利用基于深度学习的物体检测算法识别光曲线信号。II.一般光曲线分类框架

Kaiming Cui, D. J. Armstrong and Fabo Feng
{"title":"利用基于深度学习的物体检测算法识别光曲线信号。II.一般光曲线分类框架","authors":"Kaiming Cui, D. J. Armstrong and Fabo Feng","doi":"10.3847/1538-4365/ad62fd","DOIUrl":null,"url":null,"abstract":"Vast amounts of astronomical photometric data are generated from various projects, requiring significant effort to identify variable stars and other object classes. In light of this, a general, widely applicable classification framework would simplify the process of designing specific classifiers for various astronomical objects. We present a novel deep-learning framework for classifying light curves using a weakly supervised object detection model. Our framework identifies the optimal windows for both light curves and power spectra automatically, and zooms in on their corresponding data. This allows for automatic feature extraction from both time and frequency domains, enabling our model to handle data across different scales and sampling intervals. We train our model on data sets obtained from Kepler, TESS, and Zwicky Transient Facility multiband observations of variable stars and transients. We achieve an accuracy of 87% for combined variable and transient events, which is comparable to the performance of previous feature-based models. Our trained model can be utilized directly for other missions, such as the All-sky Automated Survey for Supernovae, without requiring any retraining or fine-tuning. To address known issues with miscalibrated predictive probabilities, we apply conformal prediction to generate robust predictive sets that guarantee true-label coverage with a given probability. Additionally, we incorporate various anomaly detection algorithms to empower our model with the ability to identify out-of-distribution objects. Our framework is implemented in the Deep-LC toolkit, which is an open-source Python package hosted on Github (https://github.com/ckm3/Deep-LC) and PyPI.","PeriodicalId":22368,"journal":{"name":"The Astrophysical Journal Supplement Series","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identifying Light-curve Signals with a Deep-learning-based Object Detection Algorithm. II. A General Light-curve Classification Framework\",\"authors\":\"Kaiming Cui, D. J. Armstrong and Fabo Feng\",\"doi\":\"10.3847/1538-4365/ad62fd\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Vast amounts of astronomical photometric data are generated from various projects, requiring significant effort to identify variable stars and other object classes. In light of this, a general, widely applicable classification framework would simplify the process of designing specific classifiers for various astronomical objects. We present a novel deep-learning framework for classifying light curves using a weakly supervised object detection model. Our framework identifies the optimal windows for both light curves and power spectra automatically, and zooms in on their corresponding data. This allows for automatic feature extraction from both time and frequency domains, enabling our model to handle data across different scales and sampling intervals. We train our model on data sets obtained from Kepler, TESS, and Zwicky Transient Facility multiband observations of variable stars and transients. We achieve an accuracy of 87% for combined variable and transient events, which is comparable to the performance of previous feature-based models. Our trained model can be utilized directly for other missions, such as the All-sky Automated Survey for Supernovae, without requiring any retraining or fine-tuning. To address known issues with miscalibrated predictive probabilities, we apply conformal prediction to generate robust predictive sets that guarantee true-label coverage with a given probability. Additionally, we incorporate various anomaly detection algorithms to empower our model with the ability to identify out-of-distribution objects. Our framework is implemented in the Deep-LC toolkit, which is an open-source Python package hosted on Github (https://github.com/ckm3/Deep-LC) and PyPI.\",\"PeriodicalId\":22368,\"journal\":{\"name\":\"The Astrophysical Journal Supplement Series\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"The Astrophysical Journal Supplement Series\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3847/1538-4365/ad62fd\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"The Astrophysical Journal Supplement Series","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3847/1538-4365/ad62fd","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

各种项目产生了大量的天文测光数据,需要花费大量精力来识别变星和其他天体类别。有鉴于此,一个通用、广泛适用的分类框架将简化为各种天体设计特定分类器的过程。我们提出了一个新颖的深度学习框架,利用弱监督天体检测模型对光变曲线进行分类。我们的框架能自动识别光曲线和功率谱的最佳窗口,并放大相应的数据。这样就能从时域和频域自动提取特征,使我们的模型能够处理不同尺度和采样间隔的数据。我们在开普勒、TESS 和 Zwicky 瞬变设施对变星和瞬变的多波段观测数据集上训练我们的模型。我们对变星和瞬变事件的综合准确率达到了 87%,与之前基于特征的模型性能相当。我们训练有素的模型可以直接用于其他任务,如超新星全天空自动巡天,而不需要任何重新训练或微调。为了解决已知的预测概率失准问题,我们应用保形预测来生成稳健的预测集,保证以给定概率覆盖真实标签。此外,我们还结合了各种异常检测算法,使我们的模型具有识别分布外对象的能力。我们的框架是在 Deep-LC 工具包中实现的,该工具包是一个开源 Python 软件包,托管在 Github (https://github.com/ckm3/Deep-LC) 和 PyPI 上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying Light-curve Signals with a Deep-learning-based Object Detection Algorithm. II. A General Light-curve Classification Framework
Vast amounts of astronomical photometric data are generated from various projects, requiring significant effort to identify variable stars and other object classes. In light of this, a general, widely applicable classification framework would simplify the process of designing specific classifiers for various astronomical objects. We present a novel deep-learning framework for classifying light curves using a weakly supervised object detection model. Our framework identifies the optimal windows for both light curves and power spectra automatically, and zooms in on their corresponding data. This allows for automatic feature extraction from both time and frequency domains, enabling our model to handle data across different scales and sampling intervals. We train our model on data sets obtained from Kepler, TESS, and Zwicky Transient Facility multiband observations of variable stars and transients. We achieve an accuracy of 87% for combined variable and transient events, which is comparable to the performance of previous feature-based models. Our trained model can be utilized directly for other missions, such as the All-sky Automated Survey for Supernovae, without requiring any retraining or fine-tuning. To address known issues with miscalibrated predictive probabilities, we apply conformal prediction to generate robust predictive sets that guarantee true-label coverage with a given probability. Additionally, we incorporate various anomaly detection algorithms to empower our model with the ability to identify out-of-distribution objects. Our framework is implemented in the Deep-LC toolkit, which is an open-source Python package hosted on Github (https://github.com/ckm3/Deep-LC) and PyPI.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信