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arXiv 2406.04529astro-ph.CO
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迈向基于机器学习的精确测光Ia型超新星宇宙学

Towards Precision Photometric Type Ia Supernova Cosmology with Machine Learning

Helen Qu

备注：
PhD Dissertation
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AI总结：

本论文面向大规模测光巡天时代，提出SCONE超新星测光分类、Photo-zSNthesis纯测光红移估计及通用机器学习鲁棒性改进方法，为精确测光Ia型超新星宇宙学提供关键技术支持。

摘要：

暗能量和宇宙加速膨胀的革命性发现，是1999年仅用42颗Ia型超新星（SNe Ia）完成的。此后，大型时域巡天项目，如暗能量巡天（DES），已观测到数千颗更多的SNe Ia，而即将开展的鲁宾时空遗产巡天（LSST）和罗曼空间望远镜预计将在未来十年内提供数百万颗。这一前所未有的数据量可用于检验标准宇宙学模型。然而，从如此庞大的数据集中提取纯净的SNe Ia样本并获得精确红移将是一项挑战。对绝大多数发现的天体而言，光谱分类将无法实现，且只有25%的天体能获得光谱红移。本论文提出了一系列观测和方法学研究，旨在解决与这一新的测光SNe Ia宇宙学时代相关的问题。首先，我们提出了一种用于超新星测光分类的机器学习（ML）方法——SCONE。测光分类使没有光谱信息的超新星也能被归类，这是宇宙学分析的关键步骤。SCONE在区分模拟的SNe Ia与非Ia型超新星方面达到了99%以上的准确率，并已成为DES、LSST和罗曼望远镜分析流程的一部分。我们还表明，SCONE在初次发现当晚就能以75%的准确率对6种超新星类型进行分类，与文献中针对全相位超新星的结果相当。其次，我们研究了当前估计SNe Ia红移的方法，并提出了一种仅利用超新星测光数据提取红移信息的机器学习替代方案。Photo-zSNthesis是一种不依赖宿主星系的红移估计器，在LSST的整个红移范围内精度达到2%以内，这在文献中尚属首次。最后，我们聚焦于机器学习的鲁棒性，并展示了一种改进鲁棒性的通用方法，该方法在天体分类、野生动物识别和肿瘤检测方面取得了新的最先进结果。

英文摘要：

The revolutionary discovery of dark energy and accelerating cosmic expansion was made with just 42 type Ia supernovae (SNe Ia) in 1999. Since then, large synoptic surveys, e.g., Dark Energy Survey (DES), have observed thousands more SNe Ia and the upcoming Rubin Legacy Survey of Space and Time (LSST) and Roman Space Telescope promise to deliver millions in the next decade. This unprecedented data volume can be used to test concordance cosmology. However, extracting a pure SN Ia sample with accurate redshifts for such a large dataset will be a challenge. Spectroscopic classification will not be possible for the vast majority of discovered objects, and only 25% will have spectroscopic redshifts. This thesis presents a series of observational and methodological studies designed to address the questions associated with this new era of photometric SN Ia cosmology. First, we present a machine learning (ML) method for SN photometric classification, SCONE. Photometric classification enables SNe with no spectroscopic information to be categorized, a critical step for cosmological analysis. SCONE achieves 99+% accuracy distinguishing simulated SNe Ia from non-Ia SNe, and is a part of DES, LSST, and Roman analysis pipelines. We also show that SCONE can classify 6 SN types with 75% accuracy on the night of initial discovery, comparable to results in the literature for full-phase SNe. Next, we study current methods for estimating SN Ia redshifts and propose an ML alternative that uses SN photometry alone to extract redshift information. Photo-zSNthesis is a host galaxy-independent redshift estimator accurate to within 2% across the redshift range of LSST, a first in the literature. Finally, we focus on ML robustness and demonstrate a general method for improving robustness that achieves new state-of-the-art results on astronomical object classification, wildlife identification, and tumor detection.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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