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Author(s)

NameInstitutionMail Address
Luca ReiINFN Sezione diGenovaluca.rei@ligo.org

How to Obtain Support

Mailluca.rei@ligo.org

General Information

ML/DL TechnologiesLSTM
Science FieldsGeneral Relativity
DifficultyLow
LanguageEnglish
Typefully annotated / runnable / external resource / ...

Software and Tools

Programming LanguagePython
ML ToolsetKeras + Tensorflow
Suggested Environmentsbare Linux Node

Needed datasets

Data CreatorVirgo/Ligo
Data Typereal acquisition
Data Size1 GB
Data SourceIGWN 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)

Annotated Description

References

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