Publicação
Convolutional neural networks for the classification of glitches in gravitational-wave data streams
| Resumo: | We investigate the use of convolutional neural networks (including the modern ConvNeXt network family) to classify transient noise signals (i.e. glitches) and gravitational waves (GWs) in data from the Advanced LIGO detectors. First, we use models with a supervised learning approach, both trained from scratch using the Gravity Spy dataset and employing transfer learning by fine-tuning pre-trained models in this dataset. Second, we also explore a self-supervised approach, pre-training models with automatically generated pseudo-labels. Our findings are very close to existing results for the same dataset, reaching values for the F1 score of 97.18% (94.15%) for the best supervised (self-supervised) model. We further test the models using actual GW signals from LIGO-Virgo's O3 run. Although trained using data from previous runs (O1 and O2), the models show good performance, in particular when using transfer learning. We find that transfer learning improves the scores without the need for any training on real signals apart from the less than 50 chirp examples from hardware injections present in the Gravity Spy dataset. This motivates the use of transfer learning not only for glitch classification but also for signal classification. |
|---|---|
| Autores principais: | Fernandes, Tiago |
| Outros Autores: | Vieira, Samuel; Onofre, A.; Calderón Bustillo, Juan; Torres-Forné, Alejandro; Font, José A. |
| Assunto: | deep learning glitches gravitational waves machine learning |
| Ano: | 2023 |
| País: | Portugal |
| Tipo de documento: | artigo |
| Tipo de acesso: | acesso restrito |
| Instituição associada: | Universidade do Minho |
| Idioma: | inglês |
| Origem: | RepositóriUM - Universidade do Minho |
| _version_ | 1866270943256510464 |
|---|---|
| author | Fernandes, Tiago |
| author2 | Vieira, Samuel Onofre, A. Calderón Bustillo, Juan Torres-Forné, Alejandro Font, José A. |
| author2_role | author author author author author |
| author_facet | Fernandes, Tiago Vieira, Samuel Onofre, A. Calderón Bustillo, Juan Torres-Forné, Alejandro Font, José A. |
| author_role | author |
| contributor_name_str_mv | Universidade do Minho |
| country_str | PT |
| creators_json_txt | [{\"Person.name\":\"Fernandes, Tiago\"},{\"Person.name\":\"Vieira, Samuel\"},{\"Person.name\":\"Onofre, A.\"},{\"Person.name\":\"Calderón Bustillo, Juan\"},{\"Person.name\":\"Torres-Forné, Alejandro\"},{\"Person.name\":\"Font, José A.\"}] |
| datacite.contributors.contributor.contributorName.fl_str_mv | Universidade do Minho |
| datacite.creators.creator.creatorName.fl_str_mv | Fernandes, Tiago Vieira, Samuel Onofre, A. Calderón Bustillo, Juan Torres-Forné, Alejandro Font, José A. |
| datacite.date.Accepted.fl_str_mv | 2023-01-01T00:00:00Z |
| datacite.date.available.fl_str_mv | 2024-01-02T21:57:15Z |
| datacite.date.embargoed.fl_str_mv | 2024-01-02T21:57:15Z |
| datacite.rights.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| datacite.subjects.subject.fl_str_mv | deep learning glitches gravitational waves machine learning |
| datacite.titles.title.fl_str_mv | Convolutional neural networks for the classification of glitches in gravitational-wave data streams |
| dc.contributor.none.fl_str_mv | Universidade do Minho |
| dc.creator.none.fl_str_mv | Fernandes, Tiago Vieira, Samuel Onofre, A. Calderón Bustillo, Juan Torres-Forné, Alejandro Font, José A. |
| dc.date.Accepted.fl_str_mv | 2023-01-01T00:00:00Z |
| dc.date.available.fl_str_mv | 2024-01-02T21:57:15Z |
| dc.date.embargoed.fl_str_mv | 2024-01-02T21:57:15Z |
| dc.format.none.fl_str_mv | application/pdf application/pdf |
| dc.identifier.none.fl_str_mv | https://hdl.handle.net/1822/87778 |
| dc.language.none.fl_str_mv | eng |
| dc.publisher.none.fl_str_mv | IOP Publishing |
| dc.rights.none.fl_str_mv | http://purl.org/coar/access_right/c_16ec |
| dc.subject.none.fl_str_mv | deep learning glitches gravitational waves machine learning |
| dc.title.fl_str_mv | Convolutional neural networks for the classification of glitches in gravitational-wave data streams |
| dc.type.none.fl_str_mv | http://purl.org/coar/resource_type/c_6501 |
| description | We investigate the use of convolutional neural networks (including the modern ConvNeXt network family) to classify transient noise signals (i.e. glitches) and gravitational waves (GWs) in data from the Advanced LIGO detectors. First, we use models with a supervised learning approach, both trained from scratch using the Gravity Spy dataset and employing transfer learning by fine-tuning pre-trained models in this dataset. Second, we also explore a self-supervised approach, pre-training models with automatically generated pseudo-labels. Our findings are very close to existing results for the same dataset, reaching values for the F1 score of 97.18% (94.15%) for the best supervised (self-supervised) model. We further test the models using actual GW signals from LIGO-Virgo's O3 run. Although trained using data from previous runs (O1 and O2), the models show good performance, in particular when using transfer learning. We find that transfer learning improves the scores without the need for any training on real signals apart from the less than 50 chirp examples from hardware injections present in the Gravity Spy dataset. This motivates the use of transfer learning not only for glitch classification but also for signal classification. |
