Speaker
Description
Machine learning has become an indispensable tool in the field of high-energy physics, particularly in the CMS experiment at CERN.
In this talk, we will discuss some of the new developments in ML techniques implemented in the CMS experiment. These advancements have improved tasks like event reconstruction, jet flavour tagging, data quality monitoring, anomaly detection in the triggers, and fast Monte Carlo generation.
All these ML applications have the potential to speed up the time-to-insight in HEP and improve CMS physics results. However, along with these advancements come technical challenges that must be addressed. These challenges include issues related to training data quality, reproducibility, model interpretability, and computational resources. We will explore these challenges and discuss potential solutions and future directions for ML in the CMS experiment.
