9 citations · 17 across the 5 of their papers we have counts for
5 papers
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…
Worse than Zero-shot? A Fact-Checking Dataset for Evaluating the Robustness of RAG Against Misleading Retrievals
Linda Zeng, Rithwik Gupta, Divij Motwani +2
Retrieval-augmented generation (RAG) has shown impressive capabilities in mitigating hallucinations in large language models (LLMs). However, LLMs struggle to maintain consistent r…
A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-Series
Rithwik Gupta, Daniel Muthukrishna, Michelle Lochner
Automating anomaly detection is an open problem in many scientific fields, particularly in time-domain astronomy, where modern telescopes generate millions of alerts per night. Cur…
A Classifier-Based Approach to Multi-Class Anomaly Detection for Astronomical Transients
Rithwik Gupta, Daniel Muthukrishna, Michelle Lochner
Automating real-time anomaly detection is essential for identifying rare transients, with modern survey telescopes generating tens of thousands of alerts per night, and future tele…