arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新，休息一下吧，周末愉快~~
arXiv 2602.01548astro-ph.GAastro-ph.CO
收藏

通过神经网络分类进行光度红移PDFs的估计：DESI遗产成像调查与Pan-STARRS

Photometric Redshift PDFs via Neural Network Classification for DESI Legacy Imaging Surveys and Pan-STARRS

Institute for Frontiers in Astronomy and Astrophysics, Beijing Normal University（北京师范大学前沿天文与天体物理研究所）；School of Physics and Astronomy, Beijing Normal University（北京师范大学物理与天文学院）；National Astronomical Observatories, Chinese Academy of Sciences（中国科学院国家天文台）

Da-Chuan Tian, Zhong-Lue Wen, Jun-Qing Xia

备注：
19 pages, 8 figures, accepted for publication in The Astrophysical Journal Supplement Series. Data available at https://doi.org/10.5281/zenodo.18410731
阅读 PDF
arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文提出一种神经网络分类方法，用于生成校准良好的红移概率密度函数，通过离散化红移空间并优化CRPS，提升了红移估计的精度和不确定性量化。

摘要：

我们提出了一种神经网络分类（NNC）方法，用于光度红移估计，能够生成校准良好的红移概率密度函数（PDFs）。该方法将红移空间离散化为有序的区间，并优化连续排名概率评分（CRPS），这尊重红移的有序性质，并自然地提供不确定性量化。与传统回归方法输出单点估计不同，我们的方法能够捕捉由于颜色-红移退化而产生的多模式后验分布。我们将该方法应用于DESI遗产成像调查数据释放10（LSDR10）和Pan-STARRS数据释放2（PS1DR2），使用了前所未有的光谱训练样本，来自DESI DR1和SDSS DR19。我们的方法在LSDR10上实现了σ_NMAD=0.0153和η=0.50%，在PS1DR2结合unWISE近红外光度学数据时实现了σ_NMAD=0.0222和η=0.34%。NNC方法在随机森林、XGBoost和标准神经网络回归方法上表现更优。我们证明了DESI DR1在z>1时显著提高了光度红移性能，而深光学光度学和中红外覆盖的结合对于在全红移范围内实现高精度至关重要。我们提供了一个结合LSDR10和PS1DR2的统一光度红移目录，基于可用光度学的分层模型选择策略。我们方法生成的校准良好的PDFs对宇宙学研究有价值，并可扩展到下一代调查，如CSST、Euclid和LSST。

英文摘要：

We present a neural network classification (NNC) method for photometric redshift estimation that produces well-calibrated redshift probability density functions (PDFs). The method discretizes the redshift space into ordered bins and optimizes the Continuous Ranked Probability Score (CRPS), which respects the ordinal nature of redshift and naturally provides uncertainty quantification. Unlike traditional regression approaches that output single point estimates, our method can capture multi-modal posterior distributions arising from color-redshift degeneracies. We apply this method to the DESI Legacy Imaging Surveys Data Release 10 (LSDR10) and Pan-STARRS Data Release 2 (PS1DR2), using an unprecedented spectroscopic training sample from DESI DR1 and SDSS DR19. Our method achieves $σ_{\mathrm{NMAD}} = 0.0153$ and $η= 0.50\%$ on LSDR10, and $σ_{\mathrm{NMAD}} = 0.0222$ and $η= 0.34\%$ on PS1DR2 combined with unWISE infrared photometry. The NNC method outperforms Random Forest, XGBoost, and standard neural network regression. We demonstrate that DESI DR1 significantly improves photo-$z$ performance at $z > 1$, while the combination of deep optical photometry and mid-infrared coverage is essential for achieving high precision across the full redshift range. We provide a unified photometric redshift catalog combining LSDR10 and PS1DR2 with a hierarchical model selection strategy based on available photometry. The well-calibrated PDFs produced by our method are valuable for cosmological studies and can be extended to next-generation surveys such as CSST, Euclid, and LSST.

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。

↑