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Interpretable and Deblended Photometric Redshifts with a Deep Capsule Network

使用深胶囊网络可解释和去混合的光度红移

基本信息

批准号：
2009251
负责人：
Brett Andrews
金额：
$ 53.56万
依托单位：
University of Pittsburgh
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2009251&HistoricalAwards=false
关键词：
Interpretable Deblended Photometric Redshifts Deep

项目摘要

Studies of cosmology, galaxy evolution, and galaxy clustering all critically depend on redshift measurements. However, spectroscopic redshifts can only be obtained for a small fraction of the galaxies detected in current and next-generation deep imaging surveys, so most redshifts must be estimated from the images alone. This project introduces a promising new neural network architecture, called a deep capsule network, which will leverage pixel-level information from imaging surveys to estimate photometric redshifts. Principal objectives are 1) achieving state-of-the-art accuracy on a common wide-field test data set; 2) extending these methods to higher redshifts using Legacy Survey imaging and Dark Energy Spectroscopic Instrument data; and 3) combining the resolved optical imaging with integrated multiwavelength photometry in the ultraviolet and infrared, focusing on very low redshift galaxies that might host gravitational wave sources. The work includes graduate and undergraduate research, and a summer research boot camp and weekly seminar series. All course materials from the bootcamp will be made publicly available with open access.Estimating photometric redshifts is a well-posed problem for machine learning algorithms because spectroscopic redshifts can provide definitive measurements for training. The pooling operation that fueled the widespread success of convolutional neural networks throws away fine-grained spatial information and ultimately limits their ability to generalize to novel viewpoints and to parse blended objects. The deep capsule network overcomes these drawbacks of convolutional neural networks, and is much more easily interpreted. Projects to be carried out, and suitable for student research, include combining Sloan capsule network results with other photo-z methods to improve overall accuracy, and applying capsule networks to simulated data specifically to test performance on blended objects.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.

宇宙学、星系演化和星系团的研究都严重依赖于红移测量。然而，光谱红移只能从当前和下一代深度成像巡天中探测到的一小部分星系中获得，因此大多数红移必须单独从图像中估计。该项目引入了一种很有前途的新神经网络架构，称为深胶囊网络，它将利用来自成像调查的像素级信息来估计光度红移。主要目标是：1）在一个普通的宽场测试数据集上达到最先进的精度; 2）使用传统巡天成像和暗能量分光仪器数据将这些方法扩展到更高的红移; 3）将分辨的光学成像与紫外和红外的综合多波长测光结合起来，重点关注可能拥有引力波源的非常低的红移星系。这项工作包括研究生和本科生的研究，和夏季研究靴子营和每周研讨会系列。训练营的所有课程材料都将开放获取。对于机器学习算法来说，估算光度红移是一个适定性问题，因为光谱红移可以为训练提供明确的测量结果。推动卷积神经网络取得广泛成功的池化操作丢弃了细粒度的空间信息，并最终限制了它们推广到新观点和解析混合对象的能力。深度胶囊网络克服了卷积神经网络的这些缺点，并且更容易解释。即将开展的适合学生研究的项目包括将斯隆胶囊网络结果与其他photo-z方法相结合，以提高整体精度，并将胶囊网络应用于模拟数据，专门测试混合对象的性能。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（3）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Calibrated Predictive Distributions for Photometric Redshifts

光度红移的校准预测分布

DOI：
发表时间：
2022
期刊：
proceedings of the Thirty-ninth International Conference on Machine Learning (ICML 2022
影响因子：
0
作者：
Dey, Biprateep;Zhao, David;Andrews, Brett;Newman, Jeffrey;Izbicki, Rafael;Lee, Ann
通讯作者：
Lee, Ann

Re-calibrating Photometric Redshift Probability Distributions Using Feature-space Regression

使用特征空间回归重新校准光度红移概率分布

DOI：
发表时间：
2021
期刊：
Advances in neural information processing systems
影响因子：
0
作者：
Dey, Biprateep;Newman, Jeffrey A.;Andrews, Brett H.;Izbicki, Rafael;Lee, Ann B.;Zhao, David;Rau, Markus Michael;Malz, Alex I.
通讯作者：
Malz, Alex I.

Photometric redshifts from SDSS images with an interpretable deep capsule network