Conveners
Session 1
- Markus Stoye (CERN)
- Steven Randolph Schramm (Universite de Geneve (CH))
We present a customized neural network architecture for both, slim and fat jet tagging. It is based on the idea to keep the concept of physics objects, like particle flow particles, as a core element of the network architecture. The deep learning algorithm works for most of the common jet classes, i.e. b, c, usd and gluon jets for slim jets and W, Z, H, QCD and top classes for fat jets. The...
At HL-LHC, the seven-fold increase of multiplicity wrt 2018 conditions poses a severe challenge to ATLAS and CMS tracking experiments. Both experiment are revamping their tracking detector, and are optimizing their software. But are there not new algorithms developed outside HEP which could be invoked: for example MCTS, LSTM, clustering, CNN, geometric deep learning and more?
We organize on...
Collider will constantly bring nominal luminosity increase, with the ultimate goal of reaching a peak luminosity of $5 · 10^{34} cm^{−2} s^{−1}$ for ATLAS and CMS experiments planned for the High Luminosity LHC (HL-LHC) upgrade. This rise in luminosity will directly result in an increased number of simultaneous proton collisions (pileup), up to 200, that will pose new challenges for the CMS...
The LHCb experiment at CERN operates a high precision and robust tracking system to reach its physics goals, including precise measurements of CP-violation phenomena in the heavy flavour quark sector and searches for New Physics beyond the Standard Model. Since Run2, the experiment has put in place a new trigger strategy with a real-time reconstruction, alignment and calibration, imposing...
Machine learning has been an attractive topic in high-energy physics field for many years. For example, machine learning algorithms devoted to the reconstruction of particle tracks or jets in high energy physics experiments. EOS is an open source parallel distributed file system. It has been generally used in large scale cluster computing for both physics and user use cases at IHEP, like...
