The CMS Collaboration developed an end-to-end ML based simulation that can speed up the time for production of analysis samples of several orders of magnitude with a limited loss of accuracy. Detailed event simulation at the LHC is crucial for physics analyses and it is currently taking a large fraction of computing budget. Because the CMS experiment is adopting a common analysis level format...
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...
The Phase-II Upgrade of the LHC will increase its instantaneous
luminosity by a factor of 7 leading to the HL-LHC era. At the HL-LHC, the number of proton-proton collisions in one bunch crossing, pileup, increases significantly, putting stringent requirements on the LHC detectors electronics and real-time data processing capabilities.
The ATLAS LAr calorimeter measures the energy of...
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...
Muon identification is crucial for elementary particle physics experiments. At the Belle II experiment, muons and pions with momenta greater than 0.7 GeV/c are distinguished by their penetration ability through the $K_L$ and Muon (KLM) sub-detector, which is the outermost sub-detector of Belle II.
In this presentation, we will firstly discuss the possible room for $\mu/\pi$ identification...
We present a new algorithm for tagging the production flavour of neutral $B^0$ and $B_s^0$ mesons in proton-proton collisions. It is based on a deep neural network, DeepSets, and exploits a comprehensive set of tracks associated with the hadronization process. The algorithm is calibrated on data collected by the LHCb experiment at a centre-of-mass energy of 13 TeV. This inclusive approach...
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...
