  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.
