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5 results on '"Agaras MN"'

1. Software Performance of the ATLAS Track Reconstruction for LHC Run 3

Author

Aad, G, Abbott, B, Abeling, K, Abicht, NJ, Abidi, SH, Aboulhorma, A, Abramowicz, H, Abreu, H, Abulaiti, Y, Acharya, BS, Bourdarios, C Adam, Adamczyk, L, Adamek, L, Addepalli, SV, Addison, MJ, Adelman, J, Adiguzel, A, Adye, T, Affolder, AA, Afik, Y, Agaras, MN, Agarwala, J, Aggarwal, A, Agheorghiesei, C, Ahmad, A, Ahmadov, F, Ahmed, WS, Ahuja, S, Ai, X, Aielli, G, Aikot, A, Tamlihat, M Ait, Aitbenchikh, B, Aizenberg, I, Akbiyik, M, Åkesson, TPA, Akimov, AV, Akiyama, D, Akolkar, NN, Khoury, K Al, Alberghi, GL, Albert, J, Albicocco, P, Albouy, GL, Alderweireldt, S, Aleksa, M, Aleksandrov, IN, Alexa, C, Alexopoulos, T, Alfonsi, F, Algren, M, Alhroob, M, Ali, B, Ali, HMJ, Ali, S, Alibocus, SW, Aliev, M, Alimonti, G, Alkakhi, W, Allaire, C, Allbrooke, BMM, Allen, JF, Flores, CA Allendes, Allport, PP, Aloisio, A, Alonso, F, Alpigiani, C, Estevez, M Alvarez, Fernandez, A Alvarez, Cardoso, M Alves, Alviggi, MG, Aly, M, Coutinho, Y Amaral, Ambler, A, Amelung, C, Amerl, M, Ames, CG, Amidei, D, Santos, SP Amor Dos, Amos, KR, Ananiev, V, Anastopoulos, C, Andeen, T, Anders, JK, Andrean, SY, Andreazza, A, Angelidakis, S, Angerami, A, Anisenkov, AV, Annovi, A, Antel, C, Anthony, MT, Antipov, E, Antonelli, M, Anulli, F, Aoki, M, Aoki, T, Pozo, JA Aparisi, Aparo, MA, and Bella, L Aperio

Subjects
Information and Computing Sciences, Physical Sciences, Software Engineering, Bioengineering
Abstract

Charged particle reconstruction in the presence of many simultaneous proton–proton (pp) collisions in the LHC is a challenging task for the ATLAS experiment’s reconstruction software due to the combinatorial complexity. This paper describes the major changes made to adapt the software to reconstruct high-activity collisions with an average of 50 or more simultaneous pp interactions per bunch crossing (pile-up) promptly using the available computing resources. The performance of the key components of the track reconstruction chain and its dependence on pile-up are evaluated, and the improvement achieved compared to the previous software version is quantified. For events with an average of 60pp collisions per bunch crossing, the updated track reconstruction is twice as fast as the previous version, without significant reduction in reconstruction efficiency and while reducing the rate of combinatorial fake tracks by more than a factor two.

