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<!DOCTYPE html>
<html>
<head>
<script src="https://ajax.googleapis.com/ajax/libs/jquery/3.5.1/jquery.min.js"></script>
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<title>Pamplemousse</title>
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<h1 class="display-4 text-white mt-5 mb-2">Pamplemousse:</h1>
<h2 class="text-white mb-2">A set of machine learning tools for Integral Field Unit Spectral Analysis</h2>
<p class="lead mb-5 text-white-50">
Machine learning is rapidly becoming ubiquitous in astronomy. The Pamplemousse project is a collection of tools built specifically for the analysis of spectra
taken with the SITELLE instrument on the CFHT. The tools are developed by a team lead by Carter Rhea at the Université de Montréal. The core team includes Dr. Laurie Rousseau-Nepton,
Dr. Simon Prunet, and Dr. Julie Hlavacek-Larrondo. The source code (and trained models) can be found at
<a class="text-warning" href='https://github.com/sitelle-signals/Pamplemousse'>https://github.com/sitelle-signals/Pamplemousse</a>. If you wish to use this directly with
fitting spectral cubes, please consider using the analysis code Luci which has been developed specifically for the analysis of SITELLE data cubes.
Luci documentation can be found at <a class="text-warning" href="https://crhea93.github.io/LUCI/index.html">https://crhea93.github.io/LUCI/index.html</a>. The simple-to-use
installation instructions can be found here: <a class="text-warning" href="https://github.com/crhea93/LUCI">https://github.com/crhea93/LUCI</a>.
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<h4>
Please check out our <a href="examples.html" class="text-info">examples</a>. If you use any code here in your work,
or as an inspiration, please cite us! You can find information on our pretrained networks at <a href="NetworkLibrary.html">Network Library</a>.
These are also available on our github page at
<a href="https://github.com/sitelle-signals/Pamplemousse/tree/master/PREDICTORS">https://github.com/sitelle-signals/Pamplemousse/tree/master/PREDICTORS</a>.
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Our Projects
</h2>
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<h3 class="text-center font-weight-bold text-info">Click on the headers to learn about the projects using Pamplemousse!</h3>
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<h5 class="mb-0">
<button class="btn btn-link text-center" data-toggle="collapse" data-target="#collapseOne" aria-expanded="true" aria-controls="collapseOne">
<h3 class="text-center font-weight-bold text-light">Velocity And Broadening</h3>
</button>
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<div id="collapseOne" class="collapse" aria-labelledby="headingOne" data-parent="#accordion">
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<figure>
<img class="w-100" src="images/cnn.jpg" />
<figcaption>Fig. 3 - Schematic Diagram of the Convolutional Neural Network employed in this work</figcaption>
</figure>
<p>
The <a href="https://arxiv.org/abs/2008.08093">first paper</a> explores the use of a convolutional neural network to extract the flux and velocity of underlying components.
We report a standard deviation of ∼5 km/s for the velocity parameter and a standard deviation of approximately 5.5 km/s for the broadening parameter.
</p>
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<h3 class="text-center font-weight-bold text-light">HII Region Line Ratios</h3>
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<figure>
<img class="w-100" src="images/Network.jpg" />
<figcaption>Fig. 5 - Artificial Neural Network created for this work.</figcaption>
</figure>
<p>
In <a href="https://arxiv.org/abs/2102.06230"> this work</a>, we apply an artificial neural network to combined-filter (SN1, SN2, and SN3) SITELLE data representing typical SIGNALS large program observations.
The network is designed to calculate important emission-line ratios for HII-like regions which are present in the primary SITELLE filters.
Our resultsindicate that the network can potentially constrain the line ratios with greater precision than the standard line fitting technique implemented in ORCS \textbf{if the source spectral properties are well represented in the training set}.
Timing analysis indicates that the network can analyze the entire cube approximately 100 times faster than the standard methods.
</p>
<figure>
<img class="w-100" src="images/Ratio-Errors.jpg" />
<figcaption>Fig. 6 - Line Ratio Errors for the 8 line ratios studied in this work.</figcaption>
</figure>
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<h3 class="text-center font-weight-bold text-light">Multiple Component Analysis</h3>
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<p>
In this paper (in preparation), the third of the series, we develop a convolutional neural network to classify spectra as having either a sin-gle or double line-of-sight component.
This systemati cmethod will be critical for disentangling components in merger systems, HII regions, and supernova remnants.We demonstrate that the network outperforms AIC andBayesian inference model comparisons.
</p>
<figure>
<img class="w-100" src="images/NGC2207.png" />
<figcaption>Fig. 8 - NGC2207-IC2163 merging system. Left: Deep SITELLE image of the N2207/IC2163 system created withORCS. This panel shows the stacked optical emission in the component galaxies.
Several structures such as the spiral arms, bulges, tidal tails, merging region (green circle), and diffuse emission regions (purple circles) stand out.
Right: Component map for the NGC2207/IC2163 system. White pixels correspond to double component emission. Black pixels correspond to single component emission.</figcaption>
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<h2 lang='en'>Contact Us</h2>
<hr>
<address>
<strong lang="en"><a href='https://github.com/sitelle-signals/Pamplemousse'>https://github.com/sitelle-signals/Pamplemousse</a></strong>
<br>Campus MIL
<br>L'Université de Montréal, QC, CA
<br>
</address>
<address>
<abbr title="Email">E:</abbr>
<a href="mailto:#">[email protected]</a>
</address>
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