评估用于测光红移估计的物理动机损失函数
Evaluating Physically Motivated Loss Functions for Photometric Redshift Estimation
AI总结：
本研究评估在用于斯隆数字巡天星系图像测光红移估计的神经网络中添加物理动机软约束项的效果，通过消融实验对比发现，此类软约束在正则化效果上并不优于数据增强。
摘要：
物理约束被认为能使神经网络模型比无约束基线更具泛化性、科学行为合理性及数据高效性。在本报告中，我们展示了初步工作，评估在计算机视觉神经网络上添加软物理约束的效果，这些网络经训练用于根据斯隆数字巡天（Sloan Digital Sky Survey）输入星系图像估计红移的条件密度。我们引入了不使用微分或积分算子实现的物理动机软约束项。我们将此工作构建为一项简单的消融研究，将包含软物理约束的效果与无约束基线进行比较。我们使用测光红移估计的标准点估计指标，以及评估条件密度估计在测试数据集集合上表示概率的忠实程度的指标来比较网络。我们发现没有证据表明所实现的软物理约束比数据增强是更有效的正则化器。
英文摘要：
Physical constraints have been suggested to make neural network models more generalizable, act scientifically plausible, and be more data-efficient over unconstrained baselines. In this report, we present preliminary work on evaluating the effects of adding soft physical constraints to computer vision neural networks trained to estimate the conditional density of redshift on input galaxy images for the Sloan Digital Sky Survey. We introduce physically motivated soft constraint terms that are not implemented with differential or integral operators. We frame this work as a simple ablation study where the effect of including soft physical constraints is compared to an unconstrained baseline. We compare networks using standard point estimate metrics for photometric redshift estimation, as well as metrics to evaluate how faithful our conditional density estimate represents the probability over the ensemble of our test dataset. We find no evidence that the implemented soft physical constraints are more effective regularizers than augmentation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
