ML4Jets2024

Session

Astro & Cosmo

Nov 6, 2024, 1:50 PM

LPNHE, Paris, France

Conveners

Astro & Cosmo

• Katia Matcheva

21. Fusing physics principles and machine learning: inferring dark matter densities of galaxies using stellar catalogs with incomplete kinematic information

Sung Hak Lim (Rutgers University)

11/6/24, 1:50 PM

Galactic dynamics studies often face the challenge of incomplete kinematic information in stellar catalogs.
This incompleteness poses a significant challenge to a complete and model-independent measurement of local galactic dark matter densities using stellar dynamics.
This talk presents two innovative approaches that fuse physics principles with machine learning techniques, specifically...

56. SKATR -- A Self-Supervised Summary Transformer for the Square Kilometre Array

Ayodele Ore

11/6/24, 2:10 PM

The upcoming Square Kilometre Array (SKA) will bring about a new era of radio astronomy by allowing 3D imaging of the Universe during the periods of Cosmic Dawn and Reionisation. Machine learning promises to be a powerful tool to analyse the highly structured and complex signal, however accurate training datasets are expensive to simulate and supervised learning may not generalise. We...

27. Sweeping the Dust Away: An Unbiased Map of the Milky Way's Dark Matter and Gravitational Potential with Unsupervised Machine Learning

Eric Putney (Rutgers, The State University of New Jersey)

11/6/24, 2:30 PM

The dynamics of stars in our galaxy encode crucial information about the Milky Way's dark matter halo. However, extinction from foreground dust can bias studies of stellar populations. By solving the equilibrium collisionless Boltzmann equation with novel machine learning techniques, we estimate the unbiased 6-dimensional phase space density of an equilibrated stellar population and the...

65. SkyCURTAINs: Model agnostic search for Stellar Streams with Gaia data

Stephen Brian Mulligan (Universite de Geneve (CH))

11/6/24, 2:50 PM

We introduce SkyCURTAINs, an adaptation of the CURTAINs method—a weakly supervised technique originally developed for anomaly detection in high-energy physics data—applied to data from the second Gaia Data Release (GDR2). SkyCURTAINs is employed to search for stellar streams, which appear as line-like overdensities against the background of the Milky Way. To validate the feasibility of this...

58. Generation of Air Shower Images for Imaging Air Cherenkov Telescopes using Diffusion Models

Christian Elflein (Erlangen Centre for Astroparticle Physics)

11/6/24, 3:10 PM

The major goal of Imaging Atmospheric Cherenkov Telescopes (IACTs) is the investigation of gamma-ray sources through the detection of their induced air showers. For every detected gamma ray, there are up to 10000 cosmic ray protons present forming the background, which also needs to be studied. For a detailed understanding of the instrument for deriving its response to both gamma rays and...