_version_ 1866918136223105024
author Forero-Sánchez, Daniel
Rashkovetskyi, Michael
Alves, Otávio
de Mattia, Arnaud
Padmanabhan, Nikhil
Seo, Hee-Jong
Nadathur, Seshadri
Ross, Ashley J.
Zarrouk, Pauline
Gil-Marín, Héctor
Yu, Jiaxi
Ding, Zhejie
Andrade, Uendert
Chen, Xinyi
Garcia-Quintero, Cristhian
Mena-Fernández, Juan
Ahlen, Steven
Bianchi, Davide
Brooks, David
Burtin, Etienne
Chaussidon, Edmond
Claybaugh, Todd
Cole, Shaun
de la Macorra, Axel
Vargas, Miguel Enriquez
Gaztañaga, Enrique
Gutierrez, Gaston
Honscheid, Klaus
Howlett, Cullan
Kisner, Theodore
Landriau, Martin
Guillou, Laurent Le
Levi, Michael
Miquel, Ramon
Moustakas, John
Palanque-Delabrouille, Nathalie
Percival, Will
Pérez-Ràfols, Ignasi
Ross, Ashley J.
Rossi, Graziano
Sanchez, Eusebio
Schlegel, David
Schubnell, Michael
Seo, Hee-Jong
Sprayberry, David
Tarlé, Gregory
Magana, Mariana Vargas
Weaver, Benjamin Alan
Zou, Hu
author_facet Forero-Sánchez, Daniel
Rashkovetskyi, Michael
Alves, Otávio
de Mattia, Arnaud
Padmanabhan, Nikhil
Seo, Hee-Jong
Nadathur, Seshadri
Ross, Ashley J.
Zarrouk, Pauline
Gil-Marín, Héctor
Yu, Jiaxi
Ding, Zhejie
Andrade, Uendert
Chen, Xinyi
Garcia-Quintero, Cristhian
Mena-Fernández, Juan
Ahlen, Steven
Bianchi, Davide
Brooks, David
Burtin, Etienne
Chaussidon, Edmond
Claybaugh, Todd
Cole, Shaun
de la Macorra, Axel
Vargas, Miguel Enriquez
Gaztañaga, Enrique
Gutierrez, Gaston
Honscheid, Klaus
Howlett, Cullan
Kisner, Theodore
Landriau, Martin
Guillou, Laurent Le
Levi, Michael
Miquel, Ramon
Moustakas, John
Palanque-Delabrouille, Nathalie
Percival, Will
Pérez-Ràfols, Ignasi
Ross, Ashley J.
Rossi, Graziano
Sanchez, Eusebio
Schlegel, David
Schubnell, Michael
Seo, Hee-Jong
Sprayberry, David
Tarlé, Gregory
Magana, Mariana Vargas
Weaver, Benjamin Alan
Zou, Hu
contents The estimation of uncertainties in cosmological parameters is an important challenge in Large-Scale-Structure (LSS) analyses. For standard analyses such as Baryon Acoustic Oscillations (BAO) and Full Shape, two approaches are usually considered. First: analytical estimates of the covariance matrix use Gaussian approximations and (nonlinear) clustering measurements to estimate the matrix, which allows a relatively fast and computationally cheap way to generate matrices that adapt to an arbitrary clustering measurement. On the other hand, sample covariances are an empirical estimate of the matrix based on en ensemble of clustering measurements from fast and approximate simulations. While more computationally expensive due to the large amount of simulations and volume required, these allow us to take into account systematics that are impossible to model analytically. In this work we compare these two approaches in order to enable DESI's key analyses. We find that the configuration space analytical estimate performs satisfactorily in BAO analyses and its flexibility in terms of input clustering makes it the fiducial choice for DESI's 2024 BAO analysis. On the contrary, the analytical computation of the covariance matrix in Fourier space does not reproduce the expected measurements in terms of Full Shape analyses, which motivates the use of a corrected mock covariance for DESI's Full Shape analysis.
format Preprint
id arxiv_https___arxiv_org_abs_2411_12027
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Analytical and EZmock covariance validation for the DESI 2024 results
Forero-Sánchez, Daniel
Rashkovetskyi, Michael
Alves, Otávio
de Mattia, Arnaud
Padmanabhan, Nikhil
Seo, Hee-Jong
Nadathur, Seshadri
Ross, Ashley J.
Zarrouk, Pauline
Gil-Marín, Héctor
Yu, Jiaxi
Ding, Zhejie
Andrade, Uendert
Chen, Xinyi
Garcia-Quintero, Cristhian
Mena-Fernández, Juan
Ahlen, Steven
Bianchi, Davide
Brooks, David
Burtin, Etienne
Chaussidon, Edmond
Claybaugh, Todd
Cole, Shaun
de la Macorra, Axel
Vargas, Miguel Enriquez
Gaztañaga, Enrique
Gutierrez, Gaston
Honscheid, Klaus
Howlett, Cullan
Kisner, Theodore
Landriau, Martin
Guillou, Laurent Le
Levi, Michael
Miquel, Ramon
Moustakas, John
Palanque-Delabrouille, Nathalie
Percival, Will
Pérez-Ràfols, Ignasi
Ross, Ashley J.
Rossi, Graziano
Sanchez, Eusebio
Schlegel, David
Schubnell, Michael
Seo, Hee-Jong
Sprayberry, David
Tarlé, Gregory
Magana, Mariana Vargas
Weaver, Benjamin Alan
Zou, Hu
Cosmology and Nongalactic Astrophysics
The estimation of uncertainties in cosmological parameters is an important challenge in Large-Scale-Structure (LSS) analyses. For standard analyses such as Baryon Acoustic Oscillations (BAO) and Full Shape, two approaches are usually considered. First: analytical estimates of the covariance matrix use Gaussian approximations and (nonlinear) clustering measurements to estimate the matrix, which allows a relatively fast and computationally cheap way to generate matrices that adapt to an arbitrary clustering measurement. On the other hand, sample covariances are an empirical estimate of the matrix based on en ensemble of clustering measurements from fast and approximate simulations. While more computationally expensive due to the large amount of simulations and volume required, these allow us to take into account systematics that are impossible to model analytically. In this work we compare these two approaches in order to enable DESI's key analyses. We find that the configuration space analytical estimate performs satisfactorily in BAO analyses and its flexibility in terms of input clustering makes it the fiducial choice for DESI's 2024 BAO analysis. On the contrary, the analytical computation of the covariance matrix in Fourier space does not reproduce the expected measurements in terms of Full Shape analyses, which motivates the use of a corrected mock covariance for DESI's Full Shape analysis.
title Analytical and EZmock covariance validation for the DESI 2024 results
topic Cosmology and Nongalactic Astrophysics
url https://arxiv.org/abs/2411.12027