1 citations · 1 across the 6 of their papers we have counts for
7 papers · 1 filter
ABC-SN: Attention Based Classifier for Supernova Spectra
Willow Fox Fortino, Federica B. Bianco, Pavlos Protopapas +2
While significant advances have been made in photometric classification ahead of the millions of transient events and hundreds of supernovae (SNe) each night that the Vera C. Rubin…
Deep Learning for Astrophysics: An Open Textbook from the NASA Cosmic Origins AI/ML Science and Technology Interest Group
Yuan-Sen Ting, Digvijay Wadekar, Phill Cargile +21
Recent community assessments identify education as a principal barrier to adopting modern machine learning in astronomy. We present Deep Learning for Astrophysics, a freely availab…
Learning What's Real: Disentangling Signal and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics
Pablo Mercader-Perez, Carolina Cuesta-Lazaro, Daniel Muthukrishna +5
Data collected from the physical world is always a combination of multiple sources: an underlying signal from the physical process of interest and a signal from measurement-depende…
ASTRAFier: A Novel and Scalable Transformer-based Stellar Variability Classifier
Paul F. X. Gregory, Jeroen Audenaert, Mykyta Kliapets +5
Photometric missions such as Kepler and TESS have generated millions of light curves covering almost the entire sky, offering unprecedented opportunities to study stellar variabili…
Simulation-Based Pretraining and Domain Adaptation for Astronomical Time Series with Minimal Labeled Data
Rithwik Gupta, Daniel Muthukrishna, Jeroen Audenaert
Astronomical time-series analysis faces a critical limitation: the scarcity of labeled observational data. We present a pre-training approach that leverages simulations, significan…
Transfer Learning for Transient Classification: From Simulations to Real Data and ZTF to LSST
Rithwik Gupta, Daniel Muthukrishna, Nabeel Rehemtulla +1
Machine learning has become essential for automated classification of astronomical transients, but current approaches face significant limitations: classifiers trained on simulatio…