124 citations
- Université de MontréalCA25 papers
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- Flatiron Health (United States)US8 papers
- Flatiron Institute7 papers
- Polytechnique MontréalCA5 papers
- Centre for Research in Astrophysics of QuébecCA4 papers
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7 papers · 1 filter
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…
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…