用无监督域适应提升神经网络对真实引力透镜的预测精度与不确定性估计。
Neural Network Prediction of Strong Lensing Systems with Domain Adaptation and Uncertainty Quantification
- 结合无监督域适应与均值方差估计，提升模型在真实观测数据上的表现。
- 加入域适应后目标数据精度提升约2倍，不确定性预测更准确。
- 适合从事天文图像分析与不确定性建模的研究者参考。
现代及下一代宇宙学巡天产生的复杂数据使得强引力透镜建模计算成本高昂。深度学习为快速发现透镜并预测透镜参数（如爱因斯坦半径）提供了新途径。均值方差估计器（MVE）是获取神经网络预测中数据不确定性（即然性不确定性）的常用方法。然而，现有神经网络在从模拟数据训练后应用于真实观测数据时表现不佳。本文首次研究了将MVE与无监督域适应（UDA）结合在强引力透镜数据上的有效性。源域数据为无噪声模拟数据，目标域数据含噪声，模拟现代宇宙学巡天。实验表明，在MVE基础上引入UDA，使目标数据上的预测精度提高约2倍，且不确定性估计更加校准。该方法推进了未来将MVE模型应用于真实观测数据的可能性。
原文摘要 · Abstract (English)
Modeling strong gravitational lenses is computationally expensive for the complex data from modern and next-generation cosmic surveys. Deep learning has emerged as a promising approach for finding lenses and predicting lensing parameters, such as the Einstein radius. Mean-variance Estimators (MVEs) are a common approach for obtaining aleatoric (data) uncertainties from a neural network prediction. However, neural networks have not been demonstrated to perform well on out-of-domain target data successfully - e.g., when trained on simulated data and applied to real, observational data. In this work, we perform the first study of the efficacy of MVEs in combination with unsupervised domain adaptation (UDA) on strong lensing data. The source domain data is noiseless, and the target domain data has noise mimicking modern cosmology surveys. We find that adding UDA to MVE increases the accuracy on the target data by a factor of about two over an MVE model without UDA. Including UDA also permits much more well-calibrated aleatoric uncertainty predictions. Advancements in this approach may enable future applications of MVE models to real observational data.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。