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Nonlinear Information from DESI Luminous Red Galaxies: An Emulator-Based Analysis of Pre- and Post-Reconstruction Power Spectra

Yuting Wang, Gong-Bo Zhao, Kazuya Koyama, Ruiyang Zhao, Takahiro Nishimichi, Zhongxu Zhai, Héctor Gil-Marín, Hanyu Zhang, Jessica Nicole Aguilar, Steven Ahlen, Florian Beutler, Davide Bianchi, David Brooks, Francisco Javier Castander, Todd Claybaugh, Andrei Cuceu, Axel de la Macorra, Arnaud de Mattia, Biprateep Dey, Peter Doel, Daniel J. Eisenstein, Simone Ferraro, Jaime E. Forero-Romero, Enrique Gaztañaga, Satya Gontcho A Gontcho, Gan Gu, Gaston Gutierrez, ChangHoon Hahn, Klaus Honscheid, Cullan Howlett, Dick Joyce, Stephanie Juneau, Robert Kehoe, David Kirkby, Theodore Kisner, Jean-Paul Kneib, Anthony Kremin, Claire Lamman, Martin Landriau, Laurent Le Guillou, Marc Manera, Aaron Meisner, Roman Miquel, Seshadri Nadathur, Jeffrey A. Newman, Enrique Paillas, Will J. Percival, Francisco Prada, Ignasi Pérez-Ràfols, Alberto J. Rosado-Marín, Ashley J. Ross, Graziano Rossi, Lado Samushia, Eusebio Sanchez, Edward F. Schlafly, David Schlegel, Michael Schubnell, Hee-Jong Seo, Joseph Harry Silber, David Sprayberry, Gregory Tarlé, Xiaoma Wang, Benjamin Alan Weaver, Shuo Yuan

Abstract

We present joint measurements of the pre- and post-reconstruction power spectra, $P_{\rm pre}$ and $P_{\rm post}$, together with their cross-power spectrum, $P_{\rm cross}$, for the Luminous Red Galaxies (LRGs) in the DESI Data Release 1 (DR1). We jointly analyse these observables with an emulator-based full-shape modeling framework, thereby, for the first time, we extract complementary nonlinear information from the galaxy density field before and after reconstruction in real survey data. Specifically, including $P_{\rm post}$ and $P_{\rm cross}$ in addition to $P_{\rm pre}$ (hereafter $P_{\rm all}$) yields an improvement of approximately $18$-$27\%$ in the $σ_8$ constraint in both $Λ$CDM and $w$CDM, depending on the redshift bin, relative to the $P_{\rm pre}$-only analysis with the cosmic microwave background distance priors (hereafter CMB). In $w$CDM, the joint CMB+$P_{\rm all}$ analysis can tighten the constraints on $w$ by approximately $5$-$15\%$ across the two LRG redshift bins, compared to the CMB+$P_{\rm pre}$ combination. Further incorporating the Type Ia supernova dataset and comparing the cosmological constraints in $w$CDM from each individual power-spectrum component with those from the full combination, we find that $P_{\rm all}$ consistently provides the tightest constraints. From the joint CMB+$P_{\rm all}$+DES-Dovekie dataset, we obtain $Ω_m = 0.314 \pm 0.0048$ and $w = -0.988 \pm 0.023$ for the \texttt{LRG1} sample, and $Ω_m = 0.318 \pm 0.0046$ and $w = -0.988 \pm 0.025$ for \texttt{LRG2}. These results demonstrate that combining pre- and post-reconstruction power spectra with their cross-correlation enables DESI to harvest additional nonlinear information, leading to tighter constraints on cosmological parameters.

Nonlinear Information from DESI Luminous Red Galaxies: An Emulator-Based Analysis of Pre- and Post-Reconstruction Power Spectra

Abstract

We present joint measurements of the pre- and post-reconstruction power spectra, and , together with their cross-power spectrum, , for the Luminous Red Galaxies (LRGs) in the DESI Data Release 1 (DR1). We jointly analyse these observables with an emulator-based full-shape modeling framework, thereby, for the first time, we extract complementary nonlinear information from the galaxy density field before and after reconstruction in real survey data. Specifically, including and in addition to (hereafter ) yields an improvement of approximately - in the constraint in both CDM and CDM, depending on the redshift bin, relative to the -only analysis with the cosmic microwave background distance priors (hereafter CMB). In CDM, the joint CMB+ analysis can tighten the constraints on by approximately - across the two LRG redshift bins, compared to the CMB+ combination. Further incorporating the Type Ia supernova dataset and comparing the cosmological constraints in CDM from each individual power-spectrum component with those from the full combination, we find that consistently provides the tightest constraints. From the joint CMB++DES-Dovekie dataset, we obtain and for the \texttt{LRG1} sample, and and for \texttt{LRG2}. These results demonstrate that combining pre- and post-reconstruction power spectra with their cross-correlation enables DESI to harvest additional nonlinear information, leading to tighter constraints on cosmological parameters.

Paper Structure

This paper contains 12 sections, 17 equations, 25 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Methodology
  3. Power spectrum
  4. Power spectrum window
  5. Building the emulator
  6. Mocks and data
  7. Results
  8. Cubic mock tests
  9. Validation of window matrix and cut-sky mock tests
  10. DESI DR1 results
  11. Conclusion
  12. Data Availability

Figures (25)

  • Figure 1: Comoving number density as a function of redshift for the DESI DR1 LRG sample. The horizontal lines show the mean number densities in three redshift bins, illustrating the nearly constant number density in the first two bins that motivates our emulator setup.
  • Figure 2: Left panel: Correlation matrix of the pre-, post-, and cross-reconstruction power-spectrum multipoles for the DESI DR1 LRG sample. Each block corresponds to 25 $k$-bins of $P_0$ or $P_2$ from the pre-, post-, or cross-reconstructed catalogs. The color scale indicates the correlation coefficient between multipole bins, highlighting the strong correlations between different $k$-modes and between the three types of spectra. Right panels: Correlation coefficients between the monopole (upper) and quadrupole (lower) from various types of power spectra: pre vs. post (solid black), pre vs. cross (blue dashed), and post vs. cross (red dot-dashed). The dashed horizontal line shows a perfect correlation (i.e., corr = 1) for a reference.
  • Figure 3: Same as Fig. \ref{['fig:2z1']}, but for LRG2.
  • Figure 4: Mock-test results for the first redshift bin under different modeling setups. Left panels show results in the $\Lambda$CDM model, while right panels correspond to the $w$CDM model. From top to bottom, the three blocks (separated by dot-dashed lines) correspond to Dark Quest (DQ) cubic mocks, Abacus cubic mocks, and Abacus cut-sky mocks, respectively. Within each block, constraints are shown for analyses using $P_{\rm pre}$, $P_{\rm post}$, $P_{\rm cross}$, and $P_{\rm all}$. Points and error bars denote the posterior mean values and $1\sigma$ uncertainties, normalised by the fiducial parameter values, while the vertical dashed lines indicate the fiducial cosmology of the mocks. The overall consistency across different mock sets and power-spectrum combinations demonstrate the robustness of the full-shape fitting pipeline.
  • Figure 5: Same as Fig. \ref{['fig:mockz1']}, but for the second redshift bin. Since the redshift ($z=0.8$) of the second $z$ bin of Abacus-2 cutsky mocks differs substantially from that of the emulator ($z=0.689$), we do not perform a mock validation for that bin in this work.
  • ...and 20 more figures