CDS&E: Machine Learning for Star Cluster Classification

CDS

• 批准号: 1815267
• 负责人: Daniela Calzetti
• 金额: $ 25.17万
• 依托单位: University of Massachusetts Amherst
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-11-01 至 2021-10-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1815267&HistoricalAwards=false
• 关键词: CDS& Machine; Learning; Star; Cluster

This is a pilot project to compare and test human visual inspection versus machine learning (ML) and computer vision (CV) methods for identifying young star clusters (YSC) in high resolution images of galaxies. It is the first step towards exploring and optimizing the ML methods so as to build a tool capable of automatic search, classification, and shape measurement of YSC. This study will provide a launch-pad for the full project. It seems likely that ML tools will be the only viable way to handle the vast 'Big Data' databases becoming common in astronomy. An integrated educational component includes summer research for undergraduate students, including a valuable introduction to 'Big Data' issues.Initial tests will be performed on the two closest galaxies to our own Milky Way (M31 and M33), and then extended to M51 and NGC628, which are further away from us. These are well-studied galaxies for which high-fidelity catalogs already exist, which are available for comparison and calibration. The chosen test galaxies have very different cluster populations, and thus represent key testbeds to validate both the standard (human-based) approach and the future ML approach being developed. ML/CV algorithms to be explored and tested on these images include very deep convolutional neural networks, which will be adapted to provide collective classifications of star clusters. The human-based approach is currently the 'industry standard', and its validation will provide a more secure footing for future investigations of the physics of star formation in external galaxies.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这是一个试点项目，旨在比较和测试人类视觉检查与机器学习（ML）和计算机视觉（CV）方法，以识别高分辨率星系图像中的年轻星星团（YSC）。 这是探索和优化ML方法的第一步，从而建立一个能够自动搜索，分类和形状测量的工具YSC。 这项研究将为整个项目提供一个启动平台。 机器学习工具很可能是处理天文学中越来越常见的庞大“大数据”数据库的唯一可行方法。 一个综合的教育组成部分包括本科生的暑期研究，包括对“大数据”问题的有价值的介绍。最初的测试将在距离我们银河系最近的两个星系（M31和M33）上进行，然后扩展到距离我们更远的M51和NGC 628。 这些都是经过充分研究的星系，已经存在高保真度的目录，可用于比较和校准。 所选择的测试星系具有非常不同的星系团数量，因此代表了验证标准（基于人类的）方法和正在开发的未来ML方法的关键测试平台。 在这些图像上探索和测试的ML/CV算法包括非常深的卷积神经网络，它将被调整以提供星星集群的集体分类。 以人为本的方法是目前的“行业标准”，其验证将提供一个更安全的基础，为未来的调查物理学的星星形成在外部星系。这一奖项反映了NSF的法定使命，并已被认为是值得的支持，通过评估使用基金会的智力价值和更广泛的影响审查标准。

项目成果

StarcNet: Machine Learning for Star Cluster Identification

StarcNet：用于星团识别的机器学习

• DOI: 10.3847/1538-4357/abceba
• 发表时间: 2021
• 期刊: The Astrophysical Journal
• 作者: Pérez, Gustavo;Messa, Matteo;Calzetti, Daniela;Maji, Subhransu;Jung, Dooseok E.;Adamo, Angela;Sirressi, Mattia
• 通讯作者: Sirressi, Mattia