Published
2024

2. Deep Generative Models for Fast Photon Shower Simulation in ATLAS

Author

Aad, G, Abbott, B, Abbott, DC, Abud, A Abed, Abeling, K, Abhayasinghe, DK, Abidi, SH, Aboulhorma, A, Abramowicz, H, Abreu, H, Abulaiti, Y, Hoffman, AC Abusleme, Acharya, BS, Achkar, B, Adam, L, Bourdarios, C Adam, Adamczyk, L, Adamek, L, Addepalli, SV, Adelman, J, Adiguzel, A, Adorni, S, Adye, T, Affolder, AA, Afik, Y, Agaras, MN, Agarwala, J, Aggarwal, A, Agheorghiesei, C, Aguilar-Saavedra, JA, Ahmad, A, Ahmadov, F, Ahmed, WS, Ai, X, Aielli, G, Aizenberg, I, Akbiyik, M, Åkesson, TPA, Akimov, AV, Al Khoury, K, Alberghi, GL, Albert, J, Albicocco, P, Verzini, MJ Alconada, Alderweireldt, S, Aleksa, M, Aleksandrov, IN, Alexa, C, Alexopoulos, T, Alfonsi, A, Alfonsi, F, Alhroob, M, Ali, B, Ali, S, Aliev, M, Alimonti, G, Allaire, C, Allbrooke, BMM, Allport, PP, Aloisio, A, Alonso, F, Alpigiani, C, Camelia, E Alunno, Estevez, M Alvarez, Alviggi, MG, Coutinho, Y Amaral, Ambler, A, Ambroz, L, Amelung, C, Amidei, D, Dos Santos, SP Amor, Amoroso, S, Amos, KR, Amrouche, CS, Ananiev, V, Anastopoulos, C, Andari, N, Andeen, T, Anders, JK, Andrean, SY, Andreazza, A, Angelidakis, S, Angerami, A, Anisenkov, AV, Annovi, A, Antel, C, Anthony, MT, Antipov, E, Antonelli, M, Antrim, DJA, Anulli, F, Aoki, M, Pozo, JA Aparisi, Aparo, MA, Bella, L Aperio, Appelt, C, Aranzabal, N, Ferraz, V Araujo, Arcangeletti, C, and Arce, ATH

Subjects
Nuclear and Plasma Physics, Particle and High Energy Physics, Physical Sciences
Abstract

The need for large-scale production of highly accurate simulated event samples for the extensive physics programme of the ATLAS experiment at the Large Hadron Collider motivates the development of new simulation techniques. Building on the recent success of deep learning algorithms, variational autoencoders and generative adversarial networks are investigated for modelling the response of the central region of the ATLAS electromagnetic calorimeter to photons of various energies. The properties of synthesised showers are compared with showers from a full detector simulation using geant4. Both variational autoencoders and generative adversarial networks are capable of quickly simulating electromagnetic showers with correct total energies and stochasticity, though the modelling of some shower shape distributions requires more refinement. This feasibility study demonstrates the potential of using such algorithms for ATLAS fast calorimeter simulation in the future and shows a possible way to complement current simulation techniques.

Published
2024

3. Emulating the impact of additional proton–proton interactions in the ATLAS simulation by presampling sets of inelastic Monte Carlo events

Author

Aad, G, Abbott, B, Abbott, DC, Abud, A Abed, Abeling, K, Abhayasinghe, DK, Abidi, SH, AbouZeid, OS, Abraham, NL, Abramowicz, H, Abreu, H, Abulaiti, Y, Hoffman, AC Abusleme, Acharya, BS, Achkar, B, Adam, L, Bourdarios, C Adam, Adamczyk, L, Adamek, L, Adelman, J, Adiguzel, A, Adorni, S, Adye, T, Affolder, AA, Afik, Y, Agapopoulou, C, Agaras, MN, Aggarwal, A, Agheorghiesei, C, Aguilar-Saavedra, JA, Ahmad, A, Ahmadov, F, Ahmed, WS, Ai, X, Aielli, G, Akatsuka, S, Akbiyik, M, Åkesson, TPA, Akilli, E, Akimov, AV, Khoury, K Al, Alberghi, GL, Albert, J, Verzini, MJ Alconada, Alderweireldt, S, Aleksa, M, Aleksandrov, IN, Alexa, C, Alexopoulos, T, Alfonsi, A, Alfonsi, F, Alhroob, M, Ali, B, Ali, S, Aliev, M, Alimonti, G, Allaire, C, Allbrooke, BMM, Allport, PP, Aloisio, A, Alonso, F, Alpigiani, C, Camelia, E Alunno, Estevez, M Alvarez, Alviggi, MG, Coutinho, Y Amaral, Ambler, A, Ambroz, L, Amelung, C, Amidei, D, Santos, SP Amor Dos, Amoroso, S, Amrouche, CS, Anastopoulos, C, Andari, N, Andeen, T, Anders, JK, Andrean, SY, Andreazza, A, Andrei, V, Anelli, CR, Angelidakis, S, Angerami, A, Anisenkov, AV, Annovi, A, Antel, C, Anthony, MT, Antipov, E, Antonelli, M, Antrim, DJA, Anulli, F, Aoki, M, Pozo, JA Aparisi, Aparo, MA, Bella, L Aperio, Aranzabal, N, Ferraz, V Araujo, Arcangeletti, C, Arce, ATH, and Arguin, J-F

