5 papers
Geometric deep learning for galaxy-halo connection: a case study for galaxy intrinsic alignments
Yesukhei Jagvaral, Francois Lanusse, Rachel Mandelbaum
Forthcoming cosmological imaging surveys, such as the Rubin Observatory LSST, require large-scale simulations encompassing realistic galaxy populations for a variety of scientific…
An Empirical Model For Intrinsic Alignments: Insights From Cosmological Simulations
Nicholas Van Alfen, Duncan Campbell, Jonathan Blazek +5
We extend current models of the halo occupation distribution (HOD) to include a flexible, empirical framework for the forward modeling of the intrinsic alignment (IA) of galaxies.…
Photometric redshifts and intrinsic alignments: degeneracies and biases in 32pt analysis
C. Danielle Leonard, Markus Michael Rau, Rachel Mandelbaum
We present a systematic study of cosmological parameter bias in weak lensing and large-scale structure analyses for upcoming imaging surveys induced by the interplay of intrinsic a…
Joint inference of multiplicative and additive systematics in galaxy density fluctuations and clustering measurements
Federico Berlfein, Rachel Mandelbaum, Scott Dodelson +1
Galaxy clustering measurements are a key probe of the matter density field in the Universe. With the era of precision cosmology upon us, surveys rely on precise measurements of the…
Accurate field-level weak lensing inference for precision cosmology
Alan Junzhe Zhou, Xiangchong Li, Scott Dodelson +1
We present , a catalog-to-cosmology pipeline for general flat-sky field-level inference, which provides access to cosmological information beyond the two-point stati…