falsifiable via future data releases and supports reproducible analyses when code is shared.

major comments (1)
  1. Abstract and mock calibration description: The headline 11% / ~7% improvement on actual DR1 data rests on bias weights tuned and validated exclusively on Abacus mocks. The manuscript must demonstrate that these mocks reproduce DR1 number densities, linear bias values, cross-tracer covariance, and key systematics (fiber collisions, completeness) in the 0.8<z<1.1 bin to sufficient accuracy that the weights remain near-optimal; without such a direct mock-data comparison, the reported gain on real data could be misestimated.
minor comments (2)
  1. The abstract and introduction should explicitly define α_iso and α_AP at first use for readers outside the BAO subfield.
  2. Figure captions and table legends would benefit from stating the exact redshift bin and tracer combination used in each panel or row.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their positive summary, recognition of the method's significance, and recommendation for minor revision. We address the single major comment point by point below.

read point-by-point responses

  1. Referee: [—] Abstract and mock calibration description: The headline 11% / ~7% improvement on actual DR1 data rests on bias weights tuned and validated exclusively on Abacus mocks. The manuscript must demonstrate that these mocks reproduce DR1 number densities, linear bias values, cross-tracer covariance, and key systematics (fiber collisions, completeness) in the 0.8<z<1.1 bin to sufficient accuracy that the weights remain near-optimal; without such a direct mock-data comparison, the reported gain on real data could be misestimated.

    Authors: We agree that a direct mock-data comparison strengthens the claim that the bias weights remain near-optimal when applied to DR1. The Abacus mocks were calibrated to the final DESI Early Data Release (EDR), which uses the same target selection, redshift range, and observational pipeline as the 0.8<z<1.1 portion of DR1. In the revised manuscript we will add an appendix (or expanded methods section) that explicitly compares: (i) number densities for LRG and ELG samples, (ii) linear bias values inferred from the large-scale auto- and cross-power spectra, and (iii) the leading elements of the cross-tracer covariance matrix, all measured in the mocks versus the DR1 data in the same bin. We will also document that fiber-collision and completeness systematics are implemented in the mocks using the same angular mask and fiber-assignment algorithm applied to DR1. These additions will confirm that the mock-derived weights are appropriate for DR1 and that the reported 11%/7% gains are not misestimated. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper develops a bias-weighted merger pipeline for LRG+ELG tracers, calibrates and validates the weights plus expected gains on independent Abacus mock realizations tuned to the Early Data Release, then applies the fixed pipeline to the separate DESI DR1 catalog to measure the actual 11% and 7% improvements. These measured gains on real data do not reduce by construction to the mock calibration inputs; the central result is an empirical measurement on DR1 whose error bars and significance are determined from the data itself. No self-citation, self-definition, or fitted-input-renamed-as-prediction step is load-bearing for the quoted BAO constraint improvements.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The method introduces one data-driven weight per tracer type derived from bias measurements; no new particles or forces are postulated.

free parameters (1)
  • galaxy bias weights
    Bias values used to weight LRG and ELG catalogs; estimated from clustering amplitude in mocks or data.
axioms (1)
  • domain assumption Galaxy bias provides an approximately optimal linear combination weight for overlapping tracers
    Invoked when developing the merging pipeline and validated only on the Abacus simulation suite.

pith-pipeline@v0.9.0 · 6196 in / 1237 out tokens · 34262 ms · 2026-05-19T00:21:54.836800+00:00 · methodology

discussion (0)

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Reference graph

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