The application of machine learning techniques in particle physics has accelerated the development of methodologies for exploring physics beyond the Standard Model. This talk will present an overview of anomaly detection, an unsupervised machine learning technique, and its potential to enhance the detection of new physics within data collected by the ATLAS detector at CERN. The talk will...
The LHCb experiment has deployed machine learning and artificial intelligence models in its real-time data processing from the start of Run 1 datataking. Contrary to common fears when the LHC was starting up, these models have proven to not only be more powerful than "classical" alternatives but in many cases also more robust to changing detector performance. Their judicious use has also made...
Transformers are the state-of-the-art model architectures and widely used in application areas of machine learning. However the performance of such architectures is less well explored in the ultra-low latency domains where deployment on FPGAs or ASICs is required. Such domains include the trigger and data acquisition systems of the LHC experiments.
We present a transformer-based algorithm...
