2. Fundamental Physics - Machine learning for Particle Physics, Astrophysics and Cosmology

In the past 20 years four Nobel Prizes for Physics were awarded for achievements related to astroparticle physics, in 2002, 2006, 2011 and 2017 (with recipients of the 2006 and 2011 prizes being part of UC Berkley-BCCB, our Associated Partner). The success of astroparticle physics stems from the realisation that by exploring the Universe we can address some of our most fundamental questions, such as how the Universe began, what is its destiny, what is the world made of and what forces keep it together. The excellence of the host groups was recognized by a series of prestigious National awards such as the ‘Silver Order of Merit’ (2015), and the prestigious Robert Blinc Awards in 2020 received by a JSI and a CAC member.

Research sub-areas

2.1 Dark matter and gamma-ray astrophysics with the Cherenkov Telescope Array

With more than 100 telescopes located in the northern and southern hemispheres, CTA will be the world’s largest and most sensitive high-energy gamma-ray observatory. The UNG is pushing the boundaries of sensitivity to discover more about the nature of dark matter.

Host institution: UNG-CAC

2.2 Cosmology and time domain astrophysics with the Vera Rubin Observatory

Targeted applications of ML in analysing huge amounts of Rubin Obs data (it will observe about 20 billion stars and about 20 billion galaxies across the Universe over its 10 year mission) will be critical to reaching its scientific goals. CAC/UNG is actively involved in the study of transient events in the Transients and Variable Stars Science Collaboration and contributes within the DESC Strong Lensing Group on the characterisation of the accelerated expansion of the Universe we observe today.

Host institution: UNG-CAC

2.3 New physics at the Large Hadron Collider

This research includes fast simulation and analysis backed by ML methods, e.g., GAN simulation, Search for rare and exotic phenomena, Theoretical models, and fast Monte-Carlo and Infrastructure development for extreme data processing.

Host institution: JSI F1, JSI F9

SMASH research areas
1. Data Science - Machine Learning for Scientific Applications
2. Fundamental Physics - Machine learning for Particle Physics, Astrophysics and Cosmology
3. Linguistics - Computing for Human and Animal Communication
4. Climate - Machine Learning in Climate Research
5. Precision Medicine - Personalised Medicine and Life Sciences