اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدA relation between track length and deposited energy in a homogeneous calorimeter by Geant4 simulation at high energy
65
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
We performed a Geant4 simulation study on showers generated by electrons and hadrons in a large homogeneous calorimeter. We found that the energy deposit can be expressed as a linear function of the track length. The line does not pass through the origin, and the energy deposit at the intercept is proportional to the incident energy. Moreover, for both electrons and hadrons, the slope of the line is independent of the incident energy. The energy resolution of the calorimeter can be expressed in terms of the distribution around the correlation line, which we found to be very good at about $ 19% / sqrt{E(rm{GeV})}$ for pions.
قيم البحث
اقرأ أيضاً
A simulation study of energy resolution, position resolution, and $pi^0$-$gamma$ separation using multivariate methods of a sampling calorimeter is presented. As a realistic example, the geometry of the calorimeter is taken from the design geometry o
f the Shashlik calorimeter which was considered as a candidate for CMS endcap for the phase II of LHC running. The methods proposed in this paper can be easily adapted to various geometrical layouts of a sampling calorimeter. Energy resolution is studied for different layouts and different absorber-scintillator combinations of the Shashlik detector. It is shown that a boosted decision tree using fine grained information of the calorimeter can perform three times better than a cut-based method for separation of $pi^0$ from $gamma$ over a large energy range of 20 GeV-200 GeV.
We present the 3DGAN for the simulation of a future high granularity calorimeter output as three-dimensional images. We prove the efficacy of Generative Adversarial Networks (GANs) for generating scientific data while retaining a high level of accura
cy for diverse metrics across a large range of input variables. We demonstrate a successful application of the transfer learning concept: we train the network to simulate showers for electrons from a reduced range of primary energies, we then train further for a five times larger range (the model could not train for the larger range directly). The same concept is extended to generate showers for other particles (photons and neutral pions) depositing most of their energies in electromagnetic interactions. In addition, the generation of charged pion showers is also explored, a more accurate effort would require additional data from other detectors not included in the scope of the current work. Our further contribution is a demonstration of using GAN-generated data for a practical application. We train a third-party network using GAN-generated data and prove that the response is similar to a network trained with data from the Monte Carlo simulation. The showers generated by GAN present accuracy within $10%$ of Monte Carlo for a diverse range of physics features, with three orders of magnitude speedup. The speedup for both the training and inference can be further enhanced by distributed training.
A prototype for a sampling calorimeter made out of cerium fluoride crystals interleaved with tungsten plates, and read out by wavelength-shifting fibres, has been exposed to beams of electrons with energies between 20 and 150 GeV, produced by the CER
N Super Proton Synchrotron accelerator complex. The performance of the prototype is presented and compared to that of a Geant4 simulation of the apparatus. Particular emphasis is given to the response uniformity across the channel front face, and to the prototypes energy resolution.
The liquid argon ionization current in a sampling calorimeter cell can be analyzed to determine the energy of detected particles. In practice, experimental artifacts such as pileup and electronic noise make the inference of energy from current a diff
icult process. The beam intensity of the Large Hadron Collider will be significantly increased during the Phase-II long shut down of 2024-2026. Signal processing techniques that are used to extract the energy of detected particles in the ATLAS detector will suffer a significant loss in performance under these conditions. This paper compares the presently used optimal filter technique to convolutional neural networks for energy reconstruction in the ATLAS liquid argon hadronic end cap calorimeter. In particular, it is shown that convolutional neural networks trained with an appropriately tuned and novel loss function are able to outperform the optimal filter technique.
We investigate the three dimensional substructure of hadronic showers in the CALICE scintillator-steel hadronic calorimeter. The high granularity of the detector is used to find track segments of minimum ionising particles within hadronic showers, pr
oviding sensitivity to the spatial structure and the details of secondary particle production in hadronic cascades. The multiplicity, length and angular distribution of identified track segments are compared to GEANT4 simulations with several different shower models. Track segments also provide the possibility for in-situ calibration of highly granular calorimeters.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
