{"title":"利用超小型卫星数据合成系外行星过境光曲线","authors":"A. Fuentes , M. Solar","doi":"10.1016/j.ascom.2024.100816","DOIUrl":null,"url":null,"abstract":"<div><p>In this article, we present a dataset of light curves with synthetic signals. BRITE light curves (a constellation of five nanosatellites) are the main source of this dataset. We create the synthetic light curves of exoplanet transit by applying a pre-processing to the BRITE data and an injection of transit according to the Mandel and Agol model with a constraint of stellar radius <span><math><mrow><mo><</mo><mn>3</mn><mo>.</mo><mn>08</mn><mrow><mo>[</mo><msub><mrow><mi>R</mi></mrow><mrow><mi>s</mi><mi>u</mi><mi>n</mi></mrow></msub><mo>]</mo></mrow></mrow></math></span> and planetary radius between 0.95 and 2.1 <span><math><mrow><mo>[</mo><msub><mrow><mi>R</mi></mrow><mrow><mi>j</mi><mi>u</mi><mi>p</mi></mrow></msub><mo>]</mo></mrow></math></span>. We apply a quality criterion, obtaining 597 Planet Candidate (PC) examples and 3126 Not Planet Candidate examples as a dataset. PCs are injected simulated planets and are not around unique stars. We design a Deep Learning (DL) model to be trained with the created dataset. The DL model is a modified AstroNet Convolutional Neural Network (CNN) from literature to detect possible exoplanets. After evaluation over the testing set we obtain an accuracy of 99.46%, precision of 100% (<span><math><mrow><mi>P</mi><mi>C</mi><mi>p</mi><mi>r</mi><mi>e</mi><mi>c</mi><mi>i</mi><mi>s</mi><mi>i</mi><mi>o</mi><mi>n</mi></mrow></math></span>) and a recall of 96.72% for the PC class (<span><math><mrow><mi>P</mi><mi>C</mi><mi>r</mi><mi>e</mi><mi>c</mi><mi>a</mi><mi>l</mi><mi>l</mi></mrow></math></span>), and an area under the curve receiver operating characteristics (<span><math><mrow><mi>A</mi><mi>U</mi><mi>C</mi><mo>−</mo><mi>R</mi><mi>O</mi><mi>C</mi></mrow></math></span>) of 100%, overcoming the results of existing networks tested on BRITE data. We ultimately search for potential exoplanets using the pre-processed data from BRITE, finding signals similar to exoplanetary transits in the targets HD 039060, HD 022049, HD 036861 and HD 218396.</p></div>","PeriodicalId":48757,"journal":{"name":"Astronomy and Computing","volume":"47 ","pages":"Article 100816"},"PeriodicalIF":1.9000,"publicationDate":"2024-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Synthetic light curves of exoplanet transit using nanosatellite data\",\"authors\":\"A. Fuentes , M. Solar\",\"doi\":\"10.1016/j.ascom.2024.100816\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>In this article, we present a dataset of light curves with synthetic signals. BRITE light curves (a constellation of five nanosatellites) are the main source of this dataset. We create the synthetic light curves of exoplanet transit by applying a pre-processing to the BRITE data and an injection of transit according to the Mandel and Agol model with a constraint of stellar radius <span><math><mrow><mo><</mo><mn>3</mn><mo>.</mo><mn>08</mn><mrow><mo>[</mo><msub><mrow><mi>R</mi></mrow><mrow><mi>s</mi><mi>u</mi><mi>n</mi></mrow></msub><mo>]</mo></mrow></mrow></math></span> and planetary radius between 0.95 and 2.1 <span><math><mrow><mo>[</mo><msub><mrow><mi>R</mi></mrow><mrow><mi>j</mi><mi>u</mi><mi>p</mi></mrow></msub><mo>]</mo></mrow></math></span>. We apply a quality criterion, obtaining 597 Planet Candidate (PC) examples and 3126 Not Planet Candidate examples as a dataset. PCs are injected simulated planets and are not around unique stars. We design a Deep Learning (DL) model to be trained with the created dataset. The DL model is a modified AstroNet Convolutional Neural Network (CNN) from literature to detect possible exoplanets. After evaluation over the testing set we obtain an accuracy of 99.46%, precision of 100% (<span><math><mrow><mi>P</mi><mi>C</mi><mi>p</mi><mi>r</mi><mi>e</mi><mi>c</mi><mi>i</mi><mi>s</mi><mi>i</mi><mi>o</mi><mi>n</mi></mrow></math></span>) and a recall of 96.72% for the PC class (<span><math><mrow><mi>P</mi><mi>C</mi><mi>r</mi><mi>e</mi><mi>c</mi><mi>a</mi><mi>l</mi><mi>l</mi></mrow></math></span>), and an area under the curve receiver operating characteristics (<span><math><mrow><mi>A</mi><mi>U</mi><mi>C</mi><mo>−</mo><mi>R</mi><mi>O</mi><mi>C</mi></mrow></math></span>) of 100%, overcoming the results of existing networks tested on BRITE data. We ultimately search for potential exoplanets using the pre-processed data from BRITE, finding signals similar to exoplanetary transits in the targets HD 039060, HD 022049, HD 036861 and HD 218396.</p></div>\",\"PeriodicalId\":48757,\"journal\":{\"name\":\"Astronomy and Computing\",\"volume\":\"47 \",\"pages\":\"Article 100816\"},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-02-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Astronomy and Computing\",\"FirstCategoryId\":\"101\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2213133724000313\",\"RegionNum\":4,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ASTRONOMY & ASTROPHYSICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Astronomy and Computing","FirstCategoryId":"101","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2213133724000313","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ASTRONOMY & ASTROPHYSICS","Score":null,"Total":0}
Synthetic light curves of exoplanet transit using nanosatellite data
In this article, we present a dataset of light curves with synthetic signals. BRITE light curves (a constellation of five nanosatellites) are the main source of this dataset. We create the synthetic light curves of exoplanet transit by applying a pre-processing to the BRITE data and an injection of transit according to the Mandel and Agol model with a constraint of stellar radius and planetary radius between 0.95 and 2.1 . We apply a quality criterion, obtaining 597 Planet Candidate (PC) examples and 3126 Not Planet Candidate examples as a dataset. PCs are injected simulated planets and are not around unique stars. We design a Deep Learning (DL) model to be trained with the created dataset. The DL model is a modified AstroNet Convolutional Neural Network (CNN) from literature to detect possible exoplanets. After evaluation over the testing set we obtain an accuracy of 99.46%, precision of 100% () and a recall of 96.72% for the PC class (), and an area under the curve receiver operating characteristics () of 100%, overcoming the results of existing networks tested on BRITE data. We ultimately search for potential exoplanets using the pre-processed data from BRITE, finding signals similar to exoplanetary transits in the targets HD 039060, HD 022049, HD 036861 and HD 218396.
Astronomy and ComputingASTRONOMY & ASTROPHYSICSCOMPUTER SCIENCE,-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
4.10
自引率
8.00%
发文量
67
期刊介绍:
Astronomy and Computing is a peer-reviewed journal that focuses on the broad area between astronomy, computer science and information technology. The journal aims to publish the work of scientists and (software) engineers in all aspects of astronomical computing, including the collection, analysis, reduction, visualisation, preservation and dissemination of data, and the development of astronomical software and simulations. The journal covers applications for academic computer science techniques to astronomy, as well as novel applications of information technologies within astronomy.