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pro vyhledávání: '"Saraivanov, Evan"'
Autor:
Saraivanov, Evan, Zhong, Kunhao, Miranda, Vivian, Boruah, Supranta S., Eifler, Tim, Krause, Elisabeth
The next generation of cosmological surveys is expected to generate unprecedented high-quality data, consequently increasing the already substantial computational costs of Bayesian statistical methods. This will pose a significant challenge to analyz
Externí odkaz:
http://arxiv.org/abs/2403.12337
Autor:
Zhong, Kunhao, Saraivanov, Evan, Caputi, James, Miranda, Vivian, Boruah, Supranta S., Eifler, Tim, Krause, Elisabeth
We present a new class of machine-learning emulators that accurately model the cosmic shear, galaxy-galaxy lensing, and galaxy clustering real space correlation functions in the context of Rubin Observatory year one simulated data. To illustrate its
Externí odkaz:
http://arxiv.org/abs/2402.17716
Autor:
Xu, Jiachuan, Eifler, Tim, Miranda, Vivian, Fang, Xiao, Saraivanov, Evan, Krause, Elisabeth, Huang, Hung-Jin, Benabed, Karim, Zhong, Kunhao
We constrain cosmology and baryonic feedback scenarios with a joint analysis of weak lensing, galaxy clustering, cosmic microwave background (CMB) lensing, and their cross-correlations (so-called 6$\times$2) using data from the Dark Energy Survey (DE
Externí odkaz:
http://arxiv.org/abs/2311.08047
Autor:
Zhong, Kunhao, Saraivanov, Evan, Miranda, Vivian, Xu, Jiachuan, Eifler, Tim, Krause, Elisabeth
We test the smooth dark energy paradigm using Dark Energy Survey (DES) Year 1 and Year 3 weak lensing and galaxy clustering data. Within the $\Lambda$CDM and $w$CDM model we separate the expansion and structure growth history by splitting $\Omega_\ma
Externí odkaz:
http://arxiv.org/abs/2301.03694
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