A Machine Learning Approach to Integral Field Unit Spectroscopy Observations: II. HII Region LineRatios. (arXiv:2102.06230v1 [astro-ph.GA])
<a href="http://arxiv.org/find/astro-ph/1/au:+Rhea_C/0/1/0/all/0/1">Carter Rhea</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:+Prunet_S/0/1/0/all/0/1">Simon Prunet</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Prasow_Emond_M/0/1/0/all/0/1">Myriam Prasow-Emond</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:+Asari_N/0/1/0/all/0/1">Natalia Vale Asari</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Grasha_K/0/1/0/all/0/1">Kathryn Grasha</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Perreault_Levasseur_L/0/1/0/all/0/1">Laurence Perreault-Levasseur</a>
In the first paper of this series (Rhea et al. 2020), we demonstrated that
neural networks can robustly and efficiently estimate kinematic parameters for
optical emission-line spectra taken by SITELLE at the Canada-France-Hawaii
Telescope. This paper expands upon this notion by developing an artificial
neural network to estimate the line ratios of strong emission-lines present in
the SN1, SN2, and SN3 filters of SITELLE. We construct a set of 50,000
synthetic spectra using line ratios taken from the Mexican Million Model
database replicating Hii regions. Residual analysis of the network on the test
set reveals the network’s ability to apply tight constraints to the line
ratios. We verified the network’s efficacy by constructing an activation map,
checking the [N ii] doublet fixed ratio, and applying a standard k-fold
cross-correlation. Additionally, we apply the network to SITELLE observation of
M33; the residuals between the algorithm’s estimates and values calculated
using standard fitting methods show general agreement. Moreover, the neural
network reduces the computational costs by two orders of magnitude. Although
standard fitting routines do consistently well depending on the signal-to-noise
ratio of the spectral features, the neural network can also excel at
predictions in the low signal-to-noise regime within the controlled environment
of the training set as well as on observed data when the source spectral
properties are well constrained by models. These results reinforce the power of
machine learning in spectral analysis.
In the first paper of this series (Rhea et al. 2020), we demonstrated that
neural networks can robustly and efficiently estimate kinematic parameters for
optical emission-line spectra taken by SITELLE at the Canada-France-Hawaii
Telescope. This paper expands upon this notion by developing an artificial
neural network to estimate the line ratios of strong emission-lines present in
the SN1, SN2, and SN3 filters of SITELLE. We construct a set of 50,000
synthetic spectra using line ratios taken from the Mexican Million Model
database replicating Hii regions. Residual analysis of the network on the test
set reveals the network’s ability to apply tight constraints to the line
ratios. We verified the network’s efficacy by constructing an activation map,
checking the [N ii] doublet fixed ratio, and applying a standard k-fold
cross-correlation. Additionally, we apply the network to SITELLE observation of
M33; the residuals between the algorithm’s estimates and values calculated
using standard fitting methods show general agreement. Moreover, the neural
network reduces the computational costs by two orders of magnitude. Although
standard fitting routines do consistently well depending on the signal-to-noise
ratio of the spectral features, the neural network can also excel at
predictions in the low signal-to-noise regime within the controlled environment
of the training set as well as on observed data when the source spectral
properties are well constrained by models. These results reinforce the power of
machine learning in spectral analysis.
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