12 papers
Optimizing Deep Learning Photometric Redshifts for the Roman Space Telescope with HST/CANDELS
Ashod Khederlarian, Brett H. Andrews, Jeffrey A. Newman +2
Photometric redshifts (photo-'s) will be crucial for studies of galaxy evolution, large-scale structure, and transients with the Nancy Grace Roman Space Telescope. Deep learning…
Improved photometric redshift estimations through self-organising map-based data augmentation
Yun-Hao Zhang, Joe Zuntz, Irene Moskowitz +8
We introduce a framework for the enhanced estimation of photometric redshifts using Self-Organising Maps (SOMs). Our method projects galaxy Spectral Energy Distributions (SEDs) ont…
The Vera C. Rubin Observatory Data Preview 1
Vera C Rubin Observatory Team, Tatiana Acero Cuellar, Emily Acosta +325
We present Rubin Data Preview 1 DP1, the first data from the NSF DOE Vera C Rubin Observatory, comprising raw and calibrated single epoch images, coadds, difference images, detecti…
Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production
The RAIL Team, Jan Luca van den Busch, Eric Charles +30
Virtually all extragalactic use cases of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) require the use of galaxy redshift information, yet the vast majorit…
Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration
LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz +63
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that cha…
Diagnosing the Effects of Spectroscopic Training Set Imperfection on Photometric Redshift Performance
Alice Crafford, Alex I. Malz, Tianqing Zhang +11
Most LSST extragalactic science will rely on photometric redshifts (photo-) to extract distance information for the galaxies. However, an incomplete or non-representative traini…