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Suppressing the sample variance of DESI-like galaxy clustering with fast simulations

Fuente: arXiv

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Auteurs principaux:	Ding, Z., Variu, A., Alam, S., Yu, Y., Chuang, C., Paillas, E., Garcia-Quintero, C., Chen, X., Mena-Fernández, J., Aguilar, J., Ahlen, S., Brooks, D., Claybaugh, T., de la Macorra, A., Doel, P., Fanning, K., Forero-Romero, J. E., Gaztañaga, E., Gontcho, S. Gontcho A, Gutierrez, G., Hahn, C., Honscheid, K., Howlett, C., Juneau, S., Kehoe, R., Kisner, T., Kremin, A., Lambert, A., Landriau, M., Guillou, L. Le, Manera, M., Miquel, R., Mueller, E., Myers, A. D., Nie, J., Niz, G., Poppett, C., Rezaie, M., Rossi, G., Sanchez, E., Schubnell, M., Seo, H., Silber, J., Sprayberry, D., Tarlé, G., Vargas-Magaña, M., Zou, H.
Format:	Preprint
Publié:	2024
Sujets:	Cosmology and Nongalactic Astrophysics
Accès en ligne:	Acceder al recurso
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author	Ding, Z. Variu, A. Alam, S. Yu, Y. Chuang, C. Paillas, E. Garcia-Quintero, C. Chen, X. Mena-Fernández, J. Aguilar, J. Ahlen, S. Brooks, D. Claybaugh, T. de la Macorra, A. Doel, P. Fanning, K. Forero-Romero, J. E. Gaztañaga, E. Gontcho, S. Gontcho A Gutierrez, G. Hahn, C. Honscheid, K. Howlett, C. Juneau, S. Kehoe, R. Kisner, T. Kremin, A. Lambert, A. Landriau, M. Guillou, L. Le Manera, M. Miquel, R. Mueller, E. Myers, A. D. Nie, J. Niz, G. Poppett, C. Rezaie, M. Rossi, G. Sanchez, E. Schubnell, M. Seo, H. Silber, J. Sprayberry, D. Tarlé, G. Vargas-Magaña, M. Zou, H.
author_facet	Ding, Z. Variu, A. Alam, S. Yu, Y. Chuang, C. Paillas, E. Garcia-Quintero, C. Chen, X. Mena-Fernández, J. Aguilar, J. Ahlen, S. Brooks, D. Claybaugh, T. de la Macorra, A. Doel, P. Fanning, K. Forero-Romero, J. E. Gaztañaga, E. Gontcho, S. Gontcho A Gutierrez, G. Hahn, C. Honscheid, K. Howlett, C. Juneau, S. Kehoe, R. Kisner, T. Kremin, A. Lambert, A. Landriau, M. Guillou, L. Le Manera, M. Miquel, R. Mueller, E. Myers, A. D. Nie, J. Niz, G. Poppett, C. Rezaie, M. Rossi, G. Sanchez, E. Schubnell, M. Seo, H. Silber, J. Sprayberry, D. Tarlé, G. Vargas-Magaña, M. Zou, H.
