标题： 迈向精确的光度Ia型超新星宇宙学的机器学习 显示英文标题

标题： Towards Precision Photometric Type Ia Supernova Cosmology with Machine Learning

摘要： 1999年，通过仅42个Ia型超新星（SNe Ia）的革命性发现，发现了暗能量和宇宙加速膨胀。 自那时以来，大型时域调查，例如暗能量调查（DES），已经观测到了更多数千个SNe Ia，而即将进行的鲁宾遗产空间与时间调查（LSST）和罗曼空间望远镜预计将在未来十年内提供数百万个。 这种前所未有的数据量可用于测试共识宇宙学。 然而，对于如此大规模的数据集，提取具有准确红移的纯SNe Ia样本将是一个挑战。 对于绝大多数发现的天体，光谱分类将不可行，只有25%的天体会有光谱红移。 本论文提出了一系列观测和方法研究，旨在解决这一新时代光度SNe Ia宇宙学相关的问题。 首先，我们提出了一种用于SNe光度分类的机器学习（ML）方法，SCONE。 光度分类使没有光谱信息的SNe能够被分类，这是宇宙学分析中的关键步骤。 SCONE在区分模拟的SNe Ia和非Ia SNe方面实现了99%以上的准确率，并且是DES、LSST和罗曼分析流程的一部分。 我们还表明，SCONE可以在初始发现的夜晚对6种SN类型进行75%准确率的分类，这与文献中全相位SNe的结果相当。 接下来，我们研究了当前估计SNe Ia红移的方法，并提出了一种基于SNe光度的ML替代方法，以提取红移信息。 Photo-zSNthesis是一种不依赖宿主星系的红移估计器，在LSST的红移范围内准确度达到2%，这是文献中的首次。 最后，我们关注ML的鲁棒性，并展示了一种提高鲁棒性的通用方法，在天文物体分类、野生动物识别和肿瘤检测方面达到了新的最先进的结果。 显示英文摘要

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.