524 citations · 1.1k across the 43 of their papers we have counts for
6 papers · 1 filter
AppleCiDEr. II. SpectraNet: A Spectroscopic Neural Network Classifier for Transients Demonstrated on ZTF Follow-up Data
Maojie Xu, Argyro Sasli, Alexandra Junell +21
Time-domain surveys such as the Zwicky Transient Facility have opened a new frontier in the discovery and characterization of transients. While photometric light curves provide bro…
Applying multimodal learning to Classify transient Detections Early (AppleCiDEr) I: Data set, methods, and infrastructure
Alexandra Junell, Argyro Sasli, Felipe Fontinele Nunes +18
Modern time-domain surveys like the Zwicky Transient Facility (ZTF) and the Legacy Survey of Space and Time (LSST) generate hundreds of thousands to millions of alerts, demanding a…
CCSNscore: A multi-input deep learning tool for classification of core-collapse supernovae using SED-Machine spectra
Yashvi Sharma, Ashish A. Mahabal, Jesper Sollerman +16
Supernovae (SNe) come in various flavors and are classified into different types based on emission and absorption lines in their spectra. SN candidates are now abundant with the ad…
Machine-directed gravitational-wave counterpart discovery
Niharika Sravan, Matthew J. Graham, Michael W. Coughlin +2
Joint observations in electromagnetic and gravitational waves shed light on the physics of objects and surrounding environments with extreme gravity that are otherwise unreachable…
Autonomous real-time science-driven follow-up of survey transients
Niharika Sravan, Matthew J. Graham, Christoffer Fremling +1
Astronomical surveys continue to provide unprecedented insights into the time-variable Universe and will remain the source of groundbreaking discoveries for years to come. However,…
Real-Time Value-Driven Data Augmentation in the Era of LSST
Niharika Sravan, Dan Milisavljevic, Jack M. Reynolds +2
The deluge of data from time-domain surveys is rendering traditional human-guided data collection and inference techniques impractical. We propose a novel approach for conducting d…