标题： 宇宙微透镜放大图的自编码器重构 显示英文标题

标题： Autoencoder Reconstruction of Cosmological Microlensing Magnification Maps

摘要： 对强引力透镜化类星体光变曲线中的微引力透镜变化进行增强建模，可以改善宇宙时间延迟、哈勃常数以及类星体结构的测量。 传统方法依赖于计算成本高昂的放大图生成。 随着像维拉·C·鲁宾时空遗产巡天这样的广域巡天预计会产生大量数据，包括数千个引力透镜化类星体和数百个多重成像超新星，更快的方法变得至关重要。 我们引入了一种深度学习模型，该模型在预先计算的放大图上进行训练，这些放大图覆盖了参数空间的k、g和s网格。我们的自动编码器创建了这些地图的低维潜在空间表示，从而实现了高效的地图生成。 量化从低维空间生成放大图的性能是开发基于神经网络模型的重要一步，这些模型可以在更低的计算成本下取代传统的前馈模拟。 我们开发了度量标准来研究自动编码器生成的地图的各种方面，并表明重建是可靠的。 尽管我们观察到生成的地图分辨率有轻微损失，但我们发现这种影响比用一个合理的大小对光学光谱红端及其更大波长范围内的吸积盘进行平滑处理的效果小，尤其是适合研究类星体宽线区的一个。 用于生成大批量需求的放大图，我们的模型可以实现快速建模透镜化类星体和超新星的微引力透镜变化。 显示英文摘要

摘要： Enhanced modeling of microlensing variations in light curves of strongly lensed quasars improves measurements of cosmological time delays, the Hubble Constant, and quasar structure. Traditional methods for modeling extra-galactic microlensing rely on computationally expensive magnification map generation. With large datasets expected from wide-field surveys like the Vera C. Rubin Legacy Survey of Space and Time, including thousands of lensed quasars and hundreds of multiply imaged supernovae, faster approaches become essential. We introduce a deep-learning model that is trained on pre-computed magnification maps covering the parameter space on a grid of k, g, and s. Our autoencoder creates a low-dimensional latent space representation of these maps, enabling efficient map generation. Quantifying the performance of magnification map generation from a low dimensional space is an essential step in the roadmap to develop neural network-based models that can replace traditional feed-forward simulation at much lower computational costs. We develop metrics to study various aspects of the autoencoder generated maps and show that the reconstruction is reliable. Even though we observe a mild loss of resolution in the generated maps, we find this effect to be smaller than the smoothing effect of convolving the original map with a source of a plausible size for its accretion disk in the red end of the optical spectrum and larger wavelengths and particularly one suitable for studying the Broad-Line Region of quasars. Used to generate large samples of on-demand magnification maps, our model can enable fast modeling of microlensing variability in lensed quasars and supernovae.