6 citations · 6 across the 6 of their papers we have counts for
7 papers
Not So Heavy Metal I: Low Metallicity Enhances the Rate of high-redshift Superluminous Supernovae
Ben Margalit, Allison L. Strom, Cristina Andrade +4
Type-I superluminous supernovae (SLSNe) are extremely luminous stellar explosions originating from the core collapse of massive stars. Unlike normal core-collapse supernovae, howev…
Leveraging Multimodality for Real-Time Classification of Transients and Variables found by the Zwicky Transient Facility
Ved G. Shah, Nabeel Rehemtulla, Adam A. Miller +13
Modern time-domain surveys such as the Zwicky Transient Facility (ZTF) generate hundreds of thousands of alerts each night, making real-time decisions for follow-up observations a…
SELDON: Supernova Explosions Learned by Deep ODE Networks
Jiezhong Wu, Jack O'Brien, Jennifer Li +6
The discovery rate of optical transients will explode to 10 million public alerts per night once the Vera C. Rubin Observatory's Legacy Survey of Space and Time comes online, overw…
Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration
LSST Dark Energy Science Collaboration, Eric Aubourg, Camille Avestruz +63
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that cha…
Pre-training vision models for the classification of alerts from wide-field time-domain surveys
Nabeel Rehemtulla, Adam A. Miller, Mike Walmsley +15
Modern wide-field time-domain surveys facilitate the study of transient, variable and moving phenomena by conducting image differencing and relaying alerts to their communities. Ma…
StarEmbed: Benchmarking Time Series Foundation Models on Astronomical Observations of Variable Stars
Weijian Li, Hong-Yu Chen, Nabeel Rehemtulla +6
Current time series foundation model (TSFM) training corpora largely omit data with certain complexities like irregular temporal sampling. Astronomical time series of stellar fluxe…
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…