scholarly journals Exploring the structure of hadronic showers and hadronic energy reconstruction with highly granular calorimeters

2019 ◽  
Author(s):  
Kiyotomo Kawagoe ◽  
Roman Poeschl ◽  
Keyword(s):  
Energy Reconstruction ◽  
Hadronic Energy

scholarly journals Measuring Multijet Structure of Hadronic Energy Flow or, What is a jet?

1997 ◽  
Vol 12 (30) ◽  
pp. 5411-5529 ◽  
Author(s):  
Fyodor V. Tkachov
Keyword(s):  
Energy Flow ◽  
Mass Spectra ◽  
Analytical Control ◽  
Final States ◽  
C Algebra ◽  
Approximation Errors ◽  
Basic Class ◽  
Jet Algorithms ◽  
Hadronic Energy ◽  
Optimal Recombination

Ambiguities of jet algorithms are reinterpreted as instability wrt small variations of input. Optimal stability occurs for observables possessing property of calorimetric continuity (C-continuity) predetermined by kinematical structure of calorimetric detectors. The so-called C-correlators form a basic class of such observables and fit naturally into QFT framework, allowing systematic theoretical studies. A few rules generate other C-continuous observables. The resulting C-algebra correctly quantifies any feature of multijet structure such as the "number of jets" and mass spectra of "multijet substates." The new observables are physically equivalent to traditional ones but can be computed from final states bypassing jet algorithms which reemerge as a tool of approximate computation of C-observables from data with all ambiguities under analytical control and an optimal recombination criterion minimizing approximation errors.

Electron-beam energy reconstruction for neutrino oscillation measurements

Nature ◽  
2021 ◽  
Vol 599 (7886) ◽  
pp. 565-570
Author(s):  
M. Khachatryan ◽  
A. Papadopoulou ◽  
A. Ashkenazi ◽  
F. Hauenstein ◽  
A. Nambrath ◽  
...  
Keyword(s):  
Electron Beam ◽  
Neutrino Oscillation ◽  
Beam Energy ◽  
Electron Beam Energy ◽  
Energy Reconstruction

scholarly journals Artificial Neural Networks on FPGAs for Real-Time Energy Reconstruction of the ATLAS LAr Calorimeters

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Georges Aad ◽  
Anne-Sophie Berthold ◽  
Thomas Calvet ◽  
Nemer Chiedde ◽  
Etienne Marie Fortin ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Time ◽  
Hadron Collider ◽  
Liquid Argon ◽  
Learning Approaches ◽  
High Luminosity ◽  
Proton Proton ◽  
Real Time Processing ◽  
Energy Reconstruction

AbstractThe ATLAS experiment at the Large Hadron Collider (LHC) is operated at CERN and measures proton–proton collisions at multi-TeV energies with a repetition frequency of 40 MHz. Within the phase-II upgrade of the LHC, the readout electronics of the liquid-argon (LAr) calorimeters of ATLAS are being prepared for high luminosity operation expecting a pileup of up to 200 simultaneous proton–proton interactions. Moreover, the calorimeter signals of up to 25 subsequent collisions are overlapping, which increases the difficulty of energy reconstruction by the calorimeter detector. Real-time processing of digitized pulses sampled at 40 MHz is performed using field-programmable gate arrays (FPGAs). To cope with the signal pileup, new machine learning approaches are explored: convolutional and recurrent neural networks outperform the optimal signal filter currently used, both in assignment of the reconstructed energy to the correct proton bunch crossing and in energy resolution. The improvements concern in particular energies derived from overlapping pulses. Since the implementation of the neural networks targets an FPGA, the number of parameters and the mathematical operations need to be well controlled. The trained neural network structures are converted into FPGA firmware using automated implementations in hardware description language and high-level synthesis tools. Very good agreement between neural network implementations in FPGA and software based calculations is observed. The prototype implementations on an Intel Stratix-10 FPGA reach maximum operation frequencies of 344–640 MHz. Applying time-division multiplexing allows the processing of 390–576 calorimeter channels by one FPGA for the most resource-efficient networks. Moreover, the latency achieved is about 200 ns. These performance parameters show that a neural-network based energy reconstruction can be considered for the processing of the ATLAS LAr calorimeter signals during the high-luminosity phase of the LHC.

scholarly journals Energy reconstruction in a highly granularity semi-digital hadronic calorimeter

2015 ◽  
Vol 664 (7) ◽  
pp. 072033
Author(s):  
Sameh Mannai ◽  
Kais Manai ◽  
Eduardo Cortina ◽  
Imad Laktineh
Keyword(s):  
Energy Reconstruction ◽  
Hadronic Calorimeter

scholarly journals Nonparametric Dark Energy Reconstruction Using the Tomographic Alcock–Paczynski Test

2019 ◽  
Vol 878 (2) ◽  
pp. 137 ◽  
Author(s):  
Zhenyu Zhang ◽  
Gan Gu ◽  
Xiaoma Wang ◽  
Yun-He Li ◽  
Cristiano G. Sabiu ◽  
...  
Keyword(s):  
Dark Energy ◽  
Energy Reconstruction
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