A Bayesian approach to the inference of parametric configuration of the signal-to-noise ratio in an adaptive refinement of the measurements

TL;DR

This paper introduces a Bayesian method to estimate configurable parameters affecting the signal-to-noise ratio in measurements, aiming to improve data calibration and adaptive measurement strategies in cosmological surveys.

Contribution

It presents a novel Bayesian inference approach for calibrating instrument parameters related to noise, enhancing measurement accuracy in heterogeneous cosmological data collection.

Findings

Bayesian inference effectively estimates noise-related parameters.

Adaptive calibration improves measurement quality.

Method applicable to multi-instrument, multi-wavelength data.

Abstract

Calibration is nowadays one of the most important processes involved in the extraction of valuable data from measurements. The current availability of an optimum data cube measured from a heterogeneous set of instruments and surveys relies on a systematic and robust approach in the corresponding measurement analysis. In that sense, the inference of configurable instrument parameters can considerably increase the quality of the data obtained. This paper proposes a solution based on Bayesian inference for the estimation of the configurable parameters relevant to the signal to noise ratio. The information obtained by the resolution of this problem can be handled in a very useful way if it is considered as part of an adaptive loop for the overall measurement strategy, in such a way that the outcome of this parametric inference leads to an increase in the knowledge of a model comparison problem in the context of the measurement interpretation. The context of this problem is the multi-wavelength measurements coming from diverse cosmological surveys and obtained with various telescope instruments. As a first step,, a thorough analysis of the typical noise contributions will be performed based on the current state-of-the-art of modern telescope instruments, a second step will then consist of identifying configurable parameters relevant to the noise model under consideration, for a generic context of measurement chosen. Then as a third step a Bayesian inference for these parameters estimation will be applied, taking into account a proper identification of the nuisance parameters and the adequate selection of a prior probability. Finally, a corresponding set of conclusions from the results of the implementation of the method proposed here will be derived