论文标题
GIGA镜头：快速的贝叶斯推断，对强力镜头建模
GIGA-Lens: Fast Bayesian Inference for Strong Gravitational Lens Modeling
论文作者
论文摘要
我们提出了千兆镜头：一种梯度信息，加速了GPU加速的贝叶斯框架，用于建模强力重力透镜系统，该框架以张曲流和JAX实现。使用多启动梯度下降，后验协方差估计的三个组件进行了优化，并通过Hamiltonian Monte Carlo进行采样，都通过自动分化和对图形处理单元（GPU）的自动分化和大量并行化来利用梯度信息。我们测试了大量模拟系统的管道，并详细说明了其高度性能。在四个NVIDIA A100 GPU上建模单个系统的平均时间为105秒。该框架提供的可靠性，速度和可扩展性使得对当前调查中发现的大量强镜头进行建模，并为$ \ Mathcal {o}（10^5）$镜头系统建模提供了非常有前途的前景，预计将在Vera C. Rubin C. Rubin observatory，Euclid，Euclid，Euclid，Euclid，Euclid和Nancy grace telescope中发现。
We present GIGA-Lens: a gradient-informed, GPU-accelerated Bayesian framework for modeling strong gravitational lensing systems, implemented in TensorFlow and JAX. The three components, optimization using multi-start gradient descent, posterior covariance estimation with variational inference, and sampling via Hamiltonian Monte Carlo, all take advantage of gradient information through automatic differentiation and massive parallelization on graphics processing units (GPUs). We test our pipeline on a large set of simulated systems and demonstrate in detail its high level of performance. The average time to model a single system on four Nvidia A100 GPUs is 105 seconds. The robustness, speed, and scalability offered by this framework make it possible to model the large number of strong lenses found in current surveys and present a very promising prospect for the modeling of $\mathcal{O}(10^5)$ lensing systems expected to be discovered in the era of the Vera C. Rubin Observatory, Euclid, and the Nancy Grace Roman Space Telescope.
