I will recall a basic idea behind Machine Learning, then I will bring random examples of ML applications in High Energy Physics.
At the European Organisation for Nuclear Research (CERN), scientists study the fundamental properties of matter by replicating the extreme conditions of the early universe within the Large Hadron Collider (LHC). Understanding particle collisions requires running simulations that mirror the detectors' expected responses within the LHC. With over 50% of CERN's GRID computing power dedicated to...
Applying machine learning methods to high-energy physics simulations has recently emerged as a rapidly developing area. A prominent example is the Zero Degree Calorimeter (ZDC) simulation in the ALICE experiment at CERN, where substituting the traditional computationally extensive Monte Carlo methods with generative models radically reduces computation time. Although numerous studies have...
The data processing and analyzing is one of the main challenges at HEP experiments, normally one physics result can take more than 3 years to be conducted. To accelerate the physics analysis and drive new physics discovery, the rapidly developing Large Language Model (LLM) is the most promising approach, it have demonstrated astonishing capabilities in recognition and generation of text while...
In the field of high energy physics, Monte Carlo simulations play a key role in interpreting physics results, as well as the design of new detectors. Leveraging machine learning for fast simulation is essential for generating the required amount of simulated samples. Nevertheless, transitioning from initial models to full-scale productions is usually a very challenging task.
In this talk, we...
