{"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/a-robust-hot-subdwarfs-identification-method","title":"A Robust Hot Subdwarfs Identification Method Based on Deep Learning","arxiv_id":"2201.08967","date":"2022-01-22","proceeding":null,"authors":["Lei Tan","Ying Mei","Zhicun Liu","Yangping Luo","Hui Deng","Feng Wang","Linhua Deng","Chao Liu"],"abstract":"Hot subdwarf star is a particular type of star that is crucial for studying binary evolution and atmospheric diffusion processes. In recent years, identifying Hot subdwarfs by machine learning methods has become a hot topic, but there are still limitations in automation and accuracy. In this paper, we proposed a robust identification method based on the convolutional neural network (CNN). We first constructed the dataset using the spectral data of LAMOS DR7-V1. We then constructed a hybrid recognition model including an 8-class classification model and a binary classification model. The model achieved an accuracy of 96.17% on the testing set. To further validate the accuracy of the model, we selected 835 Hot subdwarfs that were not involved in the training process from the identified LAMOST catalog (2428, including repeated observations) as the validation set. An accuracy of 96.05% was achieved. On this basis, we used the model to filter and classify all 10,640,255 spectra of LAMOST DR7-V1, and obtained a catalog of 2393 Hot subdwarf candidates, of which 2067 have been confirmed. We found 25 new Hot subdwarfs among the remaining candidates by manual validation. The overall accuracy of the model is 87.42%. Overall, the model presented in this study can effectively identify specific spectra with robust results and high accuracy, and can be further applied to the classification of large-scale spectra and the search of specific targets.","url_abs":"https://arxiv.org/abs/2201.08967v1","url_pdf":"https://arxiv.org/pdf/2201.08967v1.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"a-robust-hot-subdwarfs-identification-method","repo_url":"https://github.com/astronomical-data-processing/cnn_spectra_classification","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2201.08967","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2201.08967"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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/astronomical-data-processing/cnn_spectra_classification","reach":null}],"summary":{"ran_violates":1,"unverified":1},"by_repo_kind":{"official":{"samples":2,"ran":1,"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":2,"samples":[{"code_sha256_prefix":"f34d11007be3a537","entry":"func","repo":"astronomical-data-processing/cnn_spectra_classification","repo_kind":"official","path":"spec_generation.py","file_url":"https://github.com/astronomical-data-processing/cnn_spectra_classification/blob/HEAD/spec_generation.py","link_basis":"first_harvest_node","language":"python","status":"ran_violates","verification_level":1,"contract_check":"VIOLATES","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"f34d11007be3a537"}},{"code_sha256_prefix":"70d1db195b80d368","entry":"onehot","repo":"astronomical-data-processing/cnn_spectra_classification","repo_kind":"official","path":"cnn_spectra_classification.py","file_url":"https://github.com/astronomical-data-processing/cnn_spectra_classification/blob/HEAD/cnn_spectra_classification.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"70d1db195b80d368"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}