A machine learning classifier for microlensing in wide-field surveys. - Daniel Godines; Etienne Bachelet; Gautham Narayan; R. A. Street (Astron. Comput., 2019)

RAPID: Early Classification of Explosive Transients using Deep Learning. - Daniel Muthukrishna; Gautham Narayan; Kaisey S. Mandel; Rahul Biswas; Renée Hlozek (CoRR, 2019)

Real-time detection of anomalies in large-scale transient surveys. - Daniel Muthukrishna; Kaisey S. Mandel; Michelle Lochner; Sara Webb; Gautham Narayan (CoRR, 2021)

Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data. - Daniel Muthukrishna; Kaisey S. Mandel; Michelle Lochner; Sara Webb; Gautham Narayan (CoRR, 2021)

Machine Learning and Cosmology. - Cora Dvorkin; Siddharth Mishra-Sharma; Brian Nord; V. Ashley Villar; Camille Avestruz; Keith Bechtol; Aleksandra Ciprijanovic; Andrew J. Connolly; Lehman H. Garrison; Gautham Narayan; Francisco Villaescusa-Navarro (CoRR, 2022)

Preliminary Report on Mantis Shrimp: a Multi-Survey Computer Vision Photometric Redshift Model. - Andrew Engel; Gautham Narayan; Nell Byler (CoRR, 2024)

ORACLE: A Real-Time, Hierarchical, Deep-Learning Photometric Classifier for the LSST. - Ved G. Shah; Alex Gagliano; Konstantin Malanchev; Gautham Narayan; LSST Dark Energy Science Collaboration (CoRR, 2025)

Mantis Shrimp: Exploring Photometric Band Utilization in Computer Vision Networks for Photometric Redshift Estimation. - Andrew Engel; Nell Byler; Adam Tsou; Gautham Narayan; Emmanuel Bonilla; Ian Smith (CoRR, 2025)