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20022008
most citedThe Alloy Theoretic Automated Toolkit: A User Guide

1.7k citations

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11 papers · 1 filter

gr-qc200836 cited

Improved time-frequency analysis of extreme-mass-ratio inspiral signals in mock LISA data

Jonathan R Gair, Ilya Mandel, Linqing Wen

The planned Laser Interferometer Space Antenna (LISA) is expected to detect gravitational wave signals from ~100 extreme-mass-ratio inspirals (EMRIs) of stellar-mass compact object…

gr-qc200864 cited

A Bayesian approach to the follow-up of candidate gravitational wave signals

John Veitch, Alberto Vecchio

Ground-based gravitational wave laser interferometers (LIGO, GEO-600, Virgo and Tama-300) have now reached high sensitivity and duty cycle. We present a Bayesian evidence-based app…

gr-qc200763 cited

Search of S3 LIGO data for gravitational wave signals from spinning black hole and neutron star binary inspirals

The LIGO Scientific Collaboration, B. Abbott

We report on the methods and results of the first dedicated search for gravitational waves emitted during the inspiral of compact binaries with spinning component bodies. We analyz…

gr-qc200764 cited

Report on the second Mock LISA Data Challenge

Stanislav Babak, John G. Baker, Matthew J. Benacquista +40

The Mock LISA Data Challenges are a program to demonstrate LISA data-analysis capabilities and to encourage their development. Each round of challenges consists of several data set…

gr-qc200721 cited

Time-frequency analysis of extreme-mass-ratio inspiral signals in mock LISA data

Jonathan R Gair, Ilya Mandel, Linqing Wen

Extreme-mass-ratio inspirals (EMRIs) of ~ 1-10 solar-mass compact objects into ~ million solar-mass massive black holes can serve as excellent probes of strong-field general relati…

gr-qc200719 cited

Inference on inspiral signals using LISA MLDC data

Christian Röver, Alexander Stroeer, Ed Bloomer +11

In this paper we describe a Bayesian inference framework for analysis of data obtained by LISA. We set up a model for binary inspiral signals as defined for the Mock LISA Data Chal…