Jet maximization, axis minimization, and stable cone finding

Thaler, Jesse

doi:10.1103/physrevd.92.074001

Cited by 9 publications

(10 citation statements)

References 48 publications

(109 reference statements)

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“…where p A = i∈A p i is the total four-momentum of all jet constituents. Introducing a Lagrange multiplier λ (as in [50]), the quantity…”

Section: Update Step For General Measuresmentioning

confidence: 99%

“…[7] (see also ref. [33,50]). For this reason, we will focus the remainder of our discussion on jet measures in the SCET I category, including the XCone default.…”

Section: Impact Of Axes Minimizationmentioning

confidence: 99%

“…Stable cone finding is closely related to 1-jettiness minimization with β = 2 [33], and similar algorithms are relevant for a recently proposed "jet function" 2 optimization strategy [47][48][49]. One can even prove an exact equivalence between these algorithms when finding a single cone jet of fixed opening angle [50]. Finding the thrust axis [51] is related to 2-jettiness minimization with β = 2.…”

Section: Introductionmentioning

confidence: 99%

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XCone: N-jettiness as an exclusive cone jet algorithm

Stewart

Tackmann

Thaler

et al. 2015

J. High Energ. Phys.

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We introduce a new jet algorithm called XCone, for eXclusive Cone, which is based on minimizing the event shape N -jettiness. Because N -jettiness partitions every event into N jet regions and a beam region, XCone is an exclusive jet algorithm that always returns a fixed number of jets. We use a new "conical geometric" measure for which wellseparated jets are bounded by circles of radius R in the rapidity-azimuth plane, while overlapping jet regions automatically form nearest-neighbor "clover jets". This avoids the split/merge criteria needed in inclusive cone algorithms. A key feature of XCone is that it smoothly transitions between the resolved regime where the N signal jets of interest are well separated and the boosted regime where they overlap. The returned value of Njettiness also provides a quality criterion of how N -jet-like the event looks. We also discuss the N -jettiness factorization theorems that occur for various jet measures, which can be used to compute the associated exclusive N -jet cross sections. In a companion paper [1], the physics potential of XCone is demonstrated using the examples of dijet resonances, Higgs decays to bottom quarks, and all-hadronic top pairs.

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“…where p A = i∈A p i is the total four-momentum of all jet constituents. Introducing a Lagrange multiplier λ (as in [50]), the quantity…”

Section: Update Step For General Measuresmentioning

confidence: 99%

“…[7] (see also ref. [33,50]). For this reason, we will focus the remainder of our discussion on jet measures in the SCET I category, including the XCone default.…”

Section: Impact Of Axes Minimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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XCone: N-jettiness as an exclusive cone jet algorithm

Stewart

Tackmann

Thaler

et al. 2015

J. High Energ. Phys.

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“…A variety of N -jettiness measures have been proposed in the literature (most especially in refs. [20,21], see also [22,23]), but here we stick exclusively to the XCone recommended measure, namely the conical geometric measure with γ = 1 [1]:…”

mentioning

confidence: 99%

Resolving boosted jets with XCone

Thaler

Wilkason

2015

J. High Energ. Phys.

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We show how the recently proposed XCone jet algorithm [1] smoothly interpolates between resolved and boosted kinematics. When using standard jet algorithms to reconstruct the decays of hadronic resonances like top quarks and Higgs bosons, one typically needs separate analysis strategies to handle the resolved regime of well-separated jets and the boosted regime of fat jets with substructure. XCone, by contrast, is an exclusive cone jet algorithm that always returns a fixed number of jets, so jet regions remain resolved even when (sub)jets are overlapping in the boosted regime. In this paper, we perform three LHC case studies -dijet resonances, Higgs decays to bottom quarks, and all-hadronic top pairs -that demonstrate the physics applications of XCone over a wide kinematic range.

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“…As discussed in Ref. [147], there is a close relationship between exclusive cone finding algorithms, stable cone algorithms [148][149][150], and jet maximization algorithms [151][152][153][154][155]. For the choice of 𝛽 = 2, the jet axis aligns with the jet momentum direction, which is known as the stable cone criterion [148,149].…”

Section: General 𝑁 : Exclusive Cone Findingmentioning

confidence: 99%

Energy Flow in Particle Collisions

Metodiev

2020

Preprint

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In this thesis, I introduce a new bottom-up approach to quantum field theory and collider physics, beginning from the observable energy flow: the energy distribution produced by particle collisions. First, I establish a metric space for collision events by comparing their energy flows. I unify many ideas spanning multiple decades, such as observables and jets, as simple geometric objects in this new space. Second, I develop a basis of observables by systematically expanding in particle energies and angles, encompassing many existing observables and uncovering new analytic structures. I highlight how the traditional criteria for theoretical calculability emerge as consistency conditions, due to the redundancy of describing an event using particles rather than its energy flow. Finally, I propose a definition of particle type, or flavor, which makes use of only observable information. This definition requires refining the notion of flavor from a per-event label to a statistical category, and I showcase its direct experimental applicability at colliders. Throughout, I synthesize concepts from particle physics with ideas from statistics and computer science to expand the theoretical understanding of particle interactions and enhance the experimental capabilities of collider data analysis techniques.

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Jet maximization, axis minimization, and stable cone finding

Cited by 9 publications

References 48 publications

XCone: N-jettiness as an exclusive cone jet algorithm

XCone: N-jettiness as an exclusive cone jet algorithm

Resolving boosted jets with XCone

Energy Flow in Particle Collisions

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