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| Format: | Preprint |
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2024
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| Online Access: | https://arxiv.org/abs/2411.12027 |
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| _version_ | 1866918136223105024 |
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| 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 |