{"id":2019,"date":"2026-05-14T19:20:16","date_gmt":"2026-05-14T19:20:16","guid":{"rendered":"https:\/\/openlab.bmcc.cuny.edu\/research-and-scholarship\/?p=2019"},"modified":"2026-05-14T19:20:30","modified_gmt":"2026-05-14T19:20:30","slug":"reconstructing-the-public-calnet-baseline-for-eclipsing-binary-classification-a-deep-learning-model-for-identifying-eclipsing-binaries","status":"publish","type":"post","link":"https:\/\/openlab.bmcc.cuny.edu\/research-and-scholarship\/2026\/05\/14\/reconstructing-the-public-calnet-baseline-for-eclipsing-binary-classification-a-deep-learning-model-for-identifying-eclipsing-binaries\/","title":{"rendered":"Reconstructing the Public CALNet Baseline for Eclipsing Binary Classification. A Deep-Learning Model for Identifying Eclipsing Binaries"},"content":{"rendered":"\n<p>Research conducted by James D. Baker \u2022 mentored by Dr. Iqram Hussain<\/p>\n\n\n\n<p>Abstract<\/p>\n\n\n\n<p>CALNet is a deep-learning pipeline built to identify eclipsing binaries in TESS light-curve data. Eclipsing binaries are pairs of stars whose brightness changes in a repeating pattern when one star passes in front of the other. Establishing a reliable baseline for this task is important because future classifier extensions need a stable starting point. This project asks how much of the public CALNet baseline can be reconstructed, implemented, and run faithfully enough to support later model development.<\/p>\n\n\n\n<p>To answer that question, I used the published paper, public repository, and author clarification to reconstruct the positive-sample manifest, generate a conservative negative candidate pool, stage a TESS sectors 1\u201310 slice, preprocess the data, and rebuild CALNet-style light-curve and GLS training inputs. I then ran the unchanged public baseline path on Google Colab Pro GPU, changing only the compute environment so the training could finish more reliably.<\/p>\n\n\n\n<p>The completed 30-epoch run on 11,553 reconstructed rows achieved 95.18% accuracy, 94.72% recall, 95.63% precision, and 95.18% F1. These results show that a meaningful public-baseline reconstruction of CALNet is possible and can produce strong classification performance on a reconstructed slice. At the same time, exact historical reproduction of the final published model is still limited by missing artifacts, including the original negative-manifest workflow, original checkpoint, and full iterative retraining history. This reconstruction gives us a credible baseline for future scaling experiments and later controlled classifier extensions.<\/p>\n\n\n\n<p><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Research conducted by James D. Baker \u2022 mentored by Dr. Iqram Hussain Abstract CALNet is a deep-learning pipeline built to identify eclipsing binaries in TESS light-curve data. Eclipsing binaries are pairs of stars whose brightness changes in a repeating pattern when one star passes in front of the other. Establishing a reliable baseline for this [&hellip;]<\/p>\n","protected":false},"author":10389,"featured_media":2022,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"portfolio_post_id":0,"portfolio_citation":"","portfolio_annotation":"","openlab_post_visibility":"","footnotes":""},"categories":[536,3],"tags":[],"coauthors":[559],"class_list":["post-2019","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-bars-2026","category-research"],"_links":{"self":[{"href":"https:\/\/openlab.bmcc.cuny.edu\/research-and-scholarship\/wp-json\/wp\/v2\/posts\/2019","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/openlab.bmcc.cuny.edu\/research-and-scholarship\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/openlab.bmcc.cuny.edu\/research-and-scholarship\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/openlab.bmcc.cuny.edu\/research-and-scholarship\/wp-json\/wp\/v2\/users\/10389"}],"replies":[{"embeddable":true,"href":"https:\/\/openlab.bmcc.cuny.edu\/research-and-scholarship\/wp-json\/wp\/v2\/comments?post=2019"}],"version-history":[{"count":6,"href":"https:\/\/openlab.bmcc.cuny.edu\/research-and-scholarship\/wp-json\/wp\/v2\/posts\/2019\/revisions"}],"predecessor-version":[{"id":2031,"href":"https:\/\/openlab.bmcc.cuny.edu\/research-and-scholarship\/wp-json\/wp\/v2\/posts\/2019\/revisions\/2031"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/openlab.bmcc.cuny.edu\/research-and-scholarship\/wp-json\/wp\/v2\/media\/2022"}],"wp:attachment":[{"href":"https:\/\/openlab.bmcc.cuny.edu\/research-and-scholarship\/wp-json\/wp\/v2\/media?parent=2019"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/openlab.bmcc.cuny.edu\/research-and-scholarship\/wp-json\/wp\/v2\/categories?post=2019"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/openlab.bmcc.cuny.edu\/research-and-scholarship\/wp-json\/wp\/v2\/tags?post=2019"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/openlab.bmcc.cuny.edu\/research-and-scholarship\/wp-json\/wp\/v2\/coauthors?post=2019"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}