Parity-odd Four-Point Correlation Function from DESI Data Release 1 Luminous Red Galaxy Sample
J. Hou, R. N. Cahn, J. Aguilar, S. Ahlen, D. Bianchi, D. Brooks, T. Claybaugh, P. Doel, J. E. Forero-Romero, E. Gazta\~naga, L. Le Guillou, G. Gutierrez, C. Howlett, M. Ishak, R. Joyce, A. Kremin, O. Lahav, C. Lamman, M. Landriau, A. de la Macorra, R. Miquel, S. Nadathur, G. Niz

TL;DR
This paper measures the parity-odd four-point correlation function in DESI DR1 LRG data to test fundamental symmetries, finding no significant detection but highlighting the importance of data completeness for future analyses.
Contribution
First measurement of the parity-odd four-point function in large-scale structure data, using DESI DR1 LRG sample and multiple covariance estimation methods.
Findings
Auto-correlation signals up to 4σ significance with full covariance
Observed signals are consistent with statistical fluctuations and no definitive parity violation
Sample completeness impacts detection sensitivity and future data will improve constraints
Abstract
The parity-odd four-point function provides a unique probe of fundamental symmetries and potential new physics in the large-scale structure of the Universe. We present measurements of the parity-odd four-point function using the DESI DR1 LRG sample and assess its detection significance. Our analysis considers both auto- and cross-correlations, using two complementary approaches to the covariance: (i) the full analytic covariance matrix applied to the uncompressed data vector, and (ii) a compressed data vector combined with a hybrid covariance matrix constructed from simulations and analytic estimates. When using the full analytic covariance matrix without corrections, we observe apparent auto-correlation signals with significance up to . However, this excess is also consistent with a mismatch between the statistical fluctuations estimated from the simulations and those present…
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Taxonomy
TopicsStatistical Mechanics and Entropy · Cosmology and Gravitation Theories · Statistical and numerical algorithms
