LUCI: A Python package for SITELLE spectral analysis. (arXiv:2108.12428v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Rhea_C/0/1/0/all/0/1">Carter Lee Rhea</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Hlavacek_Larrondo_J/0/1/0/all/0/1">Julie Hlavacek-Larrondo</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Rousseau_Nepton_L/0/1/0/all/0/1">Laurie Rousseau-Nepton</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Vigneron_B/0/1/0/all/0/1">Benjamin Vigneron</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Guite_L/0/1/0/all/0/1">Louis-Simon Guit&#xe9;</a>

High-resolution optical integral field units (IFUs) are rapidly expanding our
knowledge of extragalactic emission nebulae in galaxies and galaxy clusters. By
studying the spectra of these objects — which include classic HII regions,
supernova remnants, planetary nebulae, and cluster filaments — we are able to
constrain their kinematics (velocity and velocity dispersion). In conjunction
with additional tools, such as the BPT diagram, we can further classify
emission regions based on strong emission-line flux ratios. LUCI is a
simple-to-use python module intended to facilitate the rapid analysis of IFU
spectra. LUCI does this by integrating well-developed pre-existing python tools
such as astropy and scipy with new machine learning tools for spectral analysis
(Rhea et al. 2020). Furthermore, LUCI provides several easy-to-use tools to
access and fit SITELLE data cubes.

High-resolution optical integral field units (IFUs) are rapidly expanding our
knowledge of extragalactic emission nebulae in galaxies and galaxy clusters. By
studying the spectra of these objects — which include classic HII regions,
supernova remnants, planetary nebulae, and cluster filaments — we are able to
constrain their kinematics (velocity and velocity dispersion). In conjunction
with additional tools, such as the BPT diagram, we can further classify
emission regions based on strong emission-line flux ratios. LUCI is a
simple-to-use python module intended to facilitate the rapid analysis of IFU
spectra. LUCI does this by integrating well-developed pre-existing python tools
such as astropy and scipy with new machine learning tools for spectral analysis
(Rhea et al. 2020). Furthermore, LUCI provides several easy-to-use tools to
access and fit SITELLE data cubes.

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