Understanding the predication mechanism of deep learning through error propagation among parameters in strong lensing case

Abstract

Abstract The error propagation among estimated parameters reflects the correlation among the parameters. We study the capability of machine learning of ``learning" the correlation of estimated parameters. We show that machine learning can recover the relation between the uncertainties of different parameters, especially, as predicted by the error propagation formula. As a proof-of-principle test, a toy model of linear relation with Gaussian noise is presented. We found that the predictions obtained by machine learning indeed indicate the information about the law of error propagation and the distribution of noise. Gravitational lensing can be used to probe both astrophysics and cosmology. As a practical application, we show that the machine learning is able to intelligently find the error propagation among the gravitational lens parameters (effective lens mass $M_{L}$ and Einstein radius $\theta_{E}$) in accordance with the theoretical formula for the singular isothermal ellipse (SIE) lens model. The relation of errors of lens mass and Einstein radius, (e.g. the ratio of standard deviations $\mathcal{F}=\sigma_{\hat{ M_{L}}}/ \sigma_{\hat{\theta_{E}}}$) predicted by the deep convolution neural network are consistent with the error propagation formula of SIE lens model. Error propagation plays a crucial role in identifying the physical relation among parameters, rather than a coincidence relation, therefore we anticipate our case study on the error propagation of machine learning predictions could extend to other physical systems on searching the correlation among parameters.