Short-Period Variables in TESS Full-Frame Image Light Curves Identified via Convolutional Neural Networks
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arXiv
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| Autori principali: | , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866929525178236928 |
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| author | Olmschenk, Greg Barry, Richard K. Silva, Stela Ishitani Powell, Brian P. Kruse, Ethan Schnittman, Jeremy D. Cieplak, Agnieszka M. Barclay, Thomas Solanki, Siddhant Ortega, Bianca Baker, John Mamani, Yesenia Helem Salinas |
| author_facet | Olmschenk, Greg Barry, Richard K. Silva, Stela Ishitani Powell, Brian P. Kruse, Ethan Schnittman, Jeremy D. Cieplak, Agnieszka M. Barclay, Thomas Solanki, Siddhant Ortega, Bianca Baker, John Mamani, Yesenia Helem Salinas |
| contents | The Transiting Exoplanet Survey Satellite (TESS) mission measured light from stars in ~85% of the sky throughout its two-year primary mission, resulting in millions of TESS 30-minute cadence light curves to analyze in the search for transiting exoplanets. To search this vast dataset, we aim to provide an approach that is both computationally efficient, produces highly performant predictions, and minimizes the required human search effort. We present a convolutional neural network that we train to identify short period variables. To make a prediction for a given light curve, our network requires no prior target parameters identified using other methods. Our network performs inference on a TESS 30-minute cadence light curve in ~5ms on a single GPU, enabling large scale archival searches. We present a collection of 14156 short-period variables identified by our network. The majority of our identified variables fall into two prominent populations, one of short-period main sequence binaries and another of Delta Scuti stars. Our neural network model and related code is additionally provided as open-source code for public use and extension. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12369 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Short-Period Variables in TESS Full-Frame Image Light Curves Identified via Convolutional Neural Networks Olmschenk, Greg Barry, Richard K. Silva, Stela Ishitani Powell, Brian P. Kruse, Ethan Schnittman, Jeremy D. Cieplak, Agnieszka M. Barclay, Thomas Solanki, Siddhant Ortega, Bianca Baker, John Mamani, Yesenia Helem Salinas Solar and Stellar Astrophysics Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Machine Learning Image and Video Processing The Transiting Exoplanet Survey Satellite (TESS) mission measured light from stars in ~85% of the sky throughout its two-year primary mission, resulting in millions of TESS 30-minute cadence light curves to analyze in the search for transiting exoplanets. To search this vast dataset, we aim to provide an approach that is both computationally efficient, produces highly performant predictions, and minimizes the required human search effort. We present a convolutional neural network that we train to identify short period variables. To make a prediction for a given light curve, our network requires no prior target parameters identified using other methods. Our network performs inference on a TESS 30-minute cadence light curve in ~5ms on a single GPU, enabling large scale archival searches. We present a collection of 14156 short-period variables identified by our network. The majority of our identified variables fall into two prominent populations, one of short-period main sequence binaries and another of Delta Scuti stars. Our neural network model and related code is additionally provided as open-source code for public use and extension. |
| title | Short-Period Variables in TESS Full-Frame Image Light Curves Identified via Convolutional Neural Networks |
| topic | Solar and Stellar Astrophysics Earth and Planetary Astrophysics Instrumentation and Methods for Astrophysics Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2402.12369 |