{"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/deepsource-point-source-detection-using-deep","title":"DeepSource: Point Source Detection using Deep Learning","arxiv_id":"1807.02701","date":"2018-07-07","proceeding":null,"authors":["A. Vafaei Sadr","Etienne. E. Vos","Bruce A. Bassett","Zafiirah Hosenie","N. Oozeer","Michelle Lochner"],"abstract":"Point source detection at low signal-to-noise is challenging for astronomical\nsurveys, particularly in radio interferometry images where the noise is\ncorrelated. Machine learning is a promising solution, allowing the development\nof algorithms tailored to specific telescope arrays and science cases. We\npresent DeepSource - a deep learning solution - that uses convolutional neural\nnetworks to achieve these goals. DeepSource enhances the Signal-to-Noise Ratio\n(SNR) of the original map and then uses dynamic blob detection to detect\nsources. Trained and tested on two sets of 500 simulated 1 deg x 1 deg MeerKAT\nimages with a total of 300,000 sources, DeepSource is essentially perfect in\nboth purity and completeness down to SNR = 4 and outperforms PyBDSF in all\nmetrics. For uniformly-weighted images it achieves a Purity x Completeness (PC)\nscore at SNR = 3 of 0.73, compared to 0.31 for the best PyBDSF model. For\nnatural-weighting we find a smaller improvement of ~40% in the PC score at SNR\n= 3. If instead we ask where either of the purity or completeness first drop to\n90%, we find that DeepSource reaches this value at SNR = 3.6 compared to the\n4.3 of PyBDSF (natural-weighting). A key advantage of DeepSource is that it can\nlearn to optimally trade off purity and completeness for any science case under\nconsideration. Our results show that deep learning is a promising approach to\npoint source detection in astronomical images.","url_abs":"http://arxiv.org/abs/1807.02701v1","url_pdf":"http://arxiv.org/pdf/1807.02701v1.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":"deepsource-point-source-detection-using-deep","repo_url":"https://github.com/vafaei-ar/deepsource","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"BSD-3-Clause"}}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"radio-interferometry","task_name":"Radio Interferometry"}],"methods":[{"method_slug":"pc","method_name":"pc"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.02701","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.02701"}},"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. 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