Related papers: Using machine learning to separate hadronic and el…
The purpose of the MIPP experiment is to study the inclusive production of photons, pions, kaons and nucleons in pi, K and p interactions on various targets using beams from the Main Injector at Fermilab. The function of the calorimeters is…
The next generation of particle physics experiments will face a new era of challenges in data acquisition, due to unprecedented data rates and volumes along with extreme environments and operational constraints. Harnessing this data for…
Photon interactions at the LHC result in striking final states with much lower hadronic activity in the central detectors than for pp interactions. In addition, the elastic exchange of a photon leads to a proton scattered at almost…
We present a first proof of concept to directly use neural network based pattern recognition to trigger on distinct calorimeter signatures from displaced particles, such as those that arise from the decays of exotic long-lived particles.…
Data from the Large Electron Positron collider (LEP) at CERN on hadron production in gamma-gamma interactions exceed the predictions of the standard model by an order of magnitude at the highest observed transverse momenta in three…
The meson production cross sections are estimated considering photon-photon interactions in hadron - hadron collisions at CERN LHC energies. We consider a large number of mesons with photon-photon partial decay width well constrained by the…
High-energy muons from air shower events detected in IceCube are selected using state of the art machine learning algorithms. Attributes to distinguish a HE-muon event from the background of low-energy muon bundles are selected using the…
We study the photon detection efficiency and position resolution of the electromagnetic calorimeter (EMC) of the BESIII experiment. The control sample of the initial-state-radiation (ISR) process of $e^+e^-\rightarrow \gamma \mu^+\mu^-$ is…
We investigate the effect of longitudinal and transverse calorimeter segmentation on event-by-event software compensation for hadronic showers. To factorize out sampling and electronics effects, events are simulated in which a single…
In high-granularity calorimetry, as proposed for detectors at future Higgs factories, the requirements on electronics can have a strong impact on the design of the detector, especially via the cooling and acquisition systems. This project…
The CMS experiment at the CERN LHC will be upgraded to accommodate the 5-fold increase in the instantaneous luminosity expected at the High-Luminosity LHC (HL-LHC). Concomitant with this increase will be an increase in the number of…
With the advent of powerful computer simulation techniques, it is time to move from the widely used knowledge-guided empirical methods to approaches driven by data science, mainly machine learning algorithms. We investigated the predictive…
We discuss the production of hadronic resonances in very peripheral heavy ion collisions, where the ions collide with impact parameter larger than twice the nuclear radius and remain intact after the collision. We compare the resonance…
The upgrade of the CMS experiment for the high luminosity operation of the LHC comprises the replacement of the current endcap calorimeter by a high granularity sampling calorimeter (HGCAL). The electromagnetic section of the HGCAL is based…
Calibration of sensors is a fundamental step to validate their operation. This can be a demanding task, as it relies on acquiring a detailed modelling of the device, aggravated by its possible dependence upon multiple parameters. Machine…
A machine learning approach has been implemented to measure the electron temperature directly from the emission spectra of a tokamak plasma. This approach utilized a neural network (NN) trained on a dataset of 1865 time slices from…
The identification of hadronic tau lepton decays is an important requirement for the LHC physics program, both in terms of standard model measurements and the search for beyond-the-standard-model physics. The ATLAS and CMS algorithms for…
Photons and dileptons are being used to probe the properties of nuclear and quark-gluon matter at high energy densities. This is an area where theory and experiment are driving each other to obtain solid results. However, it is important to…
Federated learning (FL) enables devices in mobile edge computing (MEC) to collaboratively train a shared model without uploading the local data. Gradient compression may be applied to FL to alleviate the communication overheads but current…
The ALICE Collaboration at the LHC studies heavy-ion collisions to investigate the properties of the Quark-Gluon Plasma (QGP). Heavy quarks (charm and beauty) are effective probes for this purpose. Both their energy loss in the medium as…