Soft drop groomed jet angularities at the LHC

Self Cite

We present results for the soft drop groomed jet radius R g at next-to-leading logarithmic accuracy. The radius of a groomed jet which corresponds to the angle between the two branches passing the soft drop criterion is one of the characteristic observables relevant for the precise understanding of groomed jet substructure. We establish a factorization formalism that allows for the resummation of all relevant large logarithms, which is based on demonstrating the all order equivalence to a jet veto in the region between the boundaries of the groomed and ungroomed jet. Non-global logarithms including clustering effects due to the Cambridge/Aachen algorithm are resummed to all orders using a suitable Monte Carlo algorithm. We perform numerical calculations and find a very good agreement with Pythia 8 simulations. We provide theoretical predictions for the LHC and RHIC.

Section: Refactorization Of the Semi-inclusive Jet Functionsupporting

confidence: 76%

Section: Introductionmentioning

confidence: 99%

The soft drop groomed jet radius at NLL

Kang

Lee

Liu

et al. 2020

Self Cite

“…Soft drop groomed observables have been recently measured by ATLAS [51] and CMS [52]. Perturbative methods have been developed to carry out calculations for groomed jets [50,[53][54][55][56], and soft-drop factorization theorems have been derived for D 2 [57], the 2-point energy correlator, e (α) 2 [55], and the jet-mass and angularities for inclusive jets [58,59]. Resummation of groomed event shapes at an e + e − collider such as soft drop thrust, hemisphere jet mass, and narrow invariant jet mass were studied in Ref.…”

Section: Contentsmentioning

confidence: 99%

Nonperturbative corrections to soft drop jet mass

Hoang

Mantry

Pathak

et al. 2019

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...

“…In this analysis, values of z cut = 0.1 and β SD = 0.0 are used, based on previous ATLAS studies [18], which is equivalent to modified mass drop tagger [74]. An important feature of soft-drop is that groomed observables are analytically calculable to high-order resummation accuracy [75][76][77].…”

Section: Definition Of the Jet Observablesmentioning

confidence: 99%

Measurement of jet-substructure observables in top quark, W boson and light jet production in proton-proton collisions at $$ \sqrt{s} $$ = 13 TeV with the ATLAS detector

Aaboud¹,

Aad²,

Abbott³

et al. 2019

A measurement of jet substructure observables is presented using data collected in 2016 by the ATLAS experiment at the LHC with proton-proton collisions at √ s = 13 TeV. Large-radius jets groomed with the trimming and soft-drop algorithms are studied. Dedicated event selections are used to study jets produced by light quarks or gluons, and hadronically decaying top quarks and W bosons. The observables measured are sensitive to substructure, and therefore are typically used for tagging large-radius jets from boosted massive particles. These include the energy correlation functions and the N-subjettiness variables. The number of subjets and the Les Houches angularity are also considered. The distributions of the substructure variables, corrected for detector effects, are compared to the predictions of various Monte Carlo event generators. They are also compared between the large-radius jets originating from light quarks or gluons, and hadronically decaying top quarks and W bosons.The ATLAS experiment uses a multipurpose particle detector [25, 26] with a forward-backward symmetric cylindrical geometry and a near 4π coverage in solid angle.1 It consists of an inner tracking detector (ID) surrounded by a thin superconducting solenoid providing a 2 T axial magnetic field, electromagnetic (EM) and hadron calorimeters, and a muon spectrometer. The ID consists of silicon pixel, silicon microstrip, and straw-tube transition-radiation tracking detectors, covering the pseudorapidity range |η| < 2.5. The calorimeter system covers the pseudorapidity range |η| < 4.9. Electromagnetic calorimetry is performed with barrel and endcap high-granularity lead/liquid-argon (LAr) sampling calorimeters, within the region |η| < 3.2. There is an additional thin LAr presampler covering |η| < 1.8, to correct for energy loss in material upstream of the calorimeters. For |η| < 2.5, the LAr calorimeters are divided into three layers in depth. Hadronic calorimetry is performed with a steel/scintillator-tile calorimeter, segmented into three barrel structures within |η| < 1.7, and two copper/LAr hadronic endcap calorimeters, which cover the region 1.5 < |η| < 3.2. The forward solid angle up to |η| = 4.9 is covered by copper/LAr and tungsten/LAr calorