Nabeel Rehemtulla
Papers
1
Total Citations
5
H-Index
1
About
Nabeel Rehemtulla is a rising astrophysicist specializing in time-domain astronomy and the automated classification of supernovae. His primary research focuses on leveraging deep learning to rapidly and accurately categorize transient celestial events, particularly core-collapse supernovae. His most notable contribution is the development of the **CCSNscore**, a multi-input deep learning tool designed to classify core-collapse supernovae using spectra from the SED-machine instrument. This work, published in 2025, directly addresses a critical bottleneck in modern astronomy: the overwhelming volume of supernova candidates generated by large-scale surveys like the Zwicky Transient Facility (ZTF). By automating the classification process, Rehemtulla’s tool enables faster identification of rare and scientifically valuable events, accelerating the pace of discovery. Though early in his career, his work has already garnered attention, with his flagship paper accumulating 5 citations. Rehemtulla’s contributions are pivotal for the future of transient science, promising to transform how we handle the data deluge from next-generation sky surveys and deepen our understanding of stellar explosions.
Research Focus
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Top Papers
- 1