| dirty | 0 |
| eu_rights_str_mv | restrictedAccess |
| format | article |
| fulltext.url.fl_str_mv | https://prod-dspace.uminho.pt/bitstreams/fc7b6bc1-c006-41de-af2f-ae4c72773f35/download |
| id | rum_d2fcc8958a23cd9eb2b0d820069f1e21 |
| identifier.url.fl_str_mv | https://hdl.handle.net/1822/87778 |
| instacron_str | repositorium |
| institution | Universidade do Minho |
| instname_str | Universidade do Minho |
| language | eng |
| network_acronym_str | rum |
| network_name_str | RepositóriUM - Universidade do Minho |
| oai_identifier_str | oai:repositorium.uminho.pt:1822/87778 |
| organization_str_mv | urn:organizationAcronym:repositorium |
| person_str_mv | Fernandes, Tiago Vieira, Samuel Onofre, A. Calderón Bustillo, Juan Torres-Forné, Alejandro Font, José A. |
| publishDate | 2023 |
| publisher.none.fl_str_mv | IOP Publishing |
| reponame_str | RepositóriUM - Universidade do Minho |
| repository_id_str | urn:repositoryAcronym:rum |
| service_str_mv | urn:repositoryAcronym:rum |
| spelling | engIOP PublishingporWe investigate the use of convolutional neural networks (including the modern ConvNeXt network family) to classify transient noise signals (i.e. glitches) and gravitational waves (GWs) in data from the Advanced LIGO detectors. First, we use models with a supervised learning approach, both trained from scratch using the Gravity Spy dataset and employing transfer learning by fine-tuning pre-trained models in this dataset. Second, we also explore a self-supervised approach, pre-training models with automatically generated pseudo-labels. Our findings are very close to existing results for the same dataset, reaching values for the F1 score of 97.18% (94.15%) for the best supervised (self-supervised) model. We further test the models using actual GW signals from LIGO-Virgo's O3 run. Although trained using data from previous runs (O1 and O2), the models show good performance, in particular when using transfer learning. We find that transfer learning improves the scores without the need for any training on real signals apart from the less than 50 chirp examples from hardware injections present in the Gravity Spy dataset. This motivates the use of transfer learning not only for glitch classification but also for signal classification.application/pdfapplication/pdfporConvolutional neural networks for the classification of glitches in gravitational-wave data streamsFernandes, TiagoVieira, SamuelOnofre, A.Calderón Bustillo, JuanTorres-Forné, AlejandroFont, José A.HostingInstitutionOrganizationalUniversidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptISSNIsPartOf0264-9381DOIIsPartOf10.1088/1361-6382/acf26c2024-01-02T21:57:15Z20232023-01-01T00:00:00ZHandlehttps://hdl.handle.net/1822/87778http://purl.org/coar/access_right/c_16ecrestricted accessdeep learningglitchesgravitational wavesmachine learning1724761 bytes2078004 bytesliteraturehttp://purl.org/coar/resource_type/c_6501journal articlehttp://purl.org/coar/access_right/c_16ecapplication/pdffulltexthttps://prod-dspace.uminho.pt/bitstreams/fc7b6bc1-c006-41de-af2f-ae4c72773f35/downloadhttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://prod-dspace.uminho.pt/bitstreams/bc90ab64-ccbc-4151-9778-4b1bf9b618ed/download |
| spellingShingle | Convolutional neural networks for the classification of glitches in gravitational-wave data streams Fernandes, Tiago deep learning glitches gravitational waves machine learning |
| status | SINGLETON |
| subject.fl_str_mv | deep learning glitches gravitational waves machine learning |
| title | Convolutional neural networks for the classification of glitches in gravitational-wave data streams |
| title_full | Convolutional neural networks for the classification of glitches in gravitational-wave data streams |
| title_fullStr | Convolutional neural networks for the classification of glitches in gravitational-wave data streams |
| title_full_unstemmed | Convolutional neural networks for the classification of glitches in gravitational-wave data streams |
| title_short | Convolutional neural networks for the classification of glitches in gravitational-wave data streams |
| title_sort | Convolutional neural networks for the classification of glitches in gravitational-wave data streams |
| topic | deep learning glitches gravitational waves machine learning |
| topic_facet | deep learning glitches gravitational waves machine learning |
| url | https://hdl.handle.net/1822/87778 |
| visible | 1 |