[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122357-en":3,"doc-seo-122357-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},122357,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Automated Identification of Exoplanets with Machine Learning","Exoplanet detection is an expanding field, increasingly powered by machine learning methods applied to stellar photometric time series. This work evaluates the AstroNet deep learning algorithm on light curves preprocessed with TFAW, aiming to classify Threshold Crossing Events (TCEs) and highlight new exoplanet candidates. The model is first validated by recovering known high-confidence predictions, then used to reclassify 478 prioritized candidates from the TFAW survey, and finally applied to 65.970K K2 light curves to identify 3.800 previously unreported candidates.","Automated Identification of Exoplanets with Machine Learning  \nAuthor: Vicen¸c Fern´andez Fern´andez, [vfernafe18@alumnes.ub.edu](vfernafe18@alumnes.ub.edu)[ ](vfernafe18@alumnes.ub.edu)Facultat de F´ısica, Universitat de Barcelona, Diagonal 645, 08028 Barcelona, Spain.  \nAdvisor: Daniel del Ser Badia, [danieldelser@ub.edu](danieldelser@ub.edu)  \nAbstract: The detection of exoplanets is a rapidly evolving field, increasingly supported by advances in Machine Learning. In this work, we explore the capabilities of the AstroNet deep learning algorithm when applied to the light curves preprocessed by the TFAW algorithm. The goal is to classify Threshold Crossing Events (TCEs) and identify new potential exoplanet candidates.  \nWe first validate the performance of the model on a subset of previously confirmed exoplanets, showing that the algorithm successfully recovers the expected high prediction scores. Subsequently, we analyze a visually selected subset of 478 candidates from the TFAW survey with assigned priority levels, using the model output to propose priority reclassifications based on objective criteria. Finally, we apply the model to a dataset of 65.970 K2 light curves, identifying 3.800 previously unreported candidates.  \nOur results demonstrate that AstroNet, when combined with TFAW, is a powerful tool for automatic exoplanet candidate classification. However, we also emphasize that such models are not definitive, and complementary validation methods remain essential to confirm the planetary nature of any new transiting candidate.  \nKeywords: Exoplanets, Planetary Systems, Photometry, Data Analysis, Machine Learning  \nI. INTRODUCTION  \nThe discovery and study of exoplanets has become oneof the most dynamic and impactful fields in modern astrophysics. Since the first confirmed detection in the early 1990s, thousands of exoplanets have been identified. These discoveries have revolutionized our understanding of planetary formation, migration, and habitability. With each new detection, researchers gain further insight into the frequency of Earth-like planets and the potential for life elsewhere in the universe.  \nFollowing the success of the original Kepler mission, NASA launched the K2 mission [1] as an extended campaign after the failure of two of Kepler’s reaction wheels. K2 made use of solar radiation pressure to maintain pointing stability, allowing observations of various regions along the ecliptic plane. Despite reduced stability, K2 maintained remarkable photometric precision and, when combined with advanced data processing algorithms, remained highly effective in detecting exoplanets.  \nTo further exploit the potential of K2, especially in the search for previously undetected exoplanet candidates, new methodologies were required. In this context, the TFAW survey [2] emerges. Its current goal is to search for exoplanet candidates that may have been missed by previous studies, by further enhancing the photometric precision of EVEREST 2.0-corrected light curves. The survey combines TFAW, a new wavelet-based detrending and denoising algorithm developed by [3], in conjunction with the EVEREST 2.0 pipeline [4] and the Transit Least Squares (TLS) search algorithm [5] . As demonstrated in [6], TFAW achieves superior photometric precision and improved planet characterization compared to other detrending techniques applied to K2 data.  \nIn recent years, machine learning (ML) techniques have been increasingly adopted to automate analysis of light  \ncurve data in exoplanetary science. From decision trees and random forests to deep neural networks, ML has enabled significant advances in vetting transit-like signals and classifying Threshold Crossing Events (TCEs) . Notably, convolutional neural networks (CNNs) have proven especially effective due to their capacity to extract features from time-series data analogous to image analysis. Projects such as Autovetter [7] and Robovetter [8] have demonstrated the power of supervised lear","cbCaiuPnHTeJ0Oqm","https://ap.wps.com/l/cbCaiuPnHTeJ0Oqm","pdf",1286406,1,6,"English","en",105,"# Introduction\n## Exoplanet discovery and K2 mission context\n## TFAW survey and data processing pipeline\n## Motivation for machine learning in light-curve vetting\n# Methodology\n## AstroNet workflow overview\n## Creating the training set (TFRecord, features)","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"Classify Threshold Crossing Events using AstroNet on TFAW-preprocessed light curves, and propose new high-potential exoplanet candidates.\"},{\"question\":\"How is the AstroNet model validated?\",\"answer\":\"The performance is tested on a subset of previously confirmed exoplanets to verify that the algorithm recovers expected high prediction scores.\"},{\"question\":\"How are new candidates identified from the K2 dataset?\",\"answer\":\"The method is applied to a dataset of 65.970K K2 light curves, producing 3.800 previously unreported candidate detections, then requiring complementary validation to confirm planetary nature.\"}]","Automated Identification of Exoplanets with Machine Learning | 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is the main goal of this study?","Question",{"text":76,"@type":77},"Classify Threshold Crossing Events using AstroNet on TFAW-preprocessed light curves, and propose new high-potential exoplanet candidates.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How is the AstroNet model validated?",{"text":81,"@type":77},"The performance is tested on a subset of previously confirmed exoplanets to verify that the algorithm recovers expected high prediction scores.",{"name":83,"@type":74,"acceptedAnswer":84},"How are new candidates identified from the K2 dataset?",{"text":85,"@type":77},"The method is applied to a dataset of 65.970K K2 light curves, producing 3.800 previously unreported candidate detections, then requiring complementary validation to confirm planetary 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