19 citations · 24 across the 10 of their papers we have counts for
11 papers
Nearby Supernova Science in the Era of High Cadence, Deep Time Domain Surveys
David J. Sand, Bhagya Subrayan, Conor L. Ransome +41
We discuss science opportunities for nearby supernovae (SNe) in the era of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), emphasizing the youngest phases o…
NAPTIME: A Neural-Process Framework for Rubin Alert Classification
Nicholas Earl, Siddharth Chaini, K. Decker French +3
The Vera C. Rubin Observatory Legacy Survey of Space and Time will produce a high-volume stream of irregularly sampled multiband alerts for which spectroscopic confirmation will be…
Toward decision-aware AI for LSST-scale time-domain astronomy
C. R. Bom, A. Mahabal, F. Bianco +27
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will generate approximately (10^7) alerts per night, pushing time-domain astronomy beyond pipelines that trea…
Probabilistic Data-Driven Modelling of Astrophysical Transients: The Neural Process Family for Ultrafast and Class-Agnostic Light Curve Reconstruction with NightLANP
Siddharth Chaini, Federica B. Bianco, Ashish Mahabal
Astrophysical observations from Earth are subject to weather, environmental, and scientific constraints that lead to sparse, irregular light curves. On the eve of the Vera C. Rubin…
Searching for Ultracool Dwarfs in Early LSST Data Products
Easton J. Honaker, John E. Gizis, Christian Aganze +8
The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) promises to drastically accelerate the discovery of ultracool dwarfs (UCDs) over the course of its 10-year su…
In Search of the Unknown Unknowns: A Multi-Metric Distance Ensemble for Out of Distribution Anomaly Detection in Astronomical Surveys
Siddharth Chaini, Federica B. Bianco, Ashish Mahabal
Distance-based methods involve the computation of distance values between features and are a well-established paradigm in machine learning. In anomaly detection, anomalies are iden…
Validation of the Bond et al. (2010) SDSS-derived kinematic models for the Milky Way's disk and halo stars with Gaia Data Release 3 proper motion and radial velocity data
Bruno Domínguez, Siddharth Chaini, Karlo Mrakovčić +2
We validate the Bond et al. (2010) kinematic models for the Milky Way's disk and halo stars with Gaia Data Release 3 data. Bond et al. constructed models for stellar velocity distr…
Light Curve Classification with DistClassiPy: a new distance-based classifier
Siddharth Chaini, Ashish Mahabal, Ajit Kembhavi +1
The rise of synoptic sky surveys has ushered in an era of big data in time-domain astronomy, making data science and machine learning essential tools for studying celestial objects…
Photometric identification of compact galaxies, stars and quasars using multiple neural networks
Siddharth Chaini, Atharva Bagul, Anish Deshpande +4
We present MargNet, a deep learning-based classifier for identifying stars, quasars and compact galaxies using photometric parameters and images from the Sloan Digital Sky Survey (…
Thermodynamic Properties of Ice: A Monte Carlo Study
Tanmay Bhore, Siddharth Chaini, Siddharth Bachoti +2
In this text, we implement a monte carlo algorithm to study thermodynamic properties of ice. Our program, written in Python, is open-sourced and available at https://github.com/AKn…
Astronomical Classification of Light Curves with an Ensemble of Gated Recurrent Units
Siddharth Chaini, Soumya Sanjay Kumar
With an ever-increasing amount of astronomical data being collected, manual classification has become obsolete; and machine learning is the only way forward. Keeping this in mind,…