GlitchGAN generates class-conditioned time-domain glitches that pass Gravity Spy classification and show UMAP overlap with real samples while running at high speed.
Improving the Data Quality of Advanced LIGO Based on Early Engineering Run Results
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abstract
The Advanced Laser Interferometer Gravitational-wave Observatory (LIGO) detectors have completed their initial upgrade phase and will enter the first observing run in late 2015, with detector sensitivity expected to improve in future runs. Through the combined efforts of on-site commissioners and the Detector Characterization group of the LIGO Scientific Collaboration, interferometer performance, in terms of data quality, at both LIGO observatories has vastly improved from the start of commissioning efforts to present. Advanced LIGO has already surpassed Enhanced LIGO in sensitivity, and the rate of noise transients, which would negatively impact astrophysical searches, has improved. Here we give details of some of the work which has taken place to better the quality of the LIGO data ahead of the first observing run.
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Realistic Time-Domain Synthesis of Gravitational-Wave Detector Glitches using Class-Conditional Derivative Generative Adversarial Networks
GlitchGAN generates class-conditioned time-domain glitches that pass Gravity Spy classification and show UMAP overlap with real samples while running at high speed.