Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning - INSPIRE

DataBETA

Jet Substructure at the Large Hadron Collider: A Review of Recent Advances in Theory and Machine Learning

Andrew J. Larkoski
(
- Reed Coll.
)
,
Ian Moult
(
- UC, Berkeley and
- LBNL, Berkeley
)
,
Benjamin Nachman
(
- LBL, Berkeley
)

Sep 13, 2017

63 pages

Published in:

Phys.Rept. 841 (2020) 1-63

Published: Jan 27, 2020

e-Print:

1709.04464 [hep-ph]

DOI:

10.1016/j.physrep.2019.11.001 (publication)

View in:

ADS Abstract Service

reference search571 citations

Citations per year

Abstract: (arXiv)

Jet substructure has emerged to play a central role at the Large Hadron Collider (LHC), where it has provided numerous innovative new ways to search for new physics and to probe the Standard Model in extreme regions of phase space. In this article we provide a comprehensive review of state of the art theoretical and machine learning developments in jet substructure. This article is meant both as a pedagogical introduction, covering the key physical principles underlying the calculation of jet substructure observables, the development of new observables, and cutting edge machine learning techniques for jet substructure, as well as a comprehensive reference for experts. We hope that it will prove a useful introduction to the exciting and rapidly developing field of jet substructure at the LHC.

Note:

107 pages double spaced, 34 figures, 613 references; v2 a number of new references included, some minor rewording to the theory section, updates to machine learning section including recent advances. Now submitted to Physics Reports

new physics: search for
standard model
structure
jet: production
CERN LHC Coll
phase space
quantum chromodynamics: perturbation theory
higher-order
resummation
correction: nonperturbative

References(624)

Figures(35)

[1]

Towards an Understanding of the Correlations in Jet Substructure

D. Adams
(
- Brookhaven
)
,
A. Arce
(
- Duke U.
)
,
L. Asquith
(
- U. Sussex (main)
)
,
M. Backovic
(
- Louvain U.
)
,
T. Barillari
(
- Munich, Max Planck Inst.
)

et al.

- Eur.Phys.J.C 75 (2015) 9, 409
•
e-Print:
- 1504.00679
•
DOI:
- 10.1140/epjc/s10052-015-3587-2

[2]

Boosted Objects and Jet Substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd-27th of July 2012

A. Altheimer
(
- Nevis Labs, Columbia U.
)
,
A. Arce
(
- Duke U.
)
,
L. Asquith
(
- Argonne
)
,
J. Backus Mayes
(
- SLAC
)
,
E. Bergeaas Kuutmann
(
- DESY
)

et al.

- Eur.Phys.J.C 74 (2014) 3, 2792
•
e-Print:
- 1311.2708
•
DOI:
- 10.1140/epjc/s10052-014-2792-8

[3]

Jet Substructure at the Tevatron and LHC: New Results, New Tools, New Benchmarks

A. Altheimer
(
- Columbia U.
)
,
S. Arora
(
- Rutgers U., Piscataway
)
,
L. Asquith
(
- Argonne
)
,
G. Brooijmans
(
- Columbia U.
)
,
J. Butterworth
(
- University Coll. London
)

et al.

- J.Phys.G 39 (2012) 063001
•
e-Print:
- 1201.0008
•
DOI:
- 10.1088/0954-3899/39/6/063001

[4]

Boosted Objects: A Probe of Beyond the Standard Model Physics

A. Abdesselam
(
- Oxford U.
)
,
E.Bergeaas Kuutmann
(
- DESY
)
,
U. Bitenc
(
- Freiburg U.
)
,
G. Brooijmans
(
- Columbia U.
)
,
J. Butterworth
(
- University Coll. London
)

et al.

- Eur.Phys.J.C 71 (2011) 1661
•
e-Print:
- 1012.5412
•
DOI:
- 10.1140/epjc/s10052-011-1661-y

[5]

Towards Jetography

Gavin P. Salam
(
- Paris, LPTHE
)

- Eur.Phys.J.C 67 (2010) 637-686
•
e-Print:
- 0906.1833
•
DOI:
- 10.1140/epjc/s10052-010-1314-6

[6]

Jet Substructure

Jessie Shelton
(
- Yale U. and
- Harvard U., Phys. Dept.
)

[7]

Top Tagging

Tilman Plehn
(
- Heidelberg U.
)
,
Michael Spannowsky
(
- Durham U., IPPP
)

