300 citations · 1.2k across the 17 of their papers we have counts for
19 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…
Active Learning for Computationally Efficient Distribution of Binary Evolution Simulations
Kyle Akira Rocha, Jeff J. Andrews, Christopher P. L. Berry +13
Binary stars undergo a variety of interactions and evolutionary phases, critical for predicting and explaining observed properties. Binary population synthesis with full stellar-st…
POSYDON: A General-Purpose Population Synthesis Code with Detailed Binary-Evolution Simulations
Tassos Fragos, Jeff J. Andrews, Simone S. Bavera +22
Most massive stars are members of a binary or a higher-order stellar systems, where the presence of a binary companion can decisively alter their evolution via binary interactions.…
Probing the progenitors of spinning binary black-hole mergers with long gamma-ray bursts
Simone S. Bavera, Tassos Fragos, Emmanouil Zapartas +15
Long-duration gamma-ray bursts are thought to be associated with the core-collapse of massive, rapidly spinning stars and the formation of black holes. However, efficient angular m…
Revisiting the explodability of single massive star progenitors of stripped-envelope supernovae
E. Zapartas, M. Renzo, T. Fragos +12
Stripped-envelope supernovae (Types IIb, Ib, and Ic) that show little or no hydrogen comprise roughly one-third of the observed explosions of massive stars. Their origin and the ev…
Modeling Dense Star Clusters in the Milky Way and Beyond with the Cluster Monte Carlo Code
Carl L. Rodriguez, Newlin C. Weatherford, Scott C. Coughlin +10
We describe the public release of the Cluster Monte Carlo Code (CMC) a parallel, star-by-star -body code for modeling dense star clusters. CMC treats collisional stellar dynamic…
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…
The Fate of Binaries in the Galactic Center: The Mundane and the Exotic
Alexander P. Stephan, Smadar Naoz, Andrea M. Ghez +6
The Galactic Center (GC) is dominated by the gravity of a super-massive black hole (SMBH), Sagittarius A, and is suspected to contain a sizable population of binary stars. Such…
DIRECT: Deep Discriminative Embedding for Clustering of LIGO Data
Sara Bahaadini, Vahid Noroozi, Neda Rohani +3
In this paper, benefiting from the strong ability of deep neural network in estimating non-linear functions, we propose a discriminative embedding function to be used as a feature…
Improvements in Gravitational-Wave Sky Localization with Expanded Networks of Interferometers
Chris Pankow, Eve A. Chase, Scott Coughlin +2
A milestone of multi-messenger astronomy has been achieved with the detection of gravitational waves from a binary neutron star merger accompanied by observations of several associ…
Characterizing accreting double white dwarf binaries with the Laser Interferometer Space Antenna and Gaia
Katelyn Breivik, Kyle Kremer, Michael Bueno +3
We demonstrate a method to fully characterize mass-transferring double white dwarf (DWD) systems with a helium-rich (He) WD donor based on the mass--radius relationship for He WDs.…
Deep Multi-view Models for Glitch Classification
Sara Bahaadini, Neda Rohani, Scott Coughlin +3
Non-cosmic, non-Gaussian disturbances known as "glitches", show up in gravitational-wave data of the Advanced Laser Interferometer Gravitational-wave Observatory, or aLIGO. In this…
Astrophysical Prior Information and Gravitational-wave Parameter Estimation
Chris Pankow, Laura Sampson, Leah Perri +4
The detection of electromagnetic counterparts to gravitational waves has great promise for the investigation of many scientific questions. It has long been hoped that in addition t…
Systematic errors in low latency gravitational wave parameter estimation impact electromagnetic follow-up observations
Tyson B. Littenberg, Ben Farr, Scott Coughlin +1
Among the most eagerly anticipated opportunities made possible by Advanced LIGO/Virgo are multimessenger observations of compact mergers. Optical counterparts may be short-lived so…
Neutron stars versus black holes: probing the mass gap with LIGO/Virgo
Tyson B. Littenberg, Benjamin Farr, Scott Coughlin +2
Inspirals and mergers of black hole (BHs) and/or neutron star (NSs) binaries are expected to be abundant sources for ground-based gravitational-wave (GW) detectors. We assess the c…