Author(s)
| Name | Institution | Mail Address | |
|---|---|---|---|
| Luca Rei | INFN Sezione diGenova | luca.rei@ligo.org |
How to Obtain Support
| luca.rei@ligo.org |
General Information
| ML/DL Technologies | LSTM |
|---|---|
| Science Fields | General Relativity |
| Difficulty | Low |
| Language | English |
| Type | fully annotated / runnable / external resource / ... |
Software and Tools
| Programming Language | Python |
|---|---|
| ML Toolset | Keras + Tensorflow |
| Suggested Environments | bare Linux Node |
Needed datasets
| Data Creator | Virgo/Ligo |
|---|---|
| Data Type | real acquisition |
| Data Size | 1 GB |
| Data Source | IGWN collaboration |
Short Description of the Use Case
simple example on how use an autoenconder to efficient data codings and foldings
"An autoencoder is a type of artificial neural network used to learn efficient data codings in an unsupervised manner. The aim of an autoencoder is to learn a representation (encoding) for a set of data, typically for dimensionality reduction, by training the network to ignore signal “noise”."
In the following we will use the autoencoder to analyse a gravitation wave format file and learn how to ignore some sources of noise, obtain a signal cleaned and size reductioned
All data files used for this exercise are public and can be obtained from the Ligo website at https://www.gw-openscience.org/archive/O2_16KHZ_R1/ in the gwf format or hdf5 format
How to execute it
Download data files and execute the Jupyter notebook (https://github.com/luca-rei/ml-genoa)