Conveners
Computing: Machine learning
- Engin Eren (FLC (FTX Fachgruppe SFT))
- Gilson Correia Silva (CMS (CMS Fachgruppe TOP))
Computing: Machine learning
- Gilson Correia Silva (CMS (CMS Fachgruppe TOP))
- Engin Eren (FLC (FTX Fachgruppe SFT))
Accurate simulation of the interaction of particles with the detector materials is of
utmost importance for the success of modern particle physics. Software libraries like
GEANT4 are tools that already allow the modeling of physical processes inside detectors
with high precision. The downside of this method is its computational cost in terms of
time.
Recent developments in generative...
Detector simulation is a key cornerstone of modern high energy physics. Traditional simulation tools are reliant upon Monte Carlo methods, which consume significant computational resources and are projected to be a major bottleneck at the high luminosity stage of the LHC and for future colliders. Calorimeter shower simulation has been a focus of fast simulation efforts, as it is particularly...
Highly precise simulations of elementary particles interaction and processes are fundamental to accurately reproduce and interpret the experimental results in High Energy Physics (HEP) detectors and to correctly reconstruct the particle flows. Today, detector simulations typically rely on Monte Carlo-based methods which are extremely demanding in terms of computing resources. The need for...
The pixel vertex detector (PXD) is the newest and the most sensitive subdetector at the Belle II. Data from the PXD and other sensors allow us to reconstruct particle tracks and decay vertices. The effect of background processes on track reconstruction is simulated by adding measured or simulated background hit patterns to the hits produced by simulated signal particles which originates from...
Imaging capabilities of highly granular calorimeters allow to study in detail the inner structure of hadronic showers. Reconstruction of the particle composition and properties of secondary showers in each hadronic cascade brings additional information that can be used in diferent applications. This contribution presents the graph neural network based reconstruction of electromagnetic...
In this talk I present a method to reconstruct the kinematics of neutral-current deep inelastic scattering (DIS) using a deep neural network (DNN). Unlike traditional methods, it exploits the full kinematic information of both the scattered electron and the hadronic-final state, and it accounts for QED radiation by identifying events with radiated photons and event-level momentum imbalance....
Despite continuous efforts by the LHC physics program as well as other experiments to conduct searches for physics beyond the standard model, no evidence has been found so far. A major disadvantage of many current searches is their reliance on specific signal and background models. Since it is impossible to cover all possible models and phase space regions with a dedicated search, the...
The associated production of a bb¯ pair with a Higgs boson could provide an important probe to both the size and the phase of the bottom-quark Yukawa coupling, yb. However, the signal is shrouded by several background processes including the irreducible Zh,Z→bb¯ background. We show that the analysis of kinematic shapes provides us with a concrete prescription for separating the yb-sensitive...