- J.Phys.G 39 (2012) 083001
•
e-Print:
- 1112.4441
•
DOI:
- 10.1088/0954-3899/39/8/083001

[8]

Phenomenological and theoretical developments in jet physics at the LHC

Matteo Cacciari
(
- Diderot U., Paris and
- Paris, LPTHE and
- UPMC, Paris (main)
)

- Int.J.Mod.Phys.A 30 (2015) 31, 1546001
•
e-Print:
- 1509.02272
•
DOI:
- 10.1142/S0217751X1546001X

[9]

Looking inside jets: an introduction to jet substructure and boosted-object phenomenology

Simone Marzani
(
- Genoa U. and
- INFN, Genoa
)
,
Gregory Soyez
(
- IPhT, Saclay
)
,
Michael Spannowsky
(
- Durham U., IPPP
)

- Lecture Notes in Physics, volume 958 (2019),
- Lect.Notes Phys. 958 (2019) pp.
•
e-Print:
- 1901.10342
•
DOI:
- 10.1007/978-3-030-15709-8

[10]

Partons, factorization and resummation, TASI 95

George F. Sterman
(
- SUNY, Stony Brook
)

•
e-Print:
- hep-ph/9606312

[11]

Lectures on the soft-collinear effective theory

I.W. Stewart
,
C.W. Bauer

[12]

Quantum Field Theory and the Standard Model

M.D. Schwartz

[13]

QCD and collider physics

R.K. Ellis
,
W.J. Stirling
,
B.R. Webber

[14]

Foundations of Perturbative QCD

J. Collins

[15]

Introduction to Soft-Collinear Effective Theory

Thomas Becher
(
- U. Bern, AEC
)
,
Alessandro Broggio
(
- PSI, Villigen
)
,
Andrea Ferroglia
(
- New York City Coll. Tech. and
- CUNY, Graduate School - U. Ctr.
)

- Lect.Notes Phys. 896 (2015) pp.1-206
•
e-Print:
- 1410.1892
•
DOI:
- 10.1007/978-3-319-14848-9

[16]

QCD resummation for hadronic final states

- J.Phys.G 42 (2015) 10, 103101
•
e-Print:
- 1505.04084
•
DOI:
- 10.1088/0954-3899/42/10/103101

[17]

The Black Book of Quantum Chromodynamics

J. Campbell
,
J. Huston
,
F. Krauss

[18]

Deep Learning

I. Goodfellow
,
Y. Bengio
,
A. Courville

[19]

Deep learning in neural networks: An overview

Jürgen Schmidhuber

- Neural Networks 61 (2015) 85-117,
- Neural Networks 61 (2015) 85-117
•
DOI:
- 10.1016/j.neunet.2014.09.003

[20]

Deep learning

- Nature 521 (2015) 436-444
•
DOI:
- 10.1038/nature14539

[21]

Deep Learning and its Application to LHC Physics

- Ann.Rev.Nucl.Part.Sci. 68 (2018) 161-181
•
e-Print:
- 1806.11484
•
DOI:
- 10.1146/annurev-nucl-101917-021019

[22]

Machine learning at the energy and intensity frontiers of particle physics

Alexander Radovic
(
- William-Mary Coll.
)
,
Mike Williams
(
- MIT
)
,
David Rousseau
(
- Orsay, LAL
)
,
Michael Kagan
(
- SLAC
)
,
Daniele Bonacorsi
(
- U. Bologna (main) and
- INFN, Bologna
)

et al.

- Nature 560 (2018) 7716, 41-48
•
DOI:
- 10.1038/s41586-018-0361-2

[23]

Scikit-learn: Machine Learning in Python

Fabian Pedregosa
(
- INRIA, Saclay
)
,
Gaël Varoquaux
(
- INRIA, Saclay
)
,
Alexandre Gramfort
(
- INRIA, Saclay
)
,
Olivier Grisel
(
- Unlisted, FR
)
,
Mathieu Blondel
(
- Kobe U.
)

et al.

- J.Machine Learning Res. 12 (2011) 2825-2830
•
e-Print:
- 1201.0490

[24]

Keras

F. Chollet

[25]

Tensorflow: A system for large-scale machine learning

M. Abadi

- OSDI vol.16 (2016) 265

1-25 of 624
1
2
3
4
5
•••
25
25 / page