The ALHAMBRA survey : Estimation of the clustering signal encoded in the cosmic variance
C. L\'opez-Sanjuan, A. J. Cenarro, C. Hern\'andez-Monteagudo, P., Arnalte-Mur, J. Varela, K. Viironen, A. Fern\'andez-Soto, V. J. Mart\'inez,, E. Alfaro, B. Ascaso, A. del Olmo, L. A. D\'iaz-Garc\'ia, Ll. Hurtado-Gil, M., Moles, A. Molino, J. Perea, M. Povi\'c, J. A. L. Aguerri

TL;DR
This study demonstrates that cosmic variance measurements in the ALHAMBRA survey effectively capture galaxy clustering, enabling bias estimation independently of traditional correlation function methods across various galaxy populations and redshifts.
Contribution
It introduces a novel approach to estimate galaxy bias from cosmic variance measurements, validated against correlation function results in the ALHAMBRA survey.
Findings
Cosmic variance correlates with galaxy bias and clustering.
Galaxy bias increases with luminosity and redshift.
Red galaxies exhibit higher bias than blue galaxies.
Abstract
The relative cosmic variance () is a fundamental source of uncertainty in pencil-beam surveys and, as a particular case of count-in-cell statistics, can be used to estimate the bias between galaxies and their underlying dark-matter distribution. Our goal is to test the significance of the clustering information encoded in the measured in the ALHAMBRA survey. We measure the cosmic variance of several galaxy populations selected with band luminosity at as the intrinsic dispersion in the number density distribution derived from the 48 ALHAMBRA subfields. We compare the observational with the cosmic variance of the dark matter expected from the theory, . This provides an estimation of the galaxy bias . The galaxy bias from the cosmic variance is in excellent agreement with the bias estimated by two-point…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
