BINDing the lightcone: A suite of astrophysical ray-traced weak lensing and SZ maps
Abstract
Recent multiwavelength observations of galaxy group and cluster gas suggest stronger baryonic feedback than our best-calibrated hydrodynamical simulations produce, while modeling this feedback remains a primary source of uncertainty in Stage-IV weak-lensing (WL) analyses. We present a suite of ray-traced maps generated with BIND (Baryonic INpainting with Deep learning), a conditional flow-matching model that paints baryonic mass and gas thermodynamics onto the halos of dark-matter-only simulations, and which was developed in a companion paper. Applied to IllustrisTNG300-Dark and ray-traced, we generate convergence, optical depth, and Compton-y maps at five source redshifts, each with $1000$ pseudo-independent realizations. We build lightcones across a 256-node Sobol sequence spanning the thirty-dimensional IllustrisTNG galaxy formation prior, with individual parameter variations and at the fiducial model. In validation, the maps match those built from the IllustrisTNG300 halos to within LSST-Y10-like precision for a range of WL statistics. Across the prior, the response to feedback exceeds Stage-IV statistical precision by more than an order of magnitude on small scales, and different statistics respond to different model sectors: galactic winds control the WL power spectrum and gas auto- and cross-spectra, while the stellar initial mass function slope and AGN parameters shape the morphological statistics (PDF, peaks, minima, and Minkowski functionals). Finally, we find that the response of statistics can be compressed into seven halo properties which linearly predict a range of WL and SZ statistics. We publicly release the maps, statistics, and model tables.
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BibTeX
@misc{lee2026binding,
title = {{BINDing the lightcone: A suite of astrophysical ray-traced weak lensing and SZ maps}},
author = {Max E. Lee and Shy Genel and Zoltan Haiman and Greg L. Bryan and Boryana Hadzhiyska},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.10710},
eprint = {2609.10710},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.10710},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - BINDing the lightcone: A suite of astrophysical ray-traced weak lensing and SZ maps
AU - Max E. Lee
AU - Shy Genel
AU - Zoltan Haiman
AU - Greg L. Bryan
AU - Boryana Hadzhiyska
PY - 2026
DA - 2026/09/09
PB - arXiv
DO - 10.48550/arxiv.2609.10710
UR - https://arxiv.org/abs/2609.10710
AB - Recent multiwavelength observations of galaxy group and cluster gas suggest stronger baryonic feedback than our best-calibrated hydrodynamical simulations produce, while modeling this feedback remains a primary source of uncertainty in Stage-IV weak-lensing (WL) analyses. We present a suite of ray-traced maps generated with BIND (Baryonic INpainting with Deep learning), a conditional flow-matching model that paints baryonic mass and gas thermodynamics onto the halos of dark-matter-only simulations, and which was developed in a companion paper. Applied to IllustrisTNG300-Dark and ray-traced, we generate convergence, optical depth, and Compton-y maps at five source redshifts, each with $1000$ pseudo-independent realizations. We build lightcones across a 256-node Sobol sequence spanning the thirty-dimensional IllustrisTNG galaxy formation prior, with individual parameter variations and at the fiducial model. In validation, the maps match those built from the IllustrisTNG300 halos to within LSST-Y10-like precision for a range of WL statistics. Across the prior, the response to feedback exceeds Stage-IV statistical precision by more than an order of magnitude on small scales, and different statistics respond to different model sectors: galactic winds control the WL power spectrum and gas auto- and cross-spectra, while the stellar initial mass function slope and AGN parameters shape the morphological statistics (PDF, peaks, minima, and Minkowski functionals). Finally, we find that the response of statistics can be compressed into seven halo properties which linearly predict a range of WL and SZ statistics. We publicly release the maps, statistics, and model tables.
ER -