DOI: 10.5281/zenodo.14671209
title: Data for GWtuna: Trawling through the data to find Gravitational Waves with Optuna and Jax

description: The output files in this Zenodo repository were used to generate the plots shown in the GWtuna paper. GWtuna is a fast gravitational-wave low-latency search prototype built on Optuna (optimisation software library) and JAX (accelerator-orientated array computation library), see paper for more information. 

1) GWtunaLambdaEtaO4TPESampler1000CmaEsampler900050ipopincpopsize2Callback500FinalFailed.csv - This output file contains the failed injections of GWtuna using the following settings. GWtuna was searching the lambda, eta and spin parameter space, using a O4 PSD. TPE sampler had at maximum of 1000 iterations (if the stopping algorithm was not called) to identify a gravitational wave signal. CmaEsampler with no learning rate adaptation had 9000 iterations to recover the parameters of the gravitational-wave event using a restart stratergy of increasing the population (called 'ipop' in Optuna), a population size of 50 (called 'popsize' in Optuna), and a multiplier (called 'inc_popsize' in Optuna) increasing the population size by 2 during each restart. TPE had 500 iterations before a Callback function (i.e. the stopping algorithm) would curtial the the serach if there was no gravitational wave in the data. 

2) GWtunaMchirpEtaO4TPESampler1000CmaEsampler9000LR50ipopincpopsize2Callback500FinalFailed.csv - This output file contains the failed injections of GWtuna using the following settings. GWtuna was searching the mchirp, eta and spin parameter space, using a O4 PSD. TPE sampler had at maximum of 1000 iterations (if the stopping algorithm was not called) to identify a gravitational wave signal. CmaEsampler with learning rate adaptation had 9000 iterations to recover the parameters of the gravitational-wave event using a restart stratergy of increasing the population (called 'ipop' in Optuna), a population size of 50 (called 'popsize' in Optuna), and a multiplier (called 'inc_popsize' in Optuna) increasing the population size by 2 during each restart. TPE had 500 iterations before a Callback function (i.e. the stopping algorithm) would curtial the the serach if there was no gravitational wave in the data. 

3) GWtunaLambdaEtaO4TPESampler1000CmaEsampler9000LR50ipopincpopsize2Callback500FinalFailed.csv - This output file contains the failed injections of GWtuna using the following settings. GWtuna was searching the lambda, eta and spin parameter space, using a O4 PSD. TPE sampler had at maximum of 1000 iterations (if the stopping algorithm was not called) to identify a gravitational wave signal. CmaEsampler with learning rate adaptation had 9000 iterations to recover the parameters of the gravitational-wave event using a restart stratergy of increasing the population (called 'ipop' in Optuna), a population size of 50 (called 'popsize' in Optuna), and a multiplier (called 'inc_popsize' in Optuna) increasing the population size by 2 during each restart. TPE had 500 iterations before a Callback function (i.e. the stopping algorithm) would curtial the the serach if there was no gravitational wave in the data. 

4) GWtunaLambdaEtaO4TPESampler1000CmaEsampler900050ipopincpopsize2Callback500FinalRecoveredSNR.csv - This output file contains the sucessful injections of GWtuna using the following settings. GWtuna was searching the lambda, eta and spin parameter space, using a O4 PSD. TPE sampler had at maximum of 1000 iterations (if the stopping algorithm was not called) to identify a gravitational wave signal. CmaEsampler with no learning rate adaptation had 9000 iterations to recover the parameters of the gravitational-wave event using a restart stratergy of increasing the population (called 'ipop' in Optuna), a population size of 50 (called 'popsize' in Optuna), and a multiplier (called 'inc_popsize' in Optuna) increasing the population size by 2 during each restart. TPE had 500 iterations before a Callback function (i.e. the stopping algorithm) would curtial the the serach if there was no gravitational wave in the data. 

5) GWtunaMassSpinO4TPESampler1000CmaEsampler9000LR50ipopincpopsize2Callback500FinalFailed.csv - This output file contains the failed injections of GWtuna using the following settings. GWtuna was searching the mass1, mass2 and spin parameter space, using a O4 PSD. TPE sampler had at maximum of 1000 iterations (if the stopping algorithm was not called) to identify a gravitational wave signal. CmaEsampler with learning rate adaptation had 9000 iterations to recover the parameters of the gravitational-wave event using a restart stratergy of increasing the population (called 'ipop' in Optuna), a population size of 50 (called 'popsize' in Optuna), and a multiplier (called 'inc_popsize' in Optuna) increasing the population size by 2 during each restart. TPE had 500 iterations before a Callback function (i.e. the stopping algorithm) would curtial the the serach if there was no gravitational wave in the data. 

