DECam 局部体积探测测量数据第 2 版中的递归神经网络光度红移概率密度估计

IF 1.9 4区 物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS Astronomy and Computing Pub Date : 2024-10-01 DOI:10.1016/j.ascom.2024.100886
G. Teixeira , C.R. Bom , L. Santana-Silva , B.M.O. Fraga , P. Darc , R. Teixeira , J.F. Wu , P.S. Ferguson , C.E. Martínez-Vázquez , A.H. Riley , A. Drlica-Wagner , Y. Choi , B. Mutlu-Pakdil , A.B. Pace , J.D. Sakowska , G.S. Stringfellow
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引用次数: 0

摘要

测光宽视场巡天正在以前所未有的细节对天空进行成像。这些巡天在高效估算星系光度红移的同时准确量化相关的不确定性方面面临着巨大的挑战。在这项工作中,我们通过探索在 17,000 平方度的广阔区域内估算星系光度红移的概率密度函数(PDF)来应对这一挑战,该区域涵盖了中位 5σ 点源深度为 g = 24.3、r=23.9、i = 23.5 和 z = 22.8 等的天体。我们的方法使用了深度学习,特别是将循环神经网络架构与混合密度网络相整合,利用星等和颜色作为输入特征,在整个DECam局部体积探测(DELVE)巡天足迹中构建光度红移PDF。随后,我们对估算方法的可靠性和稳健性进行了严格评估,将其性能与其他成熟的机器学习方法进行对比,以确保红移估算的质量。我们的最佳结果约束了测光红移,偏差为-0.0013,散度为0.0293,离群分数为5.1%。这些点估计值都附有校准良好的 PDF,并使用概率积分变换和 Odds 分布等诊断工具进行了评估。我们还解决了 PDF 在磁盘空间存储方面的可访问性问题,以及生成其相应参数所需的时间要求。我们提出了一种新颖的自动编码器模型,可将 PDF 参数数组的大小减少到原来的六分之一,从而将生成 PDF 所需的时间大幅减少到直接从幅值生成 PDF 所需的八分之一。
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Photometric redshifts probability density estimation from recurrent neural networks in the DECam local volume exploration survey data release 2
Photometric wide-field surveys are imaging the sky in unprecedented detail. These surveys face a significant challenge in efficiently estimating galactic photometric redshifts while accurately quantifying associated uncertainties. In this work, we address this challenge by exploring the estimation of Probability Density Functions (PDFs) for the photometric redshifts of galaxies across a vast area of 17,000 square degrees, encompassing objects with a median 5σ point-source depth of g = 24.3, r=23.9, i = 23.5, and z = 22.8 mag. Our approach uses deep learning, specifically integrating a Recurrent Neural Network architecture with a Mixture Density Network, to leverage magnitudes and colors as input features for constructing photometric redshift PDFs across the whole DECam Local Volume Exploration (DELVE) survey sky footprint. Subsequently, we rigorously evaluate the reliability and robustness of our estimation methodology, gauging its performance against other well-established machine learning methods to ensure the quality of our redshift estimations. Our best results constrain photometric redshifts with the bias of 0.0013, a scatter of 0.0293, and an outlier fraction of 5.1%. These point estimates are accompanied by well-calibrated PDFs evaluated using diagnostic tools such as Probability Integral Transform and Odds distribution. We also address the problem of the accessibility of PDFs in terms of disk space storage and the time demand required to generate their corresponding parameters.We present a novel Autoencoder model that reduces the size of PDF parameter arrays to one-sixth of their original length, significantly decreasing the time required for PDF generation to one-eighth of the time needed when generating PDFs directly from the magnitudes.
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来源期刊
Astronomy and Computing
Astronomy and Computing ASTRONOMY & ASTROPHYSICSCOMPUTER SCIENCE,-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
4.10
自引率
8.00%
发文量
67
期刊介绍: Astronomy and Computing is a peer-reviewed journal that focuses on the broad area between astronomy, computer science and information technology. The journal aims to publish the work of scientists and (software) engineers in all aspects of astronomical computing, including the collection, analysis, reduction, visualisation, preservation and dissemination of data, and the development of astronomical software and simulations. The journal covers applications for academic computer science techniques to astronomy, as well as novel applications of information technologies within astronomy.
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