Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning

Larkoski, Andrew J.; Moult, Ian; Nachman, B. P.

doi:10.1016/j.physrep.2019.11.001

Cited by 425 publications

(453 citation statements)

References 907 publications

(577 reference statements)

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“…Together with these advances in understanding perturbative and nonperturbative dynamics of jets, there has also been a surge of interest in jet substructure techniques [45][46][47][48][49]. This includes in particular the use of jet grooming to remove soft radiation from jets, with the goal of reducing the effects from hadronization, underlying event, and pileup.…”

Section: Contentsmentioning

confidence: 99%

Nonperturbative corrections to soft drop jet mass

Hoang

Mantry

Pathak

et al. 2019

J. High Energ. Phys.

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We provide a quantum field theory based description of the nonperturbative effects from hadronization for soft drop groomed jet mass distributions using the soft-collinear effective theory and the coherent branching formalism. There are two distinct regions of jet mass m J where grooming modifies hadronization effects. In a region with intermediate m J an operator expansion can be used, and the leading power corrections are given by three universal nonperturbative parameters that are independent of all kinematic variables and grooming parameters, and only depend on whether the parton initiating the jet is a quark or gluon. The leading power corrections in this region cannot be described by a standard normalized shape function. These power corrections depend on the kinematics of the subjet that stops soft drop through short distance coefficients, which encode a perturbatively calculable dependence on the jet transverse momentum, jet rapidity, and on the soft drop grooming parameters z cut and β. Determining this dependence requires a resummation of large logarithms, which we carry out at LL order. For smaller m J there is a nonperturbative region described by a one-dimensional shape function that is unusual because it is not normalized to unity, and has a non-trivial dependence on β. A Measurement Operator for the Boundary Term 62 B Collinear-Soft Function with a Probe Nonperturbative Gluon 64 B.1 Analysis with One Perturbative Gluon 64 B.2 Analysis with Two Perturbative Emissions 67 -1 -soft drop operator expansion (SDOE) region: QΛ QCD m 2 J m 2 J QQ cut 1 2+β Figure 2. Pythia prediction for m 2 J /E 2 J distribution indicating three different regions of the spectrum. Here we take R ee 0 = π 2 and the inequality " " in the "p + cs p + Λ " constraint for the SDOE region is replaced by a factor of 5.In Fig. 2 we show a Pythia8 Monte Carlo prediction for this groomed jet mass spectrum with z cut = 0.1 and β = 2. We distinguish three relevant regions of the spectrum: the soft drop nonperturbative region (SDNP) to the far left (left of the magenta dashed line), the soft drop operator expansion region (SDOE) in the middle (between the dashed lines), and the ungroomed resummation region on the far right where soft drop turns off but the log resummation for the ungroomed case is still active (right of the green dashed line). The distinction between the SDNP and SDOE regions is determined by Eq. (1.1), while the distinction between the SDOE and ungroomed resummation region is given by soft drop operator expansion (SDOE):ungroomed resummation region:For the hemisphere case in e + e − one has R = π/2. For R = 1, in the e + e − case this boundary roughly corresponds to m 2 J /E 2 J = z cut which we use in Fig. 2. In all three of these regions the resummation of large logarithms is important, though the precise nature of this resummation is different. There is an additional transition between resummation and fixed-order regions which occurs on the very far right of Fig. 2, near where dσ/dm J goes to zero (not indicated in th...

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Section: Contentsmentioning

confidence: 99%

Nonperturbative corrections to soft drop jet mass

Hoang

Mantry

Pathak

et al. 2019

J. High Energ. Phys.

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“…In the boosted regime, the resulting jets from the hadronic decay of these resonances are merged, and the result is a fat jet of wide radius. Using the difference in radiation pattern inside these fat jets, captured by various substructure variables, single variable (SV) [1,2] as well as multi-variable (MV) machine learning based methods have been shown to allow a good discrimination of these signals from QCD background (see [25,26] for a review of machine learning based techniques in high energy physics).…”

Section: Introductionmentioning

confidence: 99%

Mass agnostic jet taggers

Layne¹,

Mishra

Mitridate

et al. 2020

SciPost Phys.

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Distinguishing boosted objects in hadronic final states requires a combined understanding of robust distributions for signal and background events. Substructure based approaches can isolate signal events in hadronic channels but tend to distort defining features of the background to be more signal-like (such as a smoothly falling invariant mass distribution). Getting the most out of experimental efforts needs a balance between the two competing effects of signal identification and background distortion. In this work, we perform a systematic study of various jet tagging methods that aim for this balance. We explore both single variable and multivariate approaches. The methods preserve the shape of the background distribution by either augmenting the training procedure or the data itself. Multiple quantitative metrics to compare the methods are considered, for tagging 2-, 3-, or 4-prong jets from the QCD background. This is the first study to show that the data augmentation techniques of Planing and PCA based scaling deliver similar performance as the augmented training techniques of Adversarial NNs and uBoost, but are both easier to implement and computationally cheaper.

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“…This computational speedup also applies to substructure observables derived from β = 2 EFPs. A number of different observables have been proposed to tag jets with multi-prong hadronic substructure at the LHC, including 2-pronged decays of boosted W bosons and 3pronged decays of boosted top quarks, along with other more exotic scenarios [62,63]. These jet substructure observables have since found significant experimental application at the LHC for multi-prong tagging and new physics searches [64].…”

Section: Speeding Up Multi-prong Taggersmentioning

confidence: 99%

Cutting multiparticle correlators down to size

2020

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Multiparticle correlators are mathematical objects frequently encountered in quantum field theory and collider physics. By translating multiparticle correlators into the language of graph theory, we can gain new insights into their structure as well as identify efficient ways to manipulate them. In this paper, we highlight the power of this graph-theoretic approach by "cutting open" the vertices and edges of the graphs, allowing us to systematically classify linear relations among multiparticle correlators and develop faster methods for their computation. The naive computational complexity of an N -point correlator among M particles is O(M N ), but when the pairwise distances between particles can be cast as an inner product, we show that all such correlators can be computed in linear O(M ) runtime. With the help of new tensorial objects called Energy Flow Moments, we achieve a fast implementation of jet substructure observables like C2 and D2, which are widely used at the Large Hadron Collider to identify boosted hadronic resonances. As another application, we compute the number of leafless multigraphs with d edges up to d = 16 (15,641,159), conjecturing that this is the same as the number of independent kinematic polynomials of degree d, previously known only to d = 8 (279).

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Jet substructure at the Large Hadron Collider: A review of recent advances in theory and machine learning

Cited by 425 publications

References 907 publications

Nonperturbative corrections to soft drop jet mass

Nonperturbative corrections to soft drop jet mass

Mass agnostic jet taggers

Cutting multiparticle correlators down to size

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