7 citations · 8 across the 6 of their papers we have counts for
6 papers
Towards the Two-Loop EFTofLSS in Galaxy Lensing Surveys
Evan Saraivanov, Henrique Rubira, Vivian Miranda +1
Extracting cosmological information from Stage IV weak lensing surveys requires non-linear modelling of the matter power spectrum that is accurate across a broad range of scales an…
Attention-based Neural Network Emulators for Multi-Probe Data Vectors Part III: Modeling The Next Generation Surveys
Yijie Zhu, Evan Saraivanov, Joshua A. Kable +6
Machine learning can accelerate cosmological inferences that involve many sequential evaluations of computationally expensive data vectors. Previous works in this series have exami…
Attention-Based Neural Network Emulators for Multi-Probe Data Vectors Part II: Assessing Tension Metrics
Evan Saraivanov, Kunhao Zhong, Vivian Miranda +3
The next generation of cosmological surveys is expected to generate unprecedented high-quality data, consequently increasing the already substantial computational costs of Bayesian…
Attention-based Neural Network Emulators for Multi-Probe Data Vectors Part I: Forecasting the Growth-Geometry split
Kunhao Zhong, Evan Saraivanov, James Caputi +4
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 co…
Constraining Baryonic Physics with DES Y1 and Planck data -- Combining Galaxy Clustering, Weak Lensing, and CMB Lensing
Jiachuan Xu, Tim Eifler, Vivian Miranda +6
We constrain cosmology and baryonic feedback scenarios with a joint analysis of weak lensing, galaxy clustering, cosmic microwave background (CMB) lensing, and their cross-correlat…
Growth and Geometry Split in Light of the DES-Y3 Survey
Kunhao Zhong, Evan Saraivanov, Vivian Miranda +3
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 CDM and CDM model we separate the…