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# Research for summer intern student

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Brief description: 

The aim of this undergraduate project is to reproduce 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 computing the false alarm rate for a machine learning approach for searching for gravitational wave. This project will take ~2 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.

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Goals: 

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- [ ] Reproduce the results of a machine learning paper to find gravitational wave events, e.g. 
    - https://arxiv.org/abs/1909.13442 Gravitational wave signal recognition of O1 data by deep learning, He Want et al.
    - https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.120.141103 Matching Matched Filtering with Deep Networks for Gravitational-Wave Astronomy
    - https://arxiv.org/abs/1909.06296 Bayesian parameter estimation using conditional variational autoencoders for gravitational-wave astronomy
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- [ ] compute the p value/ false alarm rate for machine learning algorithm 
- [ ] extend the above paper to O2/O3a
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References:

 - A survey for literature: https://iphysresearch.github.io/Survey4GWML/