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Dataset for LearningMatch: Siamese Neural Network Learns the Match Manifold

Authors: Susanna, Green;

Dataset for LearningMatch: Siamese Neural Network Learns the Match Manifold

Abstract

A Siamese neural network (called LearningMatch) has learned the mapping between the parameters, specifically λ0 (which is proportional to the chirp mass), η (symmetric mass ratio), and equal aligned spin (χ1 = χ2), of two gravitational-wave templates and the match. This respoitory contains the training data required to train the LearningMatch model presented in the paper. 1000000LambdaEtaAlignedSpinTrainingDataset+1500000DiffusedLambdaEtaAlignedSpinTrainingDataset.csv - This is the training dataset that was used to train the LearningMatch model presenetd in the paper. 100000 datapoints were created uisng the normal method and 150000 datapoints were created using the diffused dataset (see paper for more information). 100000LambdaEtaAlignedSpinValidationDataset+150000DiffusedLambdaEtaAlignedSpinValidationDataset.csv - This is the validation dataset that was used to train the LearningMatch model presenetd in the paper. 100000 datapoints were created uisng the normal method and 150000 datapoints were created using the diffused dataset (see paper for more information). Figure 8 & 9 showcased the accuracy of the LearningMatch model and this was the dataset that was used to test the model: 100000LambdaEtaAlignedSpinTestDataset+150000DiffusedLambdaEtaAlignedSpinTestDataset.csv - This is the test dataset that was used to test the LearningMatch model presenetd in the paper. 100000 datapoints were created uisng the normal method and 150000 datapoints were created using the diffused dataset (see paper for more information). Figure 3 showcased that large datasets are required for LearningMatch to learn accurately, and these are the datasets used to create this figure: 10000LambdaEtaAlignedSpinTrainingDataset+15000DiffusedLambdaEtaAlignedSpinTrainingDataset.csv - This is the training dataset that was used to train the LearningMatch model presenetd in the paper. 10000 datapoints were created uisng the normal method and 15000 datapoints were created using the diffused dataset. 1000LambdaEtaAlignedSpinValidationDataset+1500DiffusedLambdaEtaAlignedSpinValidationDataset.csv - This is the validation dataset that was used to train the LearningMatch model presenetd in the paper. 1000 datapoints were created uisng the normal method and 1500 datapoints were created using the diffused dataset. 10000LambdaEtaAlignedSpinValidationDataset+15000DiffusedLambdaEtaAlignedSpinValidationDataset.csv - This is the validation dataset that was used to train the LearningMatch model presenetd in the paper. 10000 datapoints were created uisng the normal method and 15000 datapoints were created using the diffused dataset. 100000LambdaEtaAlignedSpinTrainingDataset+150000DiffusedLambdaEtaAlignedSpinTrainingDataset.csv - This is the training dataset that was used to train the LearningMatch model presenetd in the paper. 100000 datapoints were created uisng the normal method and 150000 datapoints were created using the diffused dataset.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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