5 papers
How Low Can We Go? Minimum Spectroscopic Requirements For Supernova Subtype Classification
Willow Fox Fortino, Federica B. Bianco, Maryam Modjaz +2
Millions of supernovae will be discovered with the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). As a result, spectrographs around the world will have to make d…
On the performance of pre-trained vision transformers for supernova spectral classification using different spectral representations
J. Serrano Bell, P. Gálvez Molina, V. Contreras Rojas +4
The spectroscopic classification of supernovae is a key component of time-domain astronomy and plays an important role in the identification of Type Ia events. The increasing volum…
Microlensify: a Transformer Based Machine Learning Classifier for Microlensing Events Trained on TESS Light Curves
Atousa Kalantari, Somayeh Khakpash, Sedighe Sajadian +3
Microlensing can reveal populations of faint compact objects that are otherwise difficult to detect. Depending on their design, all-sky surveys have the potential to search for the…
Multi-filter UV to NIR Data-driven Light Curve Templates for Stripped Envelope Supernovae
Somayeh Khakpash, Federica B. Bianco, Maryam Modjaz +4
While the spectroscopic classification scheme for Stripped envelope supernovae (SESNe) is clear, and we know that they originate from massive stars that lost some or all their enve…
Reducing ground-based astrometric errors with Gaia and Gaussian processes
W. F. Fortino, G. M. Bernstein, P. H. Bernardinelli +64
Stochastic field distortions caused by atmospheric turbulence are a fundamental limitation to the astrometric accuracy of ground-based imaging. This distortion field is measurable…