Performance results of HESP physical model

TL;DR

This paper evaluates the performance of a model-based calibration technique for the Hanle Echelle Spectrograph (HESP), demonstrating high accuracy in predicting spectral drifts and calibration, with implications for improved spectrograph stability and science applications.

Contribution

It presents the performance results of a model-based calibration method for HESP, including optimization strategies, handling of glass data discrepancies, and comparison with traditional calibration methods.

Findings

High accuracy in predicting spectral drifts due to environmental changes

Effective tracking of instrumental drifts and spectral pattern variations

Model's ability to improve calibration stability and predictability

Abstract

As a continuation to the published work on model based calibration technique with HESP(Hanle Echelle Spectrograph) as a case study, in this paper we present the performance results of the technique. We also describe how the open parameters were chosen in the model for optimization, the glass data accuracy and handling the discrepancies. It is observed through simulations that the discrepancies in glass data can be identified but not quantifiable. So having an accurate glass data is important which is possible to obtain from the glass manufacturers. The model's performance in various aspects is presented using the ThAr calibration frames from HESP during its pre-shipment tests. Accuracy of model predictions and its wave length calibration comparison with conventional empirical fitting, the behaviour of open parameters in optimization, model's ability to track instrumental drifts in the spectrum and the double fibres performance were discussed. It is observed that the optimized model is able to predict to a high accuracy the drifts in the spectrum from environmental fluctuations. It is also observed that the pattern in the spectral drifts across the 2D spectrum which vary from image to image is predictable with the optimized model. We will also discuss the possible science cases where the model can contribute.