Portrait de Julie Hlavacek-Larrondo

Julie Hlavacek-Larrondo

Membre académique associé
Professeur agrégé, Université de Montréal, Département de physique
Alumni
Sujets de recherche
Apprentissage automatique appliqué
Astronomie
Astrophysique
Astrophysique computationnelle
IA pour la science
Site web
Google Scholar
LinkedIn

Étudiants actuels

Jorge Barrera Barrera-Ballesteros
Visiteur de recherche indépendant - National Autonomous University of Mexico
Site web
Mathieu Marquis
Maîtrise recherche - UdeM
Github
Olivia Pereira
Maîtrise recherche - UdeM
Site web
Github
Marine Prunier
Doctorat - UdeM
Site web
Benjamin Vigneron
Doctorat - UdeM
Site web
Google Scholar

Publications

Galaxy cluster characterization with machine learning techniques
Maria Sadikov
Julie Hlavacek-Larrondo
Laurence Perreault-Levasseur
C. Rhea
Michael McDonald
Michelle Ntampaka
John ZuHone
We present an analysis of the X-ray properties of the galaxy cluster population in the z=0 snapshot of the IllustrisTNG simulations, utilizi… (voir plus)ng machine learning techniques to perform clustering and regression tasks. We examine five properties of the hot gas (the central cooling time, the central electron density, the central entropy excess, the concentration parameter, and the cuspiness) which are commonly used as classification metrics to identify cool core (CC), weak cool core (WCC) and non cool core (NCC) clusters of galaxies. Using mock Chandra X-ray images as inputs, we first explore an unsupervised clustering scheme to see how the resulting groups correlate with the CC/WCC/NCC classification based on the different criteria. We observe that the groups replicate almost exactly the separation of the galaxy cluster images when classifying them based on the concentration parameter. We then move on to a regression task, utilizing a ResNet model to predict the value of all five properties. The network is able to achieve a mean percentage error of 1.8% for the central cooling time, and a balanced accuracy of 0.83 on the concentration parameter, making them the best-performing metrics. Finally, we use simulation-based inference (SBI) to extract posterior distributions for the network predictions. Our neural network simultaneously predicts all five classification metrics using only mock Chandra X-ray images. This study demonstrates that machine learning is a viable approach for analyzing and classifying the large galaxy cluster datasets that will soon become available through current and upcoming X-ray surveys, such as eROSITA.
2025-01-07
ArXiv (prépublication)
arxiv.org
arxiv.org
Galaxy cluster characterization with machine learning techniques
Maria Sadikov
Julie Hlavacek-Larrondo
Laurence Perreault-Levasseur
C. Rhea
Michael McDonald
Michelle Ntampaka
John ZuHone
We present an analysis of the X-ray properties of the galaxy cluster population in the z=0 snapshot of the IllustrisTNG simulations, utilizi… (voir plus)ng machine learning techniques to perform clustering and regression tasks. We examine five properties of the hot gas (the central cooling time, the central electron density, the central entropy excess, the concentration parameter, and the cuspiness) which are commonly used as classification metrics to identify cool core (CC), weak cool core (WCC) and non cool core (NCC) clusters of galaxies. Using mock Chandra X-ray images as inputs, we first explore an unsupervised clustering scheme to see how the resulting groups correlate with the CC/WCC/NCC classification based on the different criteria. We observe that the groups replicate almost exactly the separation of the galaxy cluster images when classifying them based on the concentration parameter. We then move on to a regression task, utilizing a ResNet model to predict the value of all five properties. The network is able to achieve a mean percentage error of 1.8% for the central cooling time, and a balanced accuracy of 0.83 on the concentration parameter, making them the best-performing metrics. Finally, we use simulation-based inference (SBI) to extract posterior distributions for the network predictions. Our neural network simultaneously predicts all five classification metrics using only mock Chandra X-ray images. This study demonstrates that machine learning is a viable approach for analyzing and classifying the large galaxy cluster datasets that will soon become available through current and upcoming X-ray surveys, such as eROSITA.
2025-01-07
ArXiv (prépublication)
arxiv.org
arxiv.org
Multiphase Black Hole Feedback and a Bright [C ii] Halo in a LoBAL Quasar at z ∼ 6.6
Manuela Bischetti
Hyunseop Choi
Fabrizio Fiore
Chiara Feruglio
Stefano Carniani
Valentina D'Odorico
Eduardo Banados
Huanqing Chen
Roberto Decarli
Simona Gallerani
Julie Hlavacek-Larrondo
Samuel Lai
K. Leighly
Chiara Mazzucchelli
Laurence Perreault-Levasseur
Roberta Tripodi
Fabian Walter
Feige Wang
Jinyi Yang
Maria Vittoria Zanchettin … (voir 1 de plus)
Yongda Zhu
2024-04-18
ArXiv (prépublication)
doi.org
arxiv.org