18 citations · 32 across the 7 of their papers we have counts for
4 papers · 1 filter
Learning Intrinsic Alignments from Local Galaxy Environments
Matthew Craigie, Eric Huff, Yuan-Sen Ting +2
We present DELTA (Data-Empiric Learned Tidal Alignments), a deep learning model that isolates galaxy intrinsic alignments (IAs) from weak lensing distortions using only observation…
Learning Balanced Field Summaries of the Large-Scale Structure with the Neural Field Scattering Transform
Matthew Craigie, Yuan-Sen Ting, Rossana Ruggeri +1
We present a cosmology analysis of simulated weak lensing convergence maps using the Neural Field Scattering Transform (NFST) to constrain cosmological parameters. The NFST extends…
Inferring Cosmological Parameters on SDSS via Domain-Generalized Neural Networks and Lightcone Simulations
Jun-Young Lee, Ji-hoon Kim, Minyong Jung +6
We present a proof-of-concept simulation-based inference on and from the SDSS BOSS LOWZ NGC catalog using neural networks and domain generalization techniques w…
A new approach to observational cosmology using the scattering transform
Sihao Cheng, Yuan-Sen Ting, Brice Ménard +1
Parameter estimation with non-Gaussian stochastic fields is a common challenge in astrophysics and cosmology. In this paper, we advocate performing this task using the scattering t…