Abstract

The accurate simulation of additional interactions at the ATLAS experiment for the analysis of proton–proton collisions delivered by the Large Hadron Collider presents a significant challenge to the computing resources. During the LHC Run 2 (2015–2018), there were up to 70 inelastic interactions per bunch crossing, which need to be accounted for in Monte Carlo (MC) production. In this document, a new method to account for these additional interactions in the simulation chain is described. Instead of sampling the inelastic interactions and adding their energy deposits to a hard-scatter interaction one-by-one, the inelastic interactions are presampled, independent of the hard scatter, and stored as combined events. Consequently, for each hard-scatter interaction, only one such presampled event needs to be added as part of the simulation chain. For the Run 2 simulation chain, with an average of 35 interactions per bunch crossing, this new method provides a substantial reduction in MC production CPU needs of around 20%, while reproducing the properties of the reconstructed quantities relevant for physics analyses with good accuracy.

Published
2022

4. AtlFast3: The Next Generation of Fast Simulation in ATLAS

Author

Aad, G, Abbott, B, Abbott, DC, Abud, A Abed, Abeling, K, Abhayasinghe, DK, Abidi, SH, Aboulhorma, A, Abramowicz, H, Abreu, H, Abulaiti, Y, Hoffman, AC Abusleme, Acharya, BS, Achkar, B, Adam, L, Bourdarios, C Adam, Adamczyk, L, Adamek, L, Addepalli, SV, Adelman, J, Adiguzel, A, Adorni, S, Adye, T, Affolder, AA, Afik, Y, Agapopoulou, C, Agaras, MN, Agarwala, J, Aggarwal, A, Agheorghiesei, C, Aguilar-Saavedra, JA, Ahmad, A, Ahmadov, F, Ahmed, WS, Ai, X, Aielli, G, Aizenberg, I, Akatsuka, S, Akbiyik, M, Åkesson, TPA, Akimov, AV, Khoury, K Al, Alberghi, GL, Albert, J, Albicocco, P, Verzini, MJ Alconada, Alderweireldt, S, Aleksa, M, Aleksandrov, IN, Alexa, C, Alexopoulos, T, Alfonsi, A, Alfonsi, F, Alhroob, M, Ali, B, Ali, S, Aliev, M, Alimonti, G, Allaire, C, Allbrooke, BMM, Allport, PP, Aloisio, A, Alonso, F, Alpigiani, C, Camelia, E Alunno, Estevez, M Alvarez, Alviggi, MG, Coutinho, Y Amaral, Ambler, A, Ambroz, L, Amelung, C, Amidei, D, Santos, SP Amor Dos, Amoroso, S, Amos, KR, Amrouche, CS, Ananiev, V, Anastopoulos, C, Andari, N, Andeen, T, Anders, JK, Andrean, SY, Andreazza, A, Angelidakis, S, Angerami, A, Anisenkov, AV, Annovi, A, Antel, C, Anthony, MT, Antipov, E, Antonelli, M, Antrim, DJA, Anulli, F, Aoki, M, Pozo, JA Aparisi, Aparo, MA, Bella, L Aperio, Aranzabal, N, Ferraz, V Araujo, and Arcangeletti, C

Abstract

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 simulated Monte Carlo events. Detector simulation with Geant4 is accurate but requires significant CPU resources. Over the past decade, ATLAS has developed and utilized tools that replace the most CPU-intensive component of the simulation—the calorimeter shower simulation—with faster simulation methods. Here, AtlFast3, the next generation of high-accuracy fast simulation in ATLAS, is introduced. AtlFast3 combines parameterized approaches with machine-learning techniques and is deployed to meet current and future computing challenges, and simulation needs of the ATLAS experiment. With highly accurate performance and significantly improved modelling of substructure within jets, AtlFast3 can simulate large numbers of events for a wide range of physics processes.