6) GWtunaLambdaEtaO4TPESampler1000CmaEsampler9000LR50ipopincpopsize2Callback500FinalRecoveredSNR.csv - This output file contains the sucessful injections of GWtuna using the following settings. GWtuna was searching the lambda, eta and spin parameter space, using a O4 PSD. TPE sampler had at maximum of 1000 iterations (if the stopping algorithm was not called) to identify a gravitational wave signal. CmaEsampler with learning rate adaptation had 9000 iterations to recover the parameters of the gravitational-wave event using a restart stratergy of increasing the population (called 'ipop' in Optuna), a population size of 50 (called 'popsize' in Optuna), and a multiplier (called 'inc_popsize' in Optuna) increasing the population size by 2 during each restart. TPE had 500 iterations before a Callback function (i.e. the stopping algorithm) would curtial the the serach if there was no gravitational wave in the data. 

7) GWtunaMchirpEtaO4TPESampler1000CmaEsampler9000LR50ipopincpopsize2Callback500FinalRecoveredSNR.csv - This output file contains the sucessful injections of GWtuna using the following settings. GWtuna was searching the mchirp, eta and spin parameter space, using a O4 PSD. TPE sampler had at maximum of 1000 iterations (if the stopping algorithm was not called) to identify a gravitational wave signal. CmaEsampler with learning rate adaptation had 9000 iterations to recover the parameters of the gravitational-wave event using a restart stratergy of increasing the population (called 'ipop' in Optuna), a population size of 50 (called 'popsize' in Optuna), and a multiplier (called 'inc_popsize' in Optuna) increasing the population size by 2 during each restart. TPE had 500 iterations before a Callback function (i.e. the stopping algorithm) would curtial the the serach if there was no gravitational wave in the data. 

8) GWtunaMassSpinO4TPESampler1000CmaEsampler9000LR50ipopincpopsize2Callback500FinalRecoveredSNR.csv - This output file contains the sucessful injections of GWtuna using the following settings. GWtuna was searching the mass1, mass2 and spin parameter space, using a O4 PSD. TPE sampler had at maximum of 1000 iterations (if the stopping algorithm was not called) to identify a gravitational wave signal. CmaEsampler with learning rate adaptation had 9000 iterations to recover the parameters of the gravitational-wave event using a restart stratergy of increasing the population (called 'ipop' in Optuna), a population size of 50 (called 'popsize' in Optuna), and a multiplier (called 'inc_popsize' in Optuna) increasing the population size by 2 during each restart. TPE had 500 iterations before a Callback function (i.e. the stopping algorithm) would curtial the the serach if there was no gravitational wave in the data. 

9) GWtunaLambdaEtaO4TPESampler1000CmaEsampler9000LR50bipopincpopsize2Callback500FinalRecoveredSNR.csv - This output file contains the sucessful injections of GWtuna using the following settings. GWtuna was searching the lambda, eta and spin parameter space, using a O4 PSD. TPE sampler had at maximum of 1000 iterations (if the stopping algorithm was not called) to identify a gravitational wave signal. CmaEsampler with learning rate adaptation had 9000 iterations to recover the parameters of the gravitational-wave event using a restart stratergy of increasing/decreasing the population (called 'bipop' in Optuna), a population size of 50 (called 'popsize' in Optuna), and a multiplier (called 'inc_popsize' in Optuna) increasing the population size by 2 during each restart. TPE had 500 iterations before a Callback function (i.e. the stopping algorithm) would curtial the the serach if there was no gravitational wave in the data. 

10) GWtunaLambdaEtaO4TPESampler1000CmaEsampler9000LR50bipopincpopsize2Callback500FinalFailed.csv - This output file contains the failed injections of GWtuna using the following settings. GWtuna was searching the lambda, eta and spin parameter space, using a O4 PSD. TPE sampler had at maximum of 1000 iterations (if the stopping algorithm was not called) to identify a gravitational wave signal. CmaEsampler with learning rate adaptation had 9000 iterations to recover the parameters of the gravitational-wave event using a restart stratergy of increasing/decreasing the population (called 'bipop' in Optuna), a population size of 50 (called 'popsize' in Optuna), and a multiplier (called 'inc_popsize' in Optuna) increasing the population size by 2 during each restart. TPE had 500 iterations before a Callback function (i.e. the stopping algorithm) would curtial the the serach if there was no gravitational wave in the data.