4 papers
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…
LITMUS: Bayesian Lag Recovery in Reverberation Mapping with Fast Differentiable Models
Hugh McDougall, Tamara M. Davis, Benjamin J. S. Pope
Reverberation mapping is a technique in which the mass of a Seyfert I galaxy's central supermassive black hole is estimated, along with the system's physical scale, from the timesc…
Unsupervised Searches for Cosmological Parity Violation: Improving Detection Power with the Neural Field Scattering Transform
Matthew Craigie, Peter L. Taylor, Yuan-Sen Ting +3
Recent studies using four-point correlations suggest a parity violation in the galaxy distribution, though the significance of these detections is sensitive to the choice of simula…