@@ -12,7 +12,7 @@ Gravitational wave astronomy has brought observational astronomy into a new era.
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@@ -12,7 +12,7 @@ Gravitational wave astronomy has brought observational astronomy into a new era.
A classcial method to identify gravitational-wave signals from CBC in the data segment is match filtering. Since it is in priori unknown what kinds of gravitaional waves from which masses of black holes exist in the data, people just build a huge number of precomputed waveforms covering different masses and spins of black holes as much as possible, and then correlate the waveforms with data to produce the signal-to-noise ratio (SNR). If the SNR exceeds a certain threshold, it's a hint for signals. Using time sliding techniques between two detectors, the SNR distribution for background noise can also be produced. The p-value of a signal candidate with respect to the background noise represents how rare this kind of signal can happen, or in another words, the false alarm rate. The first gravitaional-wave event, GW150914, was identified with a very significant false alarm rate, lower than one false alarm every 203000 years.
A classcial method to identify gravitational-wave signals from CBC in the data segment is match filtering. Since it is in priori unknown what kinds of gravitaional waves from which masses of black holes exist in the data, people just build a huge number of precomputed waveforms covering different masses and spins of black holes as much as possible, and then correlate the waveforms with data to produce the signal-to-noise ratio (SNR). If the SNR exceeds a certain threshold, it's a hint for signals. Using time sliding techniques between two detectors, the SNR distribution for background noise can also be produced. The p-value of a signal candidate with respect to the background noise represents how rare this kind of signal can happen, or in another words, the false alarm rate. The first gravitaional-wave event, GW150914, was identified with a very significant false alarm rate, lower than one false alarm every 203000 years.
In recent few years, a new technique based on machine learning / deep learning is looking for alternative way to search for and analyze gravitational waves. Some results are remarkable, for example, the classcial match filtering technique can be very computational expensive (takes 10k cpus to run weeks), while the machine learning methods take much less computation time becuase it can better capture the intrinsic pattern of data. This new emerging field is promising, however, since this is a young field it needs more exploration. Can machine learning perform better than classcial match filtering ? In what situation it performs better, and in which it's worse? What is the best structure for designing a deep learning network, and why? Questions like these are interesting and waiting for us to explore !
In recent few years, a new technique based on machine learning / deep learning is looking for alternative way to search for and analyze gravitational waves. Some results are remarkable, for example, the classcial match filtering technique can be very computational expensive (takes 10k cpus to run weeks), while the machine learning methods take much less computation time becuase it can better capture the intrinsic pattern of data. This new emerging field is promising, however, since this is a young field there's still plenty of scope for improvement. Can machine learning perform better than classcial match filtering ? In what situation it performs better, and in which it's worse? What is the best structure for designing a deep learning network, and why? Questions like these are interesting and waiting for us to explore !
The aim of this undergraduate project is to reproduce and improve the results of some journal literature using machine learning techniques to identify gravitational wave signals from noisy data. Depending on the progress, the student can perform some further exploration after reproducing the results, for example, searching for real data from the LIGO and Virgo, computing the false alarm rate for a machine learning approach for searching for gravitational wave. This project will take ~3 months, the student should write a brief report at the end. Some prerequisite knowledge on general relativity, gravitational waves, python programming and machine learning would be helpful.
The aim of this undergraduate project is to reproduce and improve the results of some journal literature using machine learning techniques to identify gravitational wave signals from noisy data. Depending on the progress, the student can perform some further exploration after reproducing the results, for example, searching for real data from the LIGO and Virgo, computing the false alarm rate for a machine learning approach for searching for gravitational wave. This project will take ~3 months, the student should write a brief report at the end. Some prerequisite knowledge on general relativity, gravitational waves, python programming and machine learning would be helpful.