contents	Ongoing and upcoming galaxy redshift surveys, such as the Dark Energy Spectroscopic Instrument (DESI) survey, will observe vast regions of sky and a wide range of redshifts. In order to model the observations and address various systematic uncertainties, N-body simulations are routinely adopted, however, the number of large simulations with sufficiently high mass resolution is usually limited by available computing time. Therefore, achieving a simulation volume with the effective statistical errors significantly smaller than those of the observations becomes prohibitively expensive. In this study, we apply the Convergence Acceleration by Regression and Pooling (CARPool) method to mitigate the sample variance of the DESI-like galaxy clustering in the AbacusSummit simulations, with the assistance of the quasi-N-body simulations FastPM. Based on the halo occupation distribution (HOD) models, we construct different FastPM galaxy catalogs, including the luminous red galaxies (LRGs), emission line galaxies (ELGs), and quasars, with their number densities and two-point clustering statistics well matched to those of AbacusSummit. We also employ the same initial conditions between AbacusSummit and FastPM to achieve high cross-correlation, as it is useful in effectively suppressing the variance. Our method of reducing noise in clustering is equivalent to performing a simulation with volume larger by a factor of 5 and 4 for LRGs and ELGs, respectively. We also mitigate the standard deviation of the LRG bispectrum with the triangular configurations $k_2=2k_1=0.2$ h/Mpc by a factor of 1.6. With smaller sample variance on galaxy clustering, we are able to constrain the baryon acoustic oscillations (BAO) scale parameters to higher precision. The CARPool method will be beneficial to better constrain the theoretical systematics of BAO, redshift space distortions (RSD) and primordial non-Gaussianity (NG).
format	Preprint
id	arxiv_https___arxiv_org_abs_2404_03117
institution	arXiv
publishDate	2024
record_format	arxiv
spellingShingle	Suppressing the sample variance of DESI-like galaxy clustering with fast simulations Ding, Z. Variu, A. Alam, S. Yu, Y. Chuang, C. Paillas, E. Garcia-Quintero, C. Chen, X. Mena-Fernández, J. Aguilar, J. Ahlen, S. Brooks, D. Claybaugh, T. de la Macorra, A. Doel, P. Fanning, K. Forero-Romero, J. E. Gaztañaga, E. Gontcho, S. Gontcho A Gutierrez, G. Hahn, C. Honscheid, K. Howlett, C. Juneau, S. Kehoe, R. Kisner, T. Kremin, A. Lambert, A. Landriau, M. Guillou, L. Le Manera, M. Miquel, R. Mueller, E. Myers, A. D. Nie, J. Niz, G. Poppett, C. Rezaie, M. Rossi, G. Sanchez, E. Schubnell, M. Seo, H. Silber, J. Sprayberry, D. Tarlé, G. Vargas-Magaña, M. Zou, H. Cosmology and Nongalactic Astrophysics Ongoing and upcoming galaxy redshift surveys, such as the Dark Energy Spectroscopic Instrument (DESI) survey, will observe vast regions of sky and a wide range of redshifts. In order to model the observations and address various systematic uncertainties, N-body simulations are routinely adopted, however, the number of large simulations with sufficiently high mass resolution is usually limited by available computing time. Therefore, achieving a simulation volume with the effective statistical errors significantly smaller than those of the observations becomes prohibitively expensive. In this study, we apply the Convergence Acceleration by Regression and Pooling (CARPool) method to mitigate the sample variance of the DESI-like galaxy clustering in the AbacusSummit simulations, with the assistance of the quasi-N-body simulations FastPM. Based on the halo occupation distribution (HOD) models, we construct different FastPM galaxy catalogs, including the luminous red galaxies (LRGs), emission line galaxies (ELGs), and quasars, with their number densities and two-point clustering statistics well matched to those of AbacusSummit. We also employ the same initial conditions between AbacusSummit and FastPM to achieve high cross-correlation, as it is useful in effectively suppressing the variance. Our method of reducing noise in clustering is equivalent to performing a simulation with volume larger by a factor of 5 and 4 for LRGs and ELGs, respectively. We also mitigate the standard deviation of the LRG bispectrum with the triangular configurations $k_2=2k_1=0.2$ h/Mpc by a factor of 1.6. With smaller sample variance on galaxy clustering, we are able to constrain the baryon acoustic oscillations (BAO) scale parameters to higher precision. The CARPool method will be beneficial to better constrain the theoretical systematics of BAO, redshift space distortions (RSD) and primordial non-Gaussianity (NG).
title	Suppressing the sample variance of DESI-like galaxy clustering with fast simulations
topic	Cosmology and Nongalactic Astrophysics
url	https://arxiv.org/abs/2404.03117