ALPACA I：通过可微透镜建模控制JWST时间延迟宇宙学中的源和PSF系统误差
ALPACA I: Controlling source and PSF systematics in JWST time-delay cosmography with differentiable lens modeling
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中文总结 AI 辅助
ALPACA是基于Herculens等的可微强透镜建模流水线，可缩短JWST时间延迟宇宙学的源与PSF系统误差控制的运行时间，降低$D_{\rm \textit{Δ}t}$偏差，支持可靠的百分位精度。
中文摘要 AI 辅助
时间延迟宇宙学提供了对哈勃常数（$H_0$）的独立探测，但要利用詹姆斯·韦布空间望远镜（JWST）的高分辨率成像，需要能高保真捕捉源星系复杂形态和点扩散函数（PSF）的强透镜模型。灵活的源重建和联合PSF重建计算成本高昂，这促使人们采用更严格的参数化方式，而这可能会对推断的时间延迟距离$D_{\rm \textit{Δ}t}$产生偏差。我们提出ALPACA，这是一种基于Herculens、Starred和JAX构建的半自动、GPU加速的可微强透镜建模流水线。ALPACA在单个似然中联合重建像素化相关场源、透镜质量和PSF，将每个系统的运行时间从数周缩短至不到15小时。我们使用由形态复杂的COSMOS星系源构建的逼真模拟JWST/NIRCam观测数据对该流水线进行验证：将存在缺陷的PSF（与真值的RMSE为12%）固定时，会使$D_{\rm \textit{Δ}t}$产生高达14%的偏差，而联合PSF重建将该偏差降低至百分位水平。我们进一步表明，在后验采样期间，可将重建的PSF核心与透镜参数一起进行边缘化处理。通过分层推断将10个透镜的模拟样本的每个透镜后验进行传播，我们在未出现显著偏差的情况下恢复了输入宇宙学模型。ALPACA表明，严格控制源和PSF系统误差在计算上是可行的，为JWST时代可靠的百分位精度提供了支持。
英文摘要
Time-delay cosmography provides an independent probe of the Hubble constant ($H_0$), but exploiting the high-resolution imaging of the James Webb Space Telescope (JWST) requires strong-lens models that capture complex source-galaxy morphologies and the point spread function (PSF) with high fidelity. Flexible source and joint PSF reconstruction are computationally expensive, motivating more rigid parameterizations that can bias the inferred time-delay distance $D_{Δt}$. We present ALPACA, a semi-automatic, GPU-accelerated, differentiable strong-lens modeling pipeline built on Herculens, Starred, and JAX. ALPACA jointly reconstructs a pixelated correlated-field source, the lens mass, and the PSF within a single likelihood, reducing per-system runtimes from weeks to less than 15 hours. We validate the pipeline on realistic mock JWST/NIRCam observations built from morphologically complex COSMOS galaxy sources. Holding an imperfect PSF (RMSE = 12% from truth) fixed biases $D_{Δt}$ by up to 14%, whereas joint PSF reconstruction reduces this bias to the percent level. We further show that the reconstructed PSF core can be marginalized over jointly with the lens parameters during posterior sampling. Propagating the per-lens posteriors of a ten-lens mock sample through hierarchical inference, we recover the input cosmology without significant bias. ALPACA shows that rigorous control of source and PSF systematics is computationally affordable, supporting reliable percent-level precision in the JWST era.
发表机构
- Université de Genève（日内瓦大学）
- Stanford University（斯坦福大学）
- SLAC National Accelerator Laboratory（SLAC国家加速器实验室）
机构由 AI 辅助整理，请以论文原文为准。
