ML4Jets2022

Session

Beyond Jets

3 Nov 2022, 16:10

Multipurpose Room (aka Livingston Hall) (Rutgers University)

Conveners

Beyond Jets

• Mariel Pettee (Lawrence Berkeley National Lab. (US))
• David Shih

55. Overview of ML for Gravitational Waves

Eric Anton Moreno (Massachusetts Institute of Technology (US))

03/11/2022, 16:10

At an increasing number of interferometer sites with constantly-changing detector conditions, AI can play an important role in real-time and offline data processing. In this talk, we develop novel algorithms and training schemes that sift through noise and instrumental glitches to detect gravitational waves (GW) from compact binary coalescences (CBCs). For real-time processing, we create...

91. Overview of ML for Gaia

Matthew Buckley

03/11/2022, 16:40

In-person

The Gaia space telescope measures the position and proper motion of a billion stars in the neighborhood of the Sun. This dataset contains stellar streams, tidal debris, and other structures that can cast light on the structure of the Galaxy, its merger history, and its dark matter component. I review the machine learning approaches -- including classifiers, normalizing flows, and anomaly...

88. Overview of ML for Astro/Cosmo

Miles Cranmer (Princeton)

03/11/2022, 17:10

Zoom

I will give an overview of recent progress in ML applications to Astro/Cosmo.

89. Overview of ML for Neutrinos

Fernanda Psihas (Fermi National Accelerator Laboratory)

03/11/2022, 17:40

Zoom

I will give an overview of ML applications to Neutrino Physics.

57. GalaxyFlow: Upsampling Hydrodynamical Simulations for Realistic Gaia Mock Catalogs

Sung Hak Lim (Rutgers University)

The Gaia DR3 catalog provides high-quality measurements of stars in the Milky Way, but the current $N$-body simulation-based mock Gaia catalogs have larger resolutions compared to those of the original Gaia dataset.
Because of that, using the mock catalogs to aid statistical analyses on the Gaia dataset in a small position/velocity resolution scale is very limited.
To solve this issue, we...

63. Measuring Galactic Dark Matter through Unsupervised Machine Learning

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

Measuring the density profile of dark matter in the Solar neighborhood has important implications for both dark matter theory and experiment. In this work, we apply masked autoregressive flows to stars from a realistic simulation of a Milky Way-type galaxy to learn -- in an unsupervised way -- the stellar phase space density and its derivatives. With these as inputs we calculate the...