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Full-Text Articles in Physics
Analyzing The On Source Window Of Supernova Sn2019ejj With A Multi Layered Signal Enhancement Algorithm With Coherent Waveburst And A Convolutional Neural Network, Michael Gale Benjamin
Analyzing The On Source Window Of Supernova Sn2019ejj With A Multi Layered Signal Enhancement Algorithm With Coherent Waveburst And A Convolutional Neural Network, Michael Gale Benjamin
Theses and Dissertations
Core collapse supernovae (CCSN) are highly anticipated sources of gravitational waves during the fourth observation run (O4). CCSN signals are weak and unmodeled and the rate of occurrence in our galaxy is very low. Because of this, they provide a greater challenge to detect than previously detected GW sources. CCSN simulations are used to test the detection pipeline in the event a CCSN is detected. CCSN GW signals are often indistinguishable from the noise sources present in GW data. We present a multi layered signal enhancement pipeline which we have applied Machine Learning (ML) techniques. We have used a Convolutional …
Glitch Subtraction From Gravitational Wave Data Using Adaptive Spline Fitting, Soumya D. Mohanty, Mohammad A. T. Chowdhury
Glitch Subtraction From Gravitational Wave Data Using Adaptive Spline Fitting, Soumya D. Mohanty, Mohammad A. T. Chowdhury
Physics and Astronomy Faculty Publications and Presentations
Transient signals of instrumental and environmental origins ("glitches") in gravitational wave data elevate the false alarm rate of searches for astrophysical signals and reduce their sensitivity. Glitches that directly overlap astrophysical signals hinder their detection and worsen parameter estimation errors. As the fraction of data occupied by detectable astrophysical signals will be higher in next generation detectors, such problematic overlaps could become more frequent. These adverse effects of glitches can be mitigated by estimating and subtracting them out from the data, but their unpredictable waveforms and large morphological diversity pose a challenge. Subtraction of glitches using data from auxiliary sensors …