scholarly journals Hadron Collider Physics with Real Time Trajectory Reconstruction

2005 ◽  
Author(s):  
Alberto Annovi
Keyword(s):  
Real Time ◽  
Hadron Collider ◽  
Collider Physics ◽  
Trajectory Reconstruction

QCD at Fixed Order: Processes

2018 ◽  
Author(s):  
John Campbell ◽  
Joey Huston ◽  
Frank Krauss
Keyword(s):  
Hadron Collider ◽  
Detailed Comparison ◽  
Theoretical Description ◽  
Higgs Bosons ◽  
Final States ◽  
Collider Physics ◽  
Wide Range ◽  
Theoretical Predictions ◽  
Fixed Order ◽  
The Subject

At the core of any theoretical description of hadron collider physics is a fixed-order perturbative treatment of a hard scattering process. This chapter is devoted to a survey of fixed-order predictions for a wide range of Standard Model processes. These range from high cross-section processes such as jet production to much more elusive reactions, such as the production of Higgs bosons. Process by process, these sections illustrate how the techniques developed in Chapter 3 are applied to more complex final states and provide a summary of the fixed-order state-of-the-art. In each case, key theoretical predictions and ideas are identified that will be the subject of a detailed comparison with data in Chapters 8 and 9.

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.

Hadron Collider Physics

1997 ◽  
Author(s):  
D. Bisello ◽  
G. Busetto ◽  
L. Stanco
Keyword(s):  
Hadron Collider ◽  
Collider Physics
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