7 citations · 19 across the 6 of their papers we have counts for
6 papers
Parameter Estimation for Open Clusters using an Artificial Neural Network with a QuadTree-based Feature Extractor
L. Cavallo, L. Spina, G. Carraro +7
With the unprecedented increase of known star clusters, quick and modern tools are needed for their analysis. In this work, we develop an artificial neural network trained on synth…
Interpretable machine learning for finding intermediate-mass black holes
Mario Pasquato, Piero Trevisan, Abbas Askar +4
Definitive evidence that globular clusters (GCs) host intermediate-mass black holes (IMBHs) is elusive. Machine learning (ML) models trained on GC simulations can in principle pred…
Constructing Impactful Machine Learning Research for Astronomy: Best Practices for Researchers and Reviewers
D. Huppenkothen, M. Ntampaka, M. Ho +19
Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of…
Dynamics of intermediate mass black holes in globular clusters. Wander radius and anisotropy profiles
Pierfrancesco Di Cintio, Mario Pasquato, Luca Barbieri +2
We recently introduced a new method for simulating collisional gravitational N-body systems with approximately linear time scaling with , based on the Multi-Particle Collision (…
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…
Croatian Black Hole School 2010 lecture notes on IMBHs in GCs
Mario Pasquato
Black holes are fascinating objects. As a class of solutions to the Einstein equations they have been studied a great deal, yielding a wealth of theoretical results. But do they re…