Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Trusov, Svyatoslav"'
We present a Neural Network based emulator for the galaxy redshift-space power spectrum that enables several orders of magnitude acceleration in the galaxy clustering parameter inference, while preserving 3$\sigma$ accuracy better than 0.5\% up to $k
Externí odkaz:
http://arxiv.org/abs/2403.20093
Autor:
Trusov, Svyatoslav, Zarrouk, Pauline, Cole, Shaun, Norberg, Peder, Zhao, Cheng, Aguilar, Jessica Nicole, Ahlen, Steven, Brooks, David, de la Macorra, Axel, Doel, Peder, Font-Ribera, Andreu, Honscheid, Klaus, Kisner, Theodore, Landriau, Martin, Magneville, Christophe, Miquel, Ramon, Nie, Jundan, Poppett, Claire, Schubnell, Michael, Tarlé, Gregory, Zhou, Zhimin
Publikováno v:
Monthly Notices of the Royal Astronomical Society, Volume 527, Issue 3, January 2024
We present an approach for accurate estimation of the covariance of 2-point correlation functions that requires fewer mocks than the standard mock-based covariance. This can be achieved by dividing a set of mocks into jackknife regions and fitting th
Externí odkaz:
http://arxiv.org/abs/2306.16332
Autor:
Trusov, Svyatoslav, Zarrouk, Pauline, Cole, Shaun, Norberg, Peder, Zhao, Cheng, Aguilar, Jessica Nicole, Ahlen, Steven, Brooks, David, de la Macorra, Axel, Doel, Peter, Font-Ribera, Andreu, Honscheid, Klaus, Kisner, Theodore, Landriau, Martin, Magneville, Christophe, Miquel, Ramon, Nie, Jundan, Poppett, Claire, Schubnell, Michael, Tarlé, Gregory
Publikováno v:
Monthly Notices of the Royal Astronomical Society; Jan2024, Vol. 527 Issue 3, p9048-9060, 13p
Autor:
Trusov, Svyatoslav, Zarrouk, Pauline, Cole, Shaun, Norberg, Peder, Zhao, Cheng, Aguilar, Jessica Nicole, Ahlen, Steven, Brooks, David, de la Macorra, Axel, Doel, Peder, Font-Ribera, Andreu, Honscheid, Klaus, Kisner, Theodore, Landriau, Martin, Magneville, Christophe, Miquel, Ramon, Nie, Jundan, Poppett, Claire, Schubnell, Michael, Tarlé, Gregory, Zhou, Zhimin
We present an approach for accurate estimation of the covariance of 2-point correlation functions that requires fewer mocks than the standard mock-based covariance. This can be achieved by dividing a set of mocks into jackknife regions and fitting th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::caac47a4adff209a29408663d769a1cd
http://arxiv.org/abs/2306.16332
http://arxiv.org/abs/2306.16332