November 27, 2023 to December 1, 2023
Dual node
Europe/Paris timezone

Calculating enclosed mass with machine learning and line-of-sight data

Not scheduled
Dual node

Dual node

IAP (Paris) & CCA/Flatiron (New York) IAP 98bis Boulevard Arago 75014 Paris FRANCE CCA/Flatiron 5th Avenue New York (NY) USA
Poster Paris Posters


Jorge Sarrato-Alós (Institute of Astrophysics of the Canary Islands)


Accurately determining the mass distribution within galaxies is crucial for understanding their formation and evolution. Previous research has traditionally relied on analytical equations based on the Jeans equation to estimate the enclosed mass with minimum projection effect. In this study, we present a novel approach to predict the enclosed mass within a given radius using a machine learning model trained on line of sight data of high-resolution cosmological hydrodynamical simulations. Our dataset comprises a diverse sample of galaxies spanning a wide range of masses.

To train the model, we utilize projected positions and velocities of stars within the galaxies. Multiple training iterations are performed, each with the mass enclosed within a different radius as the target variable. By systematically varying the radius, we identify the optimal value at which the neural network exhibits the highest precision in predicting the enclosed mass.

Our results demonstrate the effectiveness of the machine learning-based approach in predicting galaxy mass within a specific radius. The trained model offers a valuable tool for studying galaxy properties, such as mass distribution and gravitational potential, providing insights into the formation and dynamics of galaxies. This work also highlights the utility of machine learning techniques for studying galaxies through line of sight data.

Primary author

Jorge Sarrato-Alós (Institute of Astrophysics of the Canary Islands)


Dr Arianna Di Cintio (Institute of Astrophysics of the Canary Islands) Dr Christopher Brook (Institute of Astrophysics of the Canary Islands)

Presentation materials