LIGO GW150914 Data Release & Tutorial

In summary, the LIGO Open Science Center has released data from the gravitational wave detection along with a tutorial going through some typical signal processing tasks on strain time-series data associated with it. This tutorial assumes that you know python well enough, and that you know a bit about signal processing of digital time series data (or want to learn!). This includes power spectral densities, spectrograms, digital filtering, whitening, audio manipulation. This is a vast and complex set of topics, but we will cover many of the basics in this tutorial. You will need the python packages: numpy, scipy, matplotlib, h5py. If you're interested in exploring the process yourself, I suggest downloading the ip
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websterling
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The[/PLAIN] LIGO Open Science Center has released data from the gravitational wave detection along with a tutorial going through some typical signal processing tasks on strain time-series data associated with it.

GW150914 Data Release


From the tutorial page-
This tutorial assumes that you know python well enough, and that you know a bit about signal processing of digital time series data (or want to learn!). This includes power spectral densities, spectrograms, digital filtering, whitening, audio manipulation. This is a vast and complex set of topics, but we will cover many of the basics in this tutorial. You will need the python packages: numpy, scipy, matplotlib, h5py.

Could be both fun and educational
 
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Greg Bernhardt said:
So have you played with it yet?

I haven't played with this, yet; maybe over the weekend.

I did play with some data from the 1st run- it required way more software to work with (mostly a pain to install and configure), the tutorial was less instructive and narrower in focus, and there were no events in the data.
 
  • #4
I finally had a chance to spend some time with this and have a few thoughts to share if anyone is still interested. The actual tutorial page, Signal Processing with GW150914 Open Data, is the output from an ipython notebook. If you're just interested in what sort of data processing goes into extracting the signal from the data, then just reading this page might be enough.

If you're actually interested in exploring the process yourself, I suggest downloading the ipython notebook file and working your way through it. You will need Python 2.7 (Python 3 won't work), ipython notebook, and the numpy, scipy, matplotlib, and h5py packages. You should be fairly familiar with these packages, as well as with digital signal processing, especially filtering. If not, then the code will be hard to follow, and easy to mangle. I'd recommend keeping a clean copy of the notebook file available.

Enjoy and maybe learn something!.
 
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FAQ: LIGO GW150914 Data Release & Tutorial

What is LIGO GW150914 Data Release & Tutorial?

LIGO GW150914 Data Release & Tutorial is a public release of data and a tutorial for analyzing the data from the LIGO (Laser Interferometer Gravitational-Wave Observatory) detection of the first gravitational wave signal from a binary black hole merger, which occurred on September 14, 2015.

Who can access the data and tutorial?

The data and tutorial are publicly available to anyone interested in learning about and analyzing the LIGO GW150914 data. There are no restrictions on who can access the data.

What is the purpose of the data release and tutorial?

The purpose of the data release and tutorial is to promote transparency and open access to scientific data, as well as to provide a resource for researchers and students to learn about and analyze the groundbreaking LIGO GW150914 detection.

What format is the data available in?

The data is available in the form of gravitational wave strain time series, which is the raw data collected by the LIGO detectors. The tutorial includes instructions on how to convert this data into a more user-friendly format for analysis.

What can the data be used for?

The data can be used for a variety of purposes, including studying the properties of gravitational waves, testing theories of gravity, and developing new techniques for detecting and analyzing gravitational waves. It can also be used for educational purposes to learn about the process of gravitational wave detection and analysis.

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