Conveners
Tues PM Plenaries: Plenaries
- Richard Philip Mount (SLAC National Accelerator Laboratory (US))
- Elizabeth Sexton-Kennedy (Fermi National Accelerator Lab. (US))
In this work we investigate different machine learning based strategies for
denoising raw simulation data from ProtoDUNE experiment. ProtoDUNE detector
is hosted by CERN and it aims to test and calibrate the technologies for DUNE, a
forthcoming experiment in neutrino physics. Our models leverage deep learning
algorithms to make the first step in the reconstruction workchain,...
Within the Phase-II upgrade of the LHC, the readout electronics of the ATLAS LAr Calorimeters is prepared for high luminosity operation expecting a pile-up of up to 200 simultaneous pp interactions. Moreover, the calorimeter signals of up to 25 subsequent collisions are overlapping, which increases the difficulty of energy reconstruction. Real-time processing of digitized pulses sampled at 40...
Quantum computers have the potential for significant speed-ups of certain computational tasks. A possibility this opens up within the field of machine learning is the use of quantum features that would be inefficient to calculate classically. Machine learning algorithms are ubiquitous in particle physics and as advances are made in quantum machine learning technology, there may be a similar...
The EDM4hep project aims to design the common event data model for the Key4hep project and is generated via the podio toolkit. We present the first version of EDM4hep and discuss some of its use cases in the Key4hep project. Additionally, we discuss recent developments in podio, like the updates of the automatic code generation and also the addition of a second I/O backend based on SIO. We...
