Related papers: A novel layered reconstruction framework for longi…
Several nuclear physics studies using the CLAS12 detector rely on the accurate reconstruction of neutrons and photons from its forward angle calorimeter system. These studies often place restrictive cuts when measuring neutral particles due…
The CALICE collaboration is developing a granular electromagnetic calorimeter using small scintillator strips for a future linear collider experiment. On developing of ~ 10^7 channel-ECAL in particle flow approach, CALICE is developing a…
Knowledge of the mass composition of ultra-high-energy cosmic rays is crucial to understanding their origins; however, current approaches have limited event-by-event resolution. With fluorescence telescope measurements of the longitudinal…
In general-purpose particle detectors, the particle-flow algorithm may be used to reconstruct a comprehensive particle-level view of the event by combining information from the calorimeters and the trackers, significantly improving the…
In the ILD detector, HCAL is inside the coil. The HCAL is about 5 interaction length thick, and ECAL is about 1 interaction length thick. For example, an eighty GeV hadron, it is only 95% energy will be contained in ECAL plus HCAL. We need…
The CALICE collaboration has constructed highly granular electromagnetic and hadronic calorimeter prototypes to evaluate technologies for the use in detector systems at a future Linear Collider. The hadron calorimeter uses small…
Recent inroads in Computer Vision (CV) and Machine Learning (ML) have motivated a new approach to the analysis of particle imaging detector data. Unlike previous efforts which tackled isolated CV tasks, this paper introduces an end-to-end,…
Liquid Argon Time Projection Chambers (LArTPCs) are a class of detectors that produce high resolution images of charged particles within their sensitive volume. In these images, the clustering of distinct particles into superstructures is…
One of the challenges of high granularity calorimeters, such as that to be built to cover the endcap region in the CMS Phase-2 Upgrade for HL-LHC, is that the large number of channels causes a surge in the computing load when clustering…
Correctly identifying the nature and properties of outgoing particles from high energy collisions at the Large Hadron Collider is a crucial task for all aspects of data analysis. Classical calorimeter-based classification techniques rely on…
We present a theoretical framework for temperature imaging from long-wavelength infrared thermal radiation (e.g. 8-12 $\mu$m) through the end-to-end design of a metasurface-optics frontend and a computational-reconstruction backend. We…
Traditionally, longitudinal shower profiles are reconstructed in fluorescence light experiments by treating the Cherenkov light contribution as background. Here we will argue that, due to universality of the energy spectra of electrons and…
The Analog Hadron Calorimeter (AHCAL) concept developed by the CALICE collaboration is a highly granular sampling calorimeter with \SI{3x3}{\square\centi\meter} plastic scintillator tiles individually read out by silicon photomultipliers…
Surface radio antenna-based measurements of cosmic-ray air showers present significant computational challenges in accurately reconstructing physics observables, in particular, the depth of shower maximum, X$_{max}$. State-of-the-art…
A detailed investigation of hadronic interactions is performed using $\pi^-$-mesons with energies in the range 2--10 GeV incident on a high granularity silicon-tungsten electromagnetic calorimeter. The data were recorded at FNAL in 2008.…
The reconstruction of the signal from hadrons and jets emerging from the proton-proton collisions at the Large Hadron Collider (LHC) and entering the ATLAS calorimeters is based on a three-dimensional topological clustering of individual…
Electrical impedance tomography is an imaging modality for extracting information on the conductivity distribution inside a physical body from boundary measurements of current and voltage. In many practical applications, it is a priori…
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…
We describe the design concept and estimated performance of an iron-scintillator sampling calorimeter for the future Electron Ion Collider. The novel aspect of this detector is a multi-dimensional readout coupled with foreseen excellent…
Fast simulation of the energy depositions in high-granular detectors is needed for future collider experiments with ever-increasing luminosities. Generative machine learning (ML) models have been shown to speed up and augment the…