Data for GWtuna: Trawling through the data to find Gravitational Waves with Optuna and Jax

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.