Published
2022

5. Total Cost of Ownership and Evaluation of Google Cloud Resources for the ATLAS Experiment at the LHC

Author

Aad, G, Aakvaag, E, Abbott, B, Abdelhameed, S, Abeling, K, Abicht, NJ, Abidi, SH, Aboelela, M, Aboulhorma, A, Abramowicz, H, Abreu, H, Abulaiti, Y, Acharya, BS, Ackermann, A, Bourdarios, C Adam, Adamczyk, L, Addepalli, SV, Addison, MJ, Adelman, J, Adiguzel, A, Adye, T, Affolder, AA, Afik, Y, Agaras, MN, Agarwala, J, Aggarwal, A, Agheorghiesei, C, Ahmadov, F, Ahmed, WS, Ahuja, S, Ai, X, Aielli, G, Aikot, A, Tamlihat, M Ait, Aitbenchikh, B, Akbiyik, M, Åkesson, TPA, Akimov, AV, Akiyama, D, Akolkar, NN, Aktas, S, Al Khoury, K, Alberghi, GL, Albert, J, Albicocco, P, Albouy, GL, Alderweireldt, S, Alegria, ZL, Aleksa, M, Aleksandrov, IN, Alexa, C, Alexopoulos, T, Alfonsi, F, Algren, M, Alhroob, M, Ali, B, Ali, HMJ, Ali, S, Alibocus, SW, Aliev, M, Alimonti, G, Alkakhi, W, Allaire, C, Allbrooke, BMM, Allen, JF, Flores, CA Allendes, Allport, PP, Aloisio, A, Alonso, F, Alpigiani, C, Alsolami, ZMK, Estevez, M Alvarez, Fernandez, A Alvarez, Cardoso, M Alves, Alviggi, MG, Aly, M, Coutinho, Y Amaral, Ambler, A, Amelung, C, Amerl, M, Ames, CG, Amidei, D, Amini, B, Amirie, KJ, Dos Santos, SP Amor, Amos, KR, Amperiadou, D, An, S, Ananiev, V, Anastopoulos, C, Andeen, T, Anders, JK, Anderson, AC, Andrean, SY, Andreazza, A, Angelidakis, S, Angerami, A, Anisenkov, AV, Annovi, A, and Antel, C

Subjects
Distributed Computing and Systems Software, Information and Computing Sciences, Generic health relevance
Abstract

Abstract: The ATLAS Google Project was established as part of an ongoing evaluation of the use of commercial clouds by the ATLAS Collaboration, in anticipation of the potential future adoption of such resources by WLCG grid sites to fulfil or complement their computing pledges. Seamless integration of Google cloud resources into the worldwide ATLAS distributed computing infrastructure was achieved at large scale and for an extended period of time, and hence cloud resources are shown to be an effective mechanism to provide additional, flexible computing capacity to ATLAS. For the first time a total cost of ownership analysis has been performed, to identify the dominant cost drivers and explore effective mechanisms for cost control. Network usage significantly impacts the costs of certain ATLAS workflows, underscoring the importance of implementing such mechanisms. Resource bursting has been successfully demonstrated, whilst exposing the true cost of this type of activity. A follow-up to the project is underway to investigate methods for improving the integration of cloud resources in data-intensive distributed computing environments and reducing costs related to network connectivity, which represents the primary expense when extensively utilising cloud resources.

Published
2025

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