94 citations · 154 across the 3 of their papers we have counts for
11 papers
Discriminative Dimensionality Reduction using Deep Neural Networks for Clustering of LIGO Data
Sara Bahaadini, Yunan Wu, Scott Coughlin +2
In this paper, leveraging the capabilities of neural networks for modeling the non-linearities that exist in the data, we propose several models that can project data into a low di…
The role of core-collapse physics in the observability of black-hole neutron-star mergers as multi-messenger sources
Jaime Román-Garza, Simone S. Bavera, Tassos Fragos +10
Recent detailed 1D core-collapse simulations have brought new insights on the final fate of massive stars, which are in contrast to commonly used parametric prescriptions. In this…
The impact of mass-transfer physics on the observable properties of field binary black hole populations
Simone S. Bavera, Tassos Fragos, Michael Zevin +13
We study the impact of mass-transfer physics on the observable properties of binary black hole populations formed through isolated binary evolution. We investigate the impact of ma…
Scalable Variational Gaussian Processes for Crowdsourcing: Glitch Detection in LIGO
Pablo Morales-Álvarez, Pablo Ruiz, Scott Coughlin +2
In the last years, crowdsourcing is transforming the way classification training sets are obtained. Instead of relying on a single expert annotator, crowdsourcing shares the labell…
COSMIC Variance in Binary Population Synthesis
Katelyn Breivik, Scott Coughlin, Michael Zevin +8
The formation and evolution of binary stars is a critical component of several fields in astronomy. The most numerous sources for gravitational wave observatories are inspiraling a…
Can Neutron-Star Mergers Explain the r-process Enrichment in Globular Clusters?
Michael Zevin, Kyle Kremer, Daniel M. Siegel +4
Star-to-star dispersion of r-process elements has been observed in a significant number of old, metal-poor globular clusters. We investigate early-time neutron-star mergers as the…