A Convolutional Neural Network–Based Approach for Detecting Solar System Objects in Wide-field Imaging
We present a deep learning method that utilizes convolutional neural networks (CNNs) to discover trans-Neptunian objects (TNOs) in wide-field survey imaging data. Our CNNs were trained using artificial sources planted into a time series of 44 205 s CFHT MegaCam large-format mosaic images. We extract...
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IOP Publishing
2025-01-01
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| Series: | The Planetary Science Journal |
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| Online Access: | https://doi.org/10.3847/PSJ/add409 |
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| author | Aram Lee J. J. Kavelaars Hossen Teimoorinia Wesley Fraser Edward Ashton |
| author_facet | Aram Lee J. J. Kavelaars Hossen Teimoorinia Wesley Fraser Edward Ashton |
| author_sort | Aram Lee |
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| description | We present a deep learning method that utilizes convolutional neural networks (CNNs) to discover trans-Neptunian objects (TNOs) in wide-field survey imaging data. Our CNNs were trained using artificial sources planted into a time series of 44 205 s CFHT MegaCam large-format mosaic images. We extracted 64 × 64 pixel subimages and labeled each subimage pair with the presence or absence of a moving source and the source’s location and magnitude. Our MobileNet-derived classification model achieved 91% recall with 90% precision on test data. A separate regression model predicted the source locations with a mean absolute error of ±1.5 pixels for sources with a signal-to-noise ratio (SNR) of 16.5 or higher. By grouping sources based on linear sky motion, we achieved ∼40% detection limit for sources with an SNR ≈ 7 in individual frames. We also examined a scoring approach to reduce the false positives. In this approach, images are scored based on the probability values from the classification model. With the scoring approach, we achieved a detection limit of SNR ≈ 3 in individual frames. We detected approximately 200 solar system object (SSO) candidates ( sim 5 moving rates consistent with TNOs) in our 1 square degree of imaging. We tested the trained models on images from different sky regions, confirming that the models learned from the motion of sources rather than from the backgrounds or shapes of sources. We demonstrate that deep learning object detection algorithms can aid in TNO and SSO discovery, supporting future discovery pipeline development. |
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| institution | Kabale University |
| issn | 2632-3338 |
| language | English |
| publishDate | 2025-01-01 |
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| spelling | doaj-art-bcb8a4ccc64740b28644a6945db30f0b2025-08-20T03:29:53ZengIOP PublishingThe Planetary Science Journal2632-33382025-01-016614710.3847/PSJ/add409A Convolutional Neural Network–Based Approach for Detecting Solar System Objects in Wide-field ImagingAram Lee0https://orcid.org/0000-0002-4258-2964J. J. Kavelaars1https://orcid.org/0000-0001-7032-5255Hossen Teimoorinia2https://orcid.org/0000-0002-4348-6974Wesley Fraser3https://orcid.org/0000-0001-6680-6558Edward Ashton4https://orcid.org/0000-0002-4637-8426University of Victoria , Department of Physics and Astronomy, Elliott Building, 3800 Finnerty Road, Victoria, BC, V8P 5C2, Canada; Department of Physics and Astronomy, University of British Columbia , 6224 Agricultural Road, Vancouver, BC V6T 1Z1, CanadaUniversity of Victoria , Department of Physics and Astronomy, Elliott Building, 3800 Finnerty Road, Victoria, BC, V8P 5C2, Canada; Department of Physics and Astronomy, University of British Columbia , 6224 Agricultural Road, Vancouver, BC V6T 1Z1, Canada; National Research Council of Canada , Herzberg Astronomy and Astrophysics Research Centre, 5071 West Saanich Road, Victoria, BC, V9E 2E7, CanadaUniversity of Victoria , Department of Physics and Astronomy, Elliott Building, 3800 Finnerty Road, Victoria, BC, V8P 5C2, Canada; National Research Council of Canada , Herzberg Astronomy and Astrophysics Research Centre, 5071 West Saanich Road, Victoria, BC, V9E 2E7, CanadaUniversity of Victoria , Department of Physics and Astronomy, Elliott Building, 3800 Finnerty Road, Victoria, BC, V8P 5C2, Canada; National Research Council of Canada , Herzberg Astronomy and Astrophysics Research Centre, 5071 West Saanich Road, Victoria, BC, V9E 2E7, CanadaInstitute of Astronomy and Astrophysics , Academia Sinica, No.1, Sec. 4, Roosevelt Road, Taipei, 10617, TaiwanWe present a deep learning method that utilizes convolutional neural networks (CNNs) to discover trans-Neptunian objects (TNOs) in wide-field survey imaging data. Our CNNs were trained using artificial sources planted into a time series of 44 205 s CFHT MegaCam large-format mosaic images. We extracted 64 × 64 pixel subimages and labeled each subimage pair with the presence or absence of a moving source and the source’s location and magnitude. Our MobileNet-derived classification model achieved 91% recall with 90% precision on test data. A separate regression model predicted the source locations with a mean absolute error of ±1.5 pixels for sources with a signal-to-noise ratio (SNR) of 16.5 or higher. By grouping sources based on linear sky motion, we achieved ∼40% detection limit for sources with an SNR ≈ 7 in individual frames. We also examined a scoring approach to reduce the false positives. In this approach, images are scored based on the probability values from the classification model. With the scoring approach, we achieved a detection limit of SNR ≈ 3 in individual frames. We detected approximately 200 solar system object (SSO) candidates ( sim 5 moving rates consistent with TNOs) in our 1 square degree of imaging. We tested the trained models on images from different sky regions, confirming that the models learned from the motion of sources rather than from the backgrounds or shapes of sources. We demonstrate that deep learning object detection algorithms can aid in TNO and SSO discovery, supporting future discovery pipeline development.https://doi.org/10.3847/PSJ/add409Solar systemKuiper beltDetectionNeural networks |
| spellingShingle | Aram Lee J. J. Kavelaars Hossen Teimoorinia Wesley Fraser Edward Ashton A Convolutional Neural Network–Based Approach for Detecting Solar System Objects in Wide-field Imaging The Planetary Science Journal Solar system Kuiper belt Detection Neural networks |
| title | A Convolutional Neural Network–Based Approach for Detecting Solar System Objects in Wide-field Imaging |
| title_full | A Convolutional Neural Network–Based Approach for Detecting Solar System Objects in Wide-field Imaging |
| title_fullStr | A Convolutional Neural Network–Based Approach for Detecting Solar System Objects in Wide-field Imaging |
| title_full_unstemmed | A Convolutional Neural Network–Based Approach for Detecting Solar System Objects in Wide-field Imaging |
| title_short | A Convolutional Neural Network–Based Approach for Detecting Solar System Objects in Wide-field Imaging |
| title_sort | convolutional neural network based approach for detecting solar system objects in wide field imaging |
| topic | Solar system Kuiper belt Detection Neural networks |
| url | https://doi.org/10.3847/PSJ/add409 |
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