Classical Machine Learning Techniques in the Search of Extrasolar Planets
The field of astronomical data analysis has experienced an important paradigm shift in the recent years. The automation of certain analysis procedures is no longer a desirable feature for reducing the human effort, but a must have asset for coping with the extremely large datasets that new instrumen...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Centro Latinoamericano de Estudios en Informática
2019-12-01
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| Series: | CLEI Electronic Journal |
| Subjects: | |
| Online Access: | http://www.clei.org/cleiej/index.php/cleiej/article/view/437 |
| _version_ | 1811296604916809728 |
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| author | Francisco Alejandro Mena Margarita Constanza Bugueño Mauricio Araya |
| author_facet | Francisco Alejandro Mena Margarita Constanza Bugueño Mauricio Araya |
| author_sort | Francisco Alejandro Mena |
| collection | DOAJ |
| description | The field of astronomical data analysis has experienced an important paradigm shift in the recent years. The automation of certain analysis procedures is no longer a desirable feature for reducing the human effort, but a must have asset for coping with the extremely large datasets that new instrumentation technologies are producing. In particular, the detection of transit planets --- bodies that move across the face of another body --- is an ideal setup for intelligent automation. Knowing if the variation within a light curve is evidence of a planet, requires applying advanced pattern recognition methods to a very large number of candidate stars. Here we present a supervised learning approach to refine the results produced by a case-by-case analysis of light-curves, harnessing the generalization power of machine learning techniques to predict the currently unclassified light-curves. The method uses feature engineering to find a suitable representation for classification, and different performance criteria to evaluate them and decide. Our results show that this automatic technique can help to speed up the very time-consuming manual process that is currently done by expert scientists. |
| first_indexed | 2024-04-13T05:50:58Z |
| format | Article |
| id | doaj.art-0844b14f4b0b459ba9c69c52929c2680 |
| institution | Directory Open Access Journal |
| issn | 0717-5000 |
| language | English |
| last_indexed | 2024-04-13T05:50:58Z |
| publishDate | 2019-12-01 |
| publisher | Centro Latinoamericano de Estudios en Informática |
| record_format | Article |
| series | CLEI Electronic Journal |
| spelling | doaj.art-0844b14f4b0b459ba9c69c52929c26802022-12-22T02:59:46ZengCentro Latinoamericano de Estudios en InformáticaCLEI Electronic Journal0717-50002019-12-0122310.19153/cleiej.22.3.3Classical Machine Learning Techniques in the Search of Extrasolar PlanetsFrancisco Alejandro Mena0Margarita Constanza Bugueño1Mauricio Araya2Universidad Técnica Federico Santa MaríaUniversidad Técnica Federico Santa MaríaUniversidad Técnica Federico Santa MaríaThe field of astronomical data analysis has experienced an important paradigm shift in the recent years. The automation of certain analysis procedures is no longer a desirable feature for reducing the human effort, but a must have asset for coping with the extremely large datasets that new instrumentation technologies are producing. In particular, the detection of transit planets --- bodies that move across the face of another body --- is an ideal setup for intelligent automation. Knowing if the variation within a light curve is evidence of a planet, requires applying advanced pattern recognition methods to a very large number of candidate stars. Here we present a supervised learning approach to refine the results produced by a case-by-case analysis of light-curves, harnessing the generalization power of machine learning techniques to predict the currently unclassified light-curves. The method uses feature engineering to find a suitable representation for classification, and different performance criteria to evaluate them and decide. Our results show that this automatic technique can help to speed up the very time-consuming manual process that is currently done by expert scientists.http://www.clei.org/cleiej/index.php/cleiej/article/view/437machine learningexoplanet detectionfeature engineeringlight curve |
| spellingShingle | Francisco Alejandro Mena Margarita Constanza Bugueño Mauricio Araya Classical Machine Learning Techniques in the Search of Extrasolar Planets CLEI Electronic Journal machine learning exoplanet detection feature engineering light curve |
| title | Classical Machine Learning Techniques in the Search of Extrasolar Planets |
| title_full | Classical Machine Learning Techniques in the Search of Extrasolar Planets |
| title_fullStr | Classical Machine Learning Techniques in the Search of Extrasolar Planets |
| title_full_unstemmed | Classical Machine Learning Techniques in the Search of Extrasolar Planets |
| title_short | Classical Machine Learning Techniques in the Search of Extrasolar Planets |
| title_sort | classical machine learning techniques in the search of extrasolar planets |
| topic | machine learning exoplanet detection feature engineering light curve |
| url | http://www.clei.org/cleiej/index.php/cleiej/article/view/437 |
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