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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 +31
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…
Galaxy Distribution Incompleteness Testing Using Self-Organizing Maps
Isaac McMahon, Markus Michael Rau, Rachel Mandelbaum
The calibration of redshift distributions for photometric samples using spectroscopic surveys is plagued by the difficulty in modelling the selection functions of spectroscopic sur…
Photometric Redshifts from SDSS Images with an Interpretable Deep Capsule Network
Biprateep Dey, Brett H. Andrews, Jeffrey A. Newman +3
Studies of cosmology, galaxy evolution, and astronomical transients with current and next-generation wide-field imaging surveys like the Rubin Observatory Legacy Survey of Space an…
Re-calibrating Photometric Redshift Probability Distributions Using Feature-space Regression
Biprateep Dey, Jeffrey A. Newman, Brett H. Andrews +5
Many astrophysical analyses depend on estimates of redshifts (a proxy for distance) determined from photometric (i.e., imaging) data alone. Inaccurate estimates of photometric reds…
The Role of Machine Learning in the Next Decade of Cosmology
Michelle Ntampaka, Camille Avestruz, Steven Boada +27
In recent years, machine learning (ML) methods have remarkably improved how cosmologists can interpret data. The next decade will bring new opportunities for data-driven cosmologic…
Calibrating Long Period Variables as Standard Candles with Machine Learning
Markus Michael Rau, Sergey E. Koposov, Hy Trac +1
Variable stars with well-calibrated period-luminosity relationships provide accurate distance measurements to nearby galaxies and are therefore a vital tool for cosmology and astro…