Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Accurately constraining velocity information from spectral imaging
  observations using machine learning techniques   Determining accurate plasma Doppler (line-of-sight) velocities from
spectroscopic measurements is a challenging endeavour, especially when weak
chromospheric absorption lines are often rapidly evolving and, hence, contain
multiple spectral components in their constituent line profiles. Here, we
present a novel method that employs machine learning techniques to identify the
underlying components present within observed spectral lines, before
subsequently constraining the constituent profiles through single or multiple
Voigt fits. Our method allows active and quiescent components present in
spectra to be identified and isolated for subsequent study. Lastly, we employ a
Ca II 8542 {\AA} spectral imaging dataset as a proof-of-concept study to
benchmark the suitability of our code for extracting two-component atmospheric
profiles that are commonly present in sunspot chromospheres. Minimisation tests
are employed to validate the reliability of the results, achieving median
reduced $\chi^2$ values equal to 1.03 between the observed and synthesised
umbral line profiles.

25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
22
The categories of this paper are: chao-dyn
19
The categories of this paper are: eess
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
17
The categories of this paper are: q-fin
0
The categories of this paper are: hep-ph
14
The categories of this paper are: math-ph