Commit 35f26af3 authored by Yifan Wang's avatar Yifan Wang
Browse files

Update README.md

parent 68378001
......@@ -6,11 +6,11 @@ First of all, welcome Simran to our group! This is the Max Planck Institute for
## Brief Introduction on the research project
Gravitational wave astronomy has brought the observational astronomy into a new era. So far over 50 gravitaitonal-wave events have been recorded by [LIGO](https://www.ligo.org) (Laser Interferometer Gravitational-wave Observatory) and [Virgo](https://www.virgo-gw.eu) detectors, all are from mergers of black holes or neutron stars. The discover has lead to significant scientific progress, e.g, testing the validity of general relativity in strong gravity regime, infer the population synthesis of black holes and neutron stars, and so on.
Gravitational wave astronomy has brought observational astronomy into a new era. So far over 50 gravitaitonal-wave events have been recorded by [LIGO](https://www.ligo.org) (Laser Interferometer Gravitational-wave Observatory) and [Virgo](https://www.virgo-gw.eu) detectors, all are from mergers of black holes or neutron stars. The discover has lead to significant scientific progress, e.g, testing the validity of general relativity in strong gravity regime, inferring the population synthesis of black holes and neutron stars, and so on.
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 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. 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 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 !
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.
......@@ -30,7 +30,7 @@ Even furthere goals:
## Skills:
During this summer research projects, you're going to develop the ability of doing research, which includes investigating where the cutting-edge of a certain research field is by reading journal literature, using programming to reproduce some reseults, and developing your own idea to explore new directions, and so on. Other soft skills are also useful, like discussing with people, using git to organize your programming codes and using other softwares to organized the journal literature, using latex to write scienctific report or even papers, and so on.
During this summer research projects, you're going to develop the ability of doing research, which includes investigating where the cutting-edge of a certain research field is by reading journal literature, using programming to reproduce some reseults, and developing your own idea to explore new directions, and so on. Other soft skills are also useful, like discussing with people, using git to organize your programming codes and using other softwares to organize the journal literature, using latex to write scienctific reports or even papers, and so on.
Hope you'll enjoy!!
......
Supports Markdown
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment