Neural network based emulation of galaxy power spectrum covariances: A reanalysis of BOSS DR12 data

Journal Article · 11 December 2024 · Physical Review. D.

DOI: https://doi.org/10.1103/physrevd.110.123517 · OSTI ID:2480706

Adamo, Joseph Dominick  ^[1]; Huang, Hung-Jin ^[2]; Eifler, Tim  ^[2]

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1. University of Arizona, Tucson, AZ (United States); University of Arizona
2. University of Arizona, Tucson, AZ (United States)

We train neural networks to quickly generate redshift-space galaxy power spectrum covariances from a given parameter set (cosmology and galaxy bias). This covariance emulator utilizes a combination of traditional fully connected network layers and transformer architecture to accurately predict covariance matrices for the high redshift, north galactic cap sample of the BOSS DR12 galaxy catalog. We run simulated likelihood analyses with emulated and brute-force computed covariances, and we quantify the network’s performance via two different metrics: (1) difference in Χ² and (2) likelihood contours for simulated BOSS DR 12 analyses. We find that the emulator returns excellent results over a large parameter range. We then use our emulator to perform a reanalysis of the BOSS HighZ NGC galaxy power spectrum, and find that varying covariance with cosmology along with the model vector produces Ω_m = $0.27⁢6$$$$^{+0.013}_{–0.015}$$, H₀ = 70.2 ± 1.9 km/s/Mpc, and σ₈ = $0.67⁢4$$$$^{+0.058}_{–0.077}$$. These constraints represent an average 0.46⁢σ shift in best-fit values and a 5% increase in constraining power compared to fixing the covariance matrix (Ω_m = 0.293 ± 0.017, H₀ = 70.3 ± 2.0 km/s/Mpc, σ₈ = $0.70⁢2$$$$^{+0.063}_{–0.075}$$). As a result, this work demonstrates that emulators for more complex cosmological quantities than second-order statistics can be trained over a wide parameter range at sufficiently high accuracy to be implemented in realistic likelihood analyses.

• Research Organization: University of Arizona, Tucson, AZ (United States)
• Sponsoring Organization: USDOE Office of Science (SC), High Energy Physics (HEP)
• Grant/Contract Number: SC0020215; SC0023892
• OSTI ID: 2480706
• Journal Information: Physical Review. D., Journal Name: Physical Review. D. Journal Issue: 12 Vol. 110; ISSN 2470-0010
• Publisher: American Physical Society (APS)Copyright Statement
• Country of Publication: United States
• Language: English