LIGO GW150914 Data Release & Tutorial

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The LIGO Open Science Center has released data from the GW150914 gravitational wave detection, accompanied by a tutorial on signal processing tasks related to the strain time-series data. The tutorial is designed for those familiar with Python and digital signal processing concepts, covering topics like power spectral densities and digital filtering. Users are encouraged to download the accompanying ipython notebook for hands-on exploration, noting the necessity of specific Python packages and version compatibility. Some participants have shared their experiences with previous data releases, highlighting improvements in the current tutorial's accessibility and instructional quality. Overall, the release aims to provide both an educational and engaging experience for those interested in gravitational wave data analysis.
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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.
 
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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In an inertial frame of reference (IFR), there are two fixed points, A and B, which share an entangled state $$ \frac{1}{\sqrt{2}}(|0>_A|1>_B+|1>_A|0>_B) $$ At point A, a measurement is made. The state then collapses to $$ |a>_A|b>_B, \{a,b\}=\{0,1\} $$ We assume that A has the state ##|a>_A## and B has ##|b>_B## simultaneously, i.e., when their synchronized clocks both read time T However, in other inertial frames, due to the relativity of simultaneity, the moment when B has ##|b>_B##...

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