Nouvelles observations et techniques d'apprentissage automatique appliquées aux galaxies et aux amas de galaxies

Galaxy clusters are one of the largest structures in the universe and host several complex physical phenomena. Although a wealth of knowledge already exists on their formation and evolution, the recent advent of machine learning in the astronomical sciences has allowed us to probe questions heretofore unanswered. While this thesis does focus heavily on the application of machine learning techniques to X-ray observations of galaxy clusters, it takes the techniques applied there to galaxy cluster's smaller counterparts: the individual galaxies themselves. Although the three papers presented here focus on distinct physics, scales, and techniques, they all form a basis of studies that I will continue during my doctorate: using new techniques to probe the underlying physics of galactic and extragalactic regions. The first paper introduced is a study of a galaxy cluster near the beginning of the epoch of cluster formation exhibiting peculiar attributes such as an elevated stellar formation rate (∽ 900M⊙/yr). In this paper, we employ traditional techniques in X-ray astronomy to determine whether or not the prodigious formation rate is due to an uninhibited cooling core. Since the object is so distant (z=1.7), we must execute our calculations with relatively few photons and thus rely on proxy measures. We determine that there exists a strong cooling flow offset by over 50 kpc from the central galaxy. Because of this offset, the AGN is not fueled and thus fails to heat up the cooling flow. This is the first example of a galaxy cluster in which we observe the failure of AGN feedback. Additionally, this provides another mechanism for the creation of intracluster light. The second article presented here focuses on X-ray emission from the hot intra-cluster medium within the galaxy cluster. We develop a comprehensive method for determining the number of underlying thermal components in the plasma's spectra. Our novel technique relies on a combination of an unsupervised learning algorithm (principal component analysis) and a supervised learning algorithm (random forest classification). We create a set of 100,000 realistic mock Chandra observations of the hot X-ray emitting gas in nearby galaxy clusters. After reducing our synthetic training set to its 25 most important principal components, we trained a random forest classifier to distinguish between the number of underlying thermal components. After successful testing and hyperparameter optimization, we applied the methodology third paper featured in this thesis once again employs machine learning to solve a previously undetermined question necessary for the accurate characterization of the kinematics of the warm gas in galaxies. We constructed a convolutional neural network to estimate the velocity and broadening parameters from the optical spectra of emission-line nebula and applied it to synthetic data replicating real SITELLE observations from the SIGNALS program. With a trained and optimized network in hand, we apply our methodology to a target of the SIGNALS program: the galaxy M33. Our results indicate our algorithm out-performs previous methods and nicely complements spectral fitting procedures. Moreover, the methodology increases calculation speeds by more than an order of magnitude -- thus greatly reducing the time needed to determine the kinematic parameters. Although the algorithm was trained for SITELLE data, this method can be readily ported to other IFUs such as MUSE. I have led two of the papers presented in this memoire and made major contributions to the third. All three papers have been either accepted for publication or have already been submitted and revised once. to the Perseus Cluster. In addition to creating a map of the cluster indicating the number of thermal components required to accurately model the thermal emission, we developed a suite of analysis routines to determine the temperatures of the underlying components. Our results are in agreement with over a decade of studies on the Perseus cluster and indicate that multiple temperature components are required for the accurate study of its intracluster medium.

Keyphrases: Machine Learning, Galaxy cluster, HII regions, Principal component analysis, Convolutional neural network, Interstellar medium, Intracluster medium, X-ray analysis