Inverse Surrogate Model of a Soft X-Ray Spectrometer using Domain Adaptation

TL;DR

This paper develops a robust inverse surrogate model for a soft X-ray spectrometer, utilizing domain adaptation and data augmentation to bridge the simulation-experiment gap with minimal real data, enabling automated alignment.

Contribution

It introduces a novel application of domain adaptation and data augmentation techniques to improve inverse modeling in spectrometry with limited experimental data.

Findings

Successfully predicts absolute coordinates for spectrometer alignment

Bridges simulation and experimental data with minimal real data

Enables automated experimentation in scientific instrumentation

Abstract

In this study, we present a method to create a robust inverse surrogate model for a soft X-ray spectrometer. During a beamtime at an electron storage ring, such as BESSY II, instrumentation and beamlines are required to be correctly aligned and calibrated for optimal experimental conditions. In order to automate these processes, machine learning methods can be developed and implemented, but in many cases these methods require the use of an inverse model which maps the output of the experiment, such as a detector image, to the parameters of the device. Due to limited experimental data, such models are often trained with simulated data, which creates the challenge of compensating for the inherent differences between simulation and experiment. In order to close this gap, we demonstrate the application of data augmentation and adversarial domain adaptation techniques, with which we can predict absolute coordinates for the automated alignment of our spectrometer. Bridging the simulation-experiment gap with minimal real-world data opens new avenues for automated experimentation using machine learning in scientific instrumentation.