StratLearn-z: Improved photo-\(z\) estimation from spectroscopic data subject to selection effects

• Published in: arXiv.org
• Main Authors: Moretti, Chiara, Autenrieth, Maximilian, Serra, Riccardo, Trotta, Roberto, van Dyk, David A, Mesinger, Andrei
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 30.09.2024
• Subjects: Algorithms; Astronomical models; Bias; Integral transforms; Machine learning; Outliers (statistics); Performance degradation; Photometry; Red shift

A precise measurement of photometric redshifts (photo-z) is key for the success of modern photometric galaxy surveys. Machine learning (ML) methods show great promise in this context, but suffer from covariate shift (CS) in training sets due to selection bias where interesting sources are underrepre...