Signal detection for spectroscopy and polarimetry

TL;DR

This paper introduces a Bayesian non-parametric method using relevance vector machines for detecting weak spectropolarimetric signals in noisy high-resolution spectral data, aiding space-based observations.

Contribution

It presents a novel Bayesian detection technique specifically designed for spectropolarimetric data analysis using relevance vector machines.

Findings

Effective detection of weak signals in noisy data.

Applicable to space mission instruments like CLASP.

Provides probabilistic evidence for signal presence.

Abstract

The analysis of high spectral resolution spectroscopic and spectropolarimetric observations constitute a very powerful way of inferring the dynamical, thermodynamical, and magnetic properties of distant objects. However, these techniques are photon-starving, making it difficult to use them for all purposes. One of the problems commonly found is just detecting the presence of a signal that is buried on the noise at the wavelength of some interesting spectral feature. This is specially relevant for spectropolarimetric observations because typically, only a small fraction of the received light is polarized. We present in this note a Bayesian technique for the detection of spectropolarimetric signals. The technique is based on the application of the non-parametric relevance vector machine to the observations, which allows us to compute the evidence for the presence of the signal and compute the more probable signal. The method would be suited for analyzing data from experimental instruments onboard space missions and rockets aiming at detecting spectropolarimetric signals in unexplored regions of the spectrum such as the Chromospheric Lyman-Alpha Spectro-Polarimeter (CLASP) sounding rocket experiment.