1.5k citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Université de MontréalCA125 papers
- McGill UniversityCA59 papers
- Polytechnique MontréalCA27 papers
- Flatiron Health (United States)US24 papers
- Flatiron Institute23 papers
- Centre National de la Recherche ScientifiqueFR22 papers
- University of TorontoCA16 papers
- Canadian Institute for Advanced ResearchCA14 papers
- Université Paris-SaclayFR14 papers
- University of AmsterdamNL13 papers
- University of WaterlooCA13 papers
- Centre for Research in Astrophysics of QuébecCA12 papers
14 papers · 1 filter
The spatially-resolved effect of mergers on the stellar mass assembly of MaNGA galaxies
Eirini Angeloudi, Marc Huertas-Company, Jesús Falcón-Barroso +3
Understanding the origin of stars within a galaxy - whether formed in-situ or accreted from other galaxies (ex-situ) - is key to constraining its evolution. Spatially resolving the…
MISTRAL: a model for AGN winds from radiatively efficient accretion in cosmological simulations
Marion Farcy, Michaela Hirschmann, Rachel S. Somerville +11
Feedback from active galactic nuclei (AGN) is crucial for regulating galaxy evolution. Motivated by observations of broad absorption line winds from rapidly accreting supermassive…
Euclid: Early Release Observations -- Interplay between dwarf galaxies and their globular clusters in the Perseus galaxy cluster
T. Saifollahi, A. Lançon, Michele Cantiello +183
We present an analysis of globular clusters (GCs) of dwarf galaxies in the Perseus galaxy cluster to explore the relationship between dwarf galaxy properties and their GCs. Our foc…
The Intrinsic Flattening of Galaxy Disks
Jeremy Favaro, Stéphane Courteau, Sébastien Comerón +1
Highly inclined (edge-on) disk galaxies offer the unique perspective to constrain their intrinsic flattening, , where and are respectively the vertical and long radial…
Quantitatively rating galaxy simulations against real observations with anomaly detection
Zehao Jin, Andrea V. Macciò, Nicholas Faucher +6
Cosmological galaxy formation simulations are powerful tools to understand the complex processes that govern the formation and evolution of galaxies. However, evaluating the realis…
Simulation-guided galaxy evolution inference: A case study with strong lensing galaxies
Andreas Filipp, Yiping Shu, Ruediger Pakmor +2
Understanding the evolution of galaxies provides crucial insights into a broad range of aspects in astrophysics, including structure formation and growth, the nature of dark energy…
Spatial variations in aromatic hydrocarbon emission in a dust-rich galaxy
Justin S. Spilker, Kedar A. Phadke, Manuel Aravena +35
Dust grains absorb half of the radiation emitted by stars throughout the history of the universe, re-emitting this energy at infrared wavelengths. Polycyclic aromatic hydrocarbons…
MaNGA galaxy properties -- II. A detailed comparison of observed and simulated spiral galaxy scaling relations
Nikhil Arora, Stéphane Courteau, Connor Stone +1
We present a catalogue of dynamical properties for 2368 late-type galaxies from the MaNGA survey. The latter complements the catalogue of photometric properties for the same sample…
Morphological Parameters and Associated Uncertainties for 8 Million Galaxies in the Hyper Suprime-Cam Wide Survey
Aritra Ghosh, C. Megan Urry, Aayush Mishra +10
We use the Galaxy Morphology Posterior Estimation Network (GaMPEN) to estimate morphological parameters and associated uncertainties for million galaxies in the Hyper Supr…
Dynamical Origin for the Collinder 132-Gulliver 21 Stream: A Mixture of three Co-Moving Populations with an Age Difference of 250 Myr
Xiaoying Pang, Yuqian Li, Shih-Yun Tang +6
We use Gaia DR3 data to study the Collinder 132-Gulliver 21 region via the machine learning algorithm StarGO, and find eight subgroups of stars (ASCC 32, Collinder 132 gp 1--6, Gul…
GaMPEN: A Machine Learning Framework for Estimating Bayesian Posteriors of Galaxy Morphological Parameters
Aritra Ghosh, C. Megan Urry, Amrit Rau +11
We introduce a novel machine learning framework for estimating the Bayesian posteriors of morphological parameters for arbitrarily large numbers of galaxies. The Galaxy Morphology…
Galaxies and Halos on Graph Neural Networks: Deep Generative Modeling Scalar and Vector Quantities for Intrinsic Alignment
Yesukhei Jagvaral, François Lanusse, Sukhdeep Singh +3
In order to prepare for the upcoming wide-field cosmological surveys, large simulations of the Universe with realistic galaxy populations are required. In particular, the tendency…
A Machine Learning Approach to Integral Field Unit Spectroscopy Observations: II. HII Region LineRatios
Carter Rhea, Laurie Rousseau-Nepton, Simon Prunet +5
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 spec…
A Novel Machine Learning Approach to Disentangle Multi-Temperature Regions in Galaxy Clusters
Carter L. Rhea, Julie Hlavacek-Larrondo, Laurence Perreault-Levasseur +2
The hot intra-cluster medium (ICM) surrounding the heart of galaxy clusters is a complex medium comprised of various emitting components. Although previous studies of nearby galaxy…