Self-supervised learning with physics-aware neural networks – I. Galaxy model fitting

Abstract

ABSTRACT Estimating the parameters of a model describing a set of observations using a neural network is, in general, solved in a supervised way. In cases when we do not have access to the model’s true parameters, this approach can not be applied. Standard unsupervised learning techniques, on the other hand, do not produce meaningful or semantic representations that can be associated with the model’s parameters. Here we introduce a novel self-supervised hybrid network architecture that combines traditional neural network elements with analytic or numerical models, which represent a physical process to be learned by the system. Self-supervised learning is achieved by generating an internal representation equivalent to the parameters of the physical model. This semantic representation is used to evaluate the model and compare it to the input data during training. The semantic autoencoder architecture described here shares the robustness of neural networks while including an explicit model of the data, learns in an unsupervised way, and estimates, by construction, parameters with direct physical interpretation. As an illustrative application, we perform unsupervised learning for 2D model fitting of exponential light profiles and evaluate the performance of the network as a function of network size and noise.