We use hierarchical Bayesian modelling to calibrate a network of 32 all-sky faint DA white dwarf (DA WD) spectrophotometric standards ($16.5 < V < 19.5$) alongside the three CALSPEC standards, from 912 \r{A} to 32 $\mu$m. The framework is the first of its kind to jointly infer photometric zeropoints and WD parameters ($\log g$, $T_{\text{eff}}$, $A_V$, $R_V$) by simultaneously modelling both photometric and spectroscopic data. We model panchromatic HST/WFC3 UVIS and IR fluxes, HST/STIS UV spectroscopy and ground-based optical spectroscopy to sub-percent precision. Photometric residuals for the sample are the lowest yet yielding $<0.004$ mag RMS on average from the UV to the NIR, achieved by jointly inferring time-dependent changes in system sensitivity and WFC3/IR count-rate nonlinearity. Our GPU-accelerated implementation enables efficient sampling via Hamiltonian Monte Carlo, critical for exploring the high-dimensional posterior space. The hierarchical nature of the model enables population analysis of intrinsic WD and dust parameters. Inferred SEDs from this model will be essential for calibrating the James Webb Space Telescope as well as next-generation surveys, including Vera Rubin Observatory's Legacy Survey of Space and Time, and the Nancy Grace Roman Space Telescope.

翻译：我们采用分层贝叶斯建模方法，对由32颗全天区暗弱DA白矮星（DA WD）分光光度标准星（$16.5 < V < 19.5$）与三颗CALSPEC标准星组成的网络，在912 \r{A}至32 $\mu$m波长范围内进行校准。该框架是首个通过联合建模测光与光谱数据，同时推断测光零点与白矮星参数（$\log g$、$T_{\text{eff}}$、$A_V$、$R_V$）的模型。我们对泛色HST/WFC3 UVIS与IR流量、HST/STIS紫外光谱以及地基光学光谱进行了亚百分比精度的建模。通过联合推断系统灵敏度随时间的变化以及WFC3/IR计数率非线性，样本的测光残差达到迄今最低水平，从紫外到近红外波段平均均方根误差小于0.004星等。我们基于GPU加速的实现通过哈密顿蒙特卡洛方法实现了高效采样，这对于探索高维后验空间至关重要。模型的分层特性使得对白矮星本征参数与尘埃参数进行群体分析成为可能。该模型推断出的光谱能量分布对于校准詹姆斯·韦伯空间望远镜以及下一代巡天项目（包括薇拉·鲁宾天文台的时空遗产巡天和南希·格雷斯·罗曼空间望远镜）至关重要。