1 citations · 2 across the 6 of their papers we have counts for
9 papers
Discovery of over a thousand variable stars in Terzan 5 and Liller 1 with JWST
Kevin B. Burdge, Ilaria Caiazzo, Malina M. Desai +18
The Bulge Fossil Fragments Terzan 5 and Liller 1 host some of the densest stellar environments in the Galaxy, yet their variable star populations have remained largely unexplored d…
Discovery of the Infrared Counterpart of the Rapid Burster with JWST
Malina M. Desai, Kevin B. Burdge, Cristina Pallanca +8
The Rapid Burster (MXB 1730335) is the only known neutron star to produce both Type I and Type II X-ray bursts, making it a key system for understanding accretion physics onto n…
The Lightspeed project: high-speed, ultra-low read noise imaging and polarimetry for the Magellan telescopes
Christopher Layden, Kevin Burdge, John J. Piotrowski +12
Lightspeed will be an ultra-fast (> kHz), ultra-low read noise, multicolor (ugriz + IR) imager for the 6.5 meter Magellan Clay telescope. In a single-channel configuration, Lightsp…
Likelihood-free inference for gravitational-wave data analysis and public alerts
Ethan Marx, Deep Chatterjee, Malina Desai +7
Rapid and reliable detection and dissemination of source parameter estimation data products from gravitational-wave events, especially sky localization, is critical for maximizing…
Building Machine Learning Challenges for Anomaly Detection in Science
Elizabeth G. Campolongo, Yuan-Tang Chou, Ekaterina Govorkova +148
Scientific discoveries are often made by finding a pattern or object that was not predicted by the known rules of science. Oftentimes, these anomalous events or objects that do not…
A Neural Network-Based Search for Unmodeled Transients in LIGO-Virgo-KAGRA's Third Observing Run
Ryan Raikman, Eric A. Moreno, Katya Govorkova +13
This paper presents the results of a Neural Network (NN)-based search for short-duration gravitational-wave transients in data from the third observing run of LIGO, Virgo, and KAGR…
Kilonova Light Curve Parameter Estimation Using Likelihood-Free Inference
Malina Desai, Deep Chatterjee, Sahil Jhawar +3
Rapid parameter estimation is critical when dealing with short lived signals such as kilonovae. We present a parameter estimation algorithm that combines likelihood-free inference…
Rapid Likelihood Free Inference of Compact Binary Coalescences using Accelerated Hardware
Deep Chatterjee, Ethan Marx, William Benoit +12
We report a gravitational-wave parameter estimation algorithm, AMPLFI, based on likelihood-free inference using normalizing flows. The focus of AMPLFI is to perform real-time param…
Optimizing Likelihood-free Inference using Self-supervised Neural Symmetry Embeddings
Deep Chatterjee, Philip C. Harris, Maanas Goel +3
Likelihood-free inference is quickly emerging as a powerful tool to perform fast/effective parameter estimation. We demonstrate a technique of optimizing likelihood-free inference…