数据驱动的星系族群先验用于光度红移
Data-driven Galaxy Population Prior for Photometric Redshifts
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中文总结 AI 辅助
本文提出纯数据驱动的生成模型学习星系SED先验，用于光度红移校准，在受控条件下满足第四阶段巡天千分之一精度要求。
中文摘要 AI 辅助
第四阶段宇宙学巡天依赖于刻画良好的光度红移分布来约束宇宙学模型，然而其预期的弱引力透镜精度要求比当前最先进的方法高约一个数量级。任何光度红移推断都显式或隐式地依赖于星系SED、光度和红移的先验。在本文中，我们开发了一种纯数据驱动的方法来建模星系族群先验，基于学习星系SED的分布，并评估其引入的系统不确定性对光度红移校准的影响。在受控条件下，我们构建了一个无模板的星系SED模型，仅需最少的物理假设，并评估其再现颜色-红移关系的能力。我们在一个由GalSBI-SPS星系族群模型模拟的现实噪声模拟星系光谱族群上训练生成模型，以学习内禀光谱、光度和红移的联合分布。我们采用概率自编码器将光谱压缩到低维潜空间，并使用归一化流进行神经密度估计。该自编码器能以标准差σ的高斯噪声重建星系光谱形状，噪声水平约为真实值的0.1σ，从而仅从噪声光谱构建无噪声SED模型。我们使用自组织映射构建颜色选择的层析bin，并比较预测的平均红移。我们发现每个bin中的偏差小于千分之一的第四阶段要求，即|Δ⟨z⟩|≲0.0007(1+z)。这项工作作为概念验证，表明生成模型能够在受控条件下足够准确地学习星系观测的先验，以满足即将进行的巡天需求。
英文摘要
Stage-IV cosmological surveys rely on well-characterised photometric redshift distributions for constraining cosmological models, yet the projected weak lensing requirements are about an order of magnitude more accurate than current state-of-the-art methods. Any photometric redshift inference depends - explicitly or implicitly - on a prior over galaxy SEDs, luminosities, and redshifts. In this paper, we develop a purely data-driven approach for modelling the galaxy population prior based on learning the distribution of galaxy SEDs and evaluate its induced systematic uncertainties for photometric redshift calibration. Under controlled conditions, we build a template-free model for galaxy SEDs with minimal physical assumptions and assess how well it can reproduce the colour-redshift relation. We train a generative model on a realistic population of noisy mock galaxy spectra, simulated using the GalSBI-SPS galaxy population model, to learn the joint distribution of intrinsic spectra, luminosities, and redshifts. We employ a probabilistic autoencoder that compresses spectra into a low-dimensional latent space and performs neural density estimation with a normalising flow. The autoencoder can reconstruct the shape of galaxy spectra with Gaussian noise of standard deviation $σ$ to $\sim0.1\,σ$ of the ground truth, thereby building a model for noiseless SEDs from noisy spectra only. We construct colour-selected tomographic bins with a self-organising map and compare the predicted mean redshift. We find that deviations in each bin are smaller than the per-mille Stage-IV requirements, with $| Δ\langle z \rangle |\lesssim 0.0007 (1+z)$. This work serves as a proof-of-concept that generative models can learn the prior of galaxy observations accurately enough for upcoming surveys under controlled conditions.
发表机构
- Ludwig-Maximilians Universität München（慕尼黑大学）
- Excellence Cluster ORIGINS（ORIGINS卓越集群）
- Princeton University（普林斯顿大学）
机构由 AI 辅助整理，请以论文原文为准。
