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Found 3 papers, 2 papers with code
What's the Difference? The potential for Convolutional Neural Networks for transient detection without template subtraction
We present a study of the potential for Convolutional Neural Networks (CNNs) to enable separation of astrophysical transients from image artifacts, a task known as "real-bogus" classification without requiring a template subtracted (or difference) image which requires a computationally expensive process to generate, involving image matching on small spatial scales in large volumes of data.
Enabling real-time multi-messenger astrophysics discoveries with deep learning
Multi-messenger astrophysics is a fast-growing, interdisciplinary field that combines data, which vary in volume and speed of data processing, from many different instruments that probe the Universe using different cosmic messengers: electromagnetic waves, cosmic rays, gravitational waves and neutrinos.
Optimal Classification and Outlier Detection for Stripped-Envelope Core-Collapse Supernovae
In the current era of time-domain astronomy, it is increasingly important to have rigorous, data driven models for classifying transients, including supernovae.
Solar and Stellar Astrophysics High Energy Astrophysical Phenomena
