Toward the end-to-end optimization of particle physics instruments with differentiable programming

被引：15

作者：

Dorigo T. ^{[1
,2
,24
]}

Giammanco A. ^{[1
,3
,24
]}

Vischia P. ^{[1
,3
,24
]}

Aehle M. ^{[4
]}

Bawaj M. ^{[5
]}

Boldyrev A. ^{[1
,6
]}

de Castro Manzano P. ^{[1
,2
]}

Derkach D. ^{[1
,6
]}

Donini J. ^{[1
,7
,24
]}

Edelen A. ^{[8
]}

Fanzago F. ^{[1
,2
]}

Gauger N.R. ^{[4
]}

Glaser C. ^{[1
,9
]}

Baydin A.G. ^{[1
,10
]}

Heinrich L. ^{[1
,11
]}

Keidel R. ^{[12
]}

Kieseler J. ^{[1
,13
]}

Krause C. ^{[1
,14
]}

Lagrange M. ^{[1
,3
]}

Lamparth M. ^{[1
,11
]}

Layer L. ^{[1
,2
,15
]}

Maier G. ^{[16
]}

Nardi F. ^{[1
,2
,7
,17
]}

Pettersen H.E.S. ^{[18
]}

Ramos A. ^{[19
]}

Ratnikov F. ^{[1
,6
]}

Röhrich D. ^{[20
]}

de Austri R.R. ^{[19
]}

del Árbol P.M.R. ^{[1
,21
]}

Savchenko O. ^{[2
,3
]}

Simpson N. ^{[22
]}

Strong G.C. ^{[1
,2
]}

Taliercio A. ^{[3
]}

Tosi M. ^{[1
,2
,17
]}

Ustyuzhanin A. ^{[1
,25
]}

Zaraket H. ^{[1
,23
]}

机构：

[1] Istituto Nazionale di Fisica Nucleare, Sezione di Padova

[2] Centre for Cosmology, Particle Physics and Phenomenology (CP3), Université catholique de Louvain

[3] Chair for Scientific Computing, Technische Universität Kaiserslautern

[4] Università di Perugia and INFN, Sezione di Perugia

[5] Université Clermont Auvergne, Laboratoire de Physique de Clermont

[6] Department of Physics and Astronomy, Uppsala University

[7] Department of Computer Science, University of Oxford

[8] Physik-Department, Technische Universität München

[9] Center for Technology and Transfer, University of Applied Sciences Worms

[10] NHETC, Department of Physics and Astronomy, Rutgers University

[11] Department of Oncology and Medical Physics, Haukeland University Hospital

[12] Instituto de Física Corpuscular, UV-CSIC

[13] Department of Physics and Technology, University of Bergen

[14] Instituto de Física de Cantabria, UC-CSIC

[15] Multi-Disciplinary Physics Laboratory, Optics and Fiber Optics Group, Faculty of Sciences, Lebanese University

[16] Constructor University Bremen gGmbH, Campus Ring 1, Bremen

来源：

Reviews in Physics | 2023年 / 10卷

基金：

欧盟地平线“2020”; 美国国家科学基金会;

关键词：

Astrophysics; Differentiable programming; Machine learning; Nuclear physics; Optimization; Particle detectors; Particle physics;

D O I：

10.1016/j.revip.2023.100085

中图分类号：

学科分类号：

摘要：

The full optimization of the design and operation of instruments whose functioning relies on the interaction of radiation with matter is a super-human task, due to the large dimensionality of the space of possible choices for geometry, detection technology, materials, data-acquisition, and information-extraction techniques, and the interdependence of the related parameters. On the other hand, massive potential gains in performance over standard, “experience-driven” layouts are in principle within our reach if an objective function fully aligned with the final goals of the instrument is maximized through a systematic search of the configuration space. The stochastic nature of the involved quantum processes make the modeling of these systems an intractable problem from a classical statistics point of view, yet the construction of a fully differentiable pipeline and the use of deep learning techniques may allow the simultaneous optimization of all design parameters. In this white paper, we lay down our plans for the design of a modular and versatile modeling tool for the end-to-end optimization of complex instruments for particle physics experiments as well as industrial and medical applications that share the detection of radiation as their basic ingredient. We consider a selected set of use cases to highlight the specific needs of different applications. © 2023 The Author(s)

引用

共 50 条

[41] End-to-End Optimization of Deep Learning Applications
Sohrabizadeh, Atefeh
Wang, Jie
Cong, Jason
2020 ACM/SIGDA INTERNATIONAL SYMPOSIUM ON FIELD-PROGRAMMABLE GATE ARRAYS (FPGA '20), 2020, : 133 - 139
[42] End-to-end learning of multiple sequence alignments with differentiable Smith-Waterman
Petti, Samantha
Bhattacharya, Nicholas
Rao, Roshan
Dauparas, Justas
Thomas, Neil
Zhou, Juannan
Rush, Alexander M.
Koo, Peter
Ovchinnikov, Sergey
BIOINFORMATICS, 2023, 39 (01)
[43] End-to-end Lane Detection through Differentiable Least-Squares Fitting
Van Gansbeke, Wouter
De Brabandere, Bert
Neven, Davy
Proesmans, Marc
Van Gool, Luc
2019 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION WORKSHOPS (ICCVW), 2019, : 905 - 913
[44] End-to-end Deep Learning of Optimization Heuristics
Cummins, Chris
Petoumenos, Pavlos
Wang, Zheng
Leather, Hugh
2017 26TH INTERNATIONAL CONFERENCE ON PARALLEL ARCHITECTURES AND COMPILATION TECHNIQUES (PACT), 2017, : 219 - 232
[45] End-to-End Constrained Optimization Learning: A Survey
Kotary, James
Fioretto, Ferdinando
Van Hentenryck, Pascal
Wilder, Bryan
PROCEEDINGS OF THE THIRTIETH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2021, 2021, : 4475 - 4482
[46] End-to-end LPCNet: A Neural Vocoder With Fully-Differentiable LPC Estimation
Subramani, Krishna
Valin, Jean-Marc
Isik, Umut
Smaragdis, Paris
Krishnaswamy, Arvindh
INTERSPEECH 2022, 2022, : 818 - 822
[47] Steps Toward End-to-End Personalized AAL Services
Cesta, Amedeo
Coraci, Luca
Cortellessa, Gabriella
De Benedictis, Riccardo
Orlandini, Andrea
Palumbo, Filippo
Stimec, Ales
WORKSHOP PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT ENVIRONMENTS, 2013, 17 : 78 - 89
[48] Toward an efficient End-to-End test suite execution
Augusto, Cristian
2023 IEEE 34TH INTERNATIONAL SYMPOSIUM ON SOFTWARE RELIABILITY ENGINEERING WORKSHOPS, ISSREW, 2023, : 26 - 29
[49] Toward End-to-End Object Detection and Tracking on the Edge
Tabkhi, Hamed
SEC 2017: 2017 THE SECOND ACM/IEEE SYMPOSIUM ON EDGE COMPUTING (SEC'17), 2017,
[50] An end-to-end test of neutron stars as particle accelerators
Caraveo, PA
PROCEEDINGS OF THE X-RAY UNIVERSE 2005, VOLS 1 AND 2, 2006, 604 : 131 - 138

← 1 2 3 4 5 →