Related papers: ML-assisted versatile approach to Calorimeter R&D

The LHCb experiment at CERN has been upgraded for the Run 3 operation of the Large Hadron Collider (LHC). A new concept of tracking detector based on Scintillating Fibres (SciFi) read out with multichannel silicon photomultipliers (SiPMs)…

Future experiments of nuclear and particle physics are moving towards the high luminosity regime, in order to access suppressed processes like rare B decays or exotic charmonium resonances. In this scenario, high rate capability is a key…

Mathematical modeling of lithium-ion batteries (LiBs) is a central challenge in advanced battery management. This paper presents a new approach to integrate a physics-based model with machine learning to achieve high-precision modeling for…

Event reconstruction is a central step in many particle physics experiments, turning detector observables into parameter estimates; for example estimating the energy of an interaction given the sensor readout of a detector. A corresponding…

High-Energy Physics experiments are facing a multi-fold data increase with every new iteration. This is certainly the case for the upcoming High-Luminosity LHC upgrade. Such increased data processing requirements forces revisions to almost…

In particle physics the simulation of particle transport through detectors requires an enormous amount of computational resources, utilizing more than 50% of the resources of the CERN Worldwide Large Hadron Collider Grid. This challenge has…

Simulations of colloidal suspensions consisting of mesoscopic particles and smaller species such as ions or depletants are computationally challenging as different length and time scales are involved. Here, we introduce a machine learning…

We present the current stage of research progress towards a one-pass, completely Machine Learning (ML) based imaging calorimeter reconstruction. The model used is based on Graph Neural Networks (GNNs) and directly analyzes the hits in each…

The precise modeling of subatomic particle interactions and propagation through matter is paramount for the advancement of nuclear and particle physics searches and precision measurements. The most computationally expensive step in the…

The CMS detector will undergo significant improvements to face the 10-fold increase in integrated luminosity of LHC, the so-called High-Luminosity LHC, scheduled to start in 2027. This will include a completely new calorimeter in the CMS…

The LHCb upgrade represents a major change of the experiment. The detectors have been almost completely renewed to allow running at an instantaneous luminosity five times larger than that of the previous running periods. Readout of all…

The Upgrade II of the LHCb detector, foreseen for 2031, will operate at an instantaneous luminosity of 1.5 x 10$^{34}$ cm$^{-2}$s$^{-1}$, accumulating a sample of more than 300 fb$^{-1}$. To cope with the estimated pile-up of 42 and 200…

The success of high energy physics programs relies heavily on accurate detector simulations and beam interaction modeling. The increasingly complex detector geometries and beam dynamics require sophisticated techniques in order to meet the…

Radiation damage significantly impacts the performance of silicon tracking detectors in Large Hadron Collider (LHC) experiments such as ATLAS and CMS, with signal reduction being the most critical effect; adjusting sensor bias voltage and…

The CALICE collaboration is currently developing an engineering prototype of an analog hadron calorimeter for a future linear collider like the ILC. One main task of this prototype is to demonstrate the feasibility of building a realistic…

The ATLAS experiment at the Large Hadron Collider has a broad physics programme ranging from precision measurements to direct searches for new particles and new interactions, requiring ever larger and ever more accurate datasets of…

The High Luminosity Large Hadron Collider (HL-LHC) will have a peak luminosity of $5\times10^{34} $cm$^{-2} $\,s$^{-1}$, five times higher than the design luminosity of the LHC. The hadronic ATLAS Tile Calorimeter TileCal) is a sampling…

We propose the LCB-CV-UNet to tackle performance degradation caused by High Dynamic Range (HDR) radar signals. Initially, a hardware-efficient, plug-and-play module named Logarithmic Connect Block (LCB) is proposed as a phase coherence…

Currently, over half of the computing power at CERN GRID is used to run High Energy Physics simulations. The recent updates at the Large Hadron Collider (LHC) create the need for developing more efficient simulation methods. In particular,…

Machine learning (ML) techniques have been demonstrated to improve the accuracy and efficiency of anomaly detection (AD) when compared to conventional methods. This has led to the adoption of ML for data quality monitoring (DQM) use cases…