{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/deep-recurrent-neural-networks-for-supernovae","title":"Deep Recurrent Neural Networks for Supernovae Classification","arxiv_id":"1606.07442","date":"2016-06-23","proceeding":null,"authors":["Tom Charnock","Adam Moss"],"abstract":"We apply deep recurrent neural networks, which are capable of learning\ncomplex sequential information, to classify supernovae\\footnote{Code available\nat\n\\href{https://github.com/adammoss/supernovae}{https://github.com/adammoss/supernovae}}.\nThe observational time and filter fluxes are used as inputs to the network, but\nsince the inputs are agnostic additional data such as host galaxy information\ncan also be included. Using the Supernovae Photometric Classification Challenge\n(SPCC) data, we find that deep networks are capable of learning about light\ncurves, however the performance of the network is highly sensitive to the\namount of training data. For a training size of 50\\% of the representational\nSPCC dataset (around $10^4$ supernovae) we obtain a type-Ia vs. non-type-Ia\nclassification accuracy of 94.7\\%, an area under the Receiver Operating\nCharacteristic curve AUC of 0.986 and a SPCC figure-of-merit $F_1=0.64$. When\nusing only the data for the early-epoch challenge defined by the SPCC we\nachieve a classification accuracy of 93.1\\%, AUC of 0.977 and $F_1=0.58$,\nresults almost as good as with the whole light-curve. By employing\nbidirectional neural networks we can acquire impressive classification results\nbetween supernovae types -I,~-II and~-III at an accuracy of 90.4\\% and AUC of\n0.974. We also apply a pre-trained model to obtain classification probabilities\nas a function of time, and show it can give early indications of supernovae\ntype. Our method is competitive with existing algorithms and has applications\nfor future large-scale photometric surveys.","url_abs":"http://arxiv.org/abs/1606.07442v2","url_pdf":"http://arxiv.org/pdf/1606.07442v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"deep-recurrent-neural-networks-for-supernovae","repo_url":"https://github.com/adammoss/supernovae","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1606.07442","atlas_url":"https://app.syntology.ai/?focus=1606.07442","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.07442"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/adammoss/supernovae","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"unverified":4},"by_repo_kind":{"official":{"samples":4,"ran":0,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"5fcdbec6672b35dc","entry":"lambda_mask_average","repo":"adammoss/supernovae","repo_kind":"official","path":"custom_layers.py","file_url":"https://github.com/adammoss/supernovae/blob/HEAD/custom_layers.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5fcdbec6672b35dc"}},{"code_sha256_prefix":"8e59c7d8dd262e43","entry":"load_data","repo":"adammoss/supernovae","repo_kind":"official","path":"dataset.py","file_url":"https://github.com/adammoss/supernovae/blob/HEAD/dataset.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8e59c7d8dd262e43"}},{"code_sha256_prefix":"5f7bedfa9a69fd57","entry":"pad_sequences","repo":"adammoss/supernovae","repo_kind":"official","path":"dataset.py","file_url":"https://github.com/adammoss/supernovae/blob/HEAD/dataset.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"5f7bedfa9a69fd57"}},{"code_sha256_prefix":"e3225138fbcdb30a","entry":"to_categorical","repo":"adammoss/supernovae","repo_kind":"official","path":"dataset.py","file_url":"https://github.com/adammoss/supernovae/blob/HEAD/dataset.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"e3225138fbcdb30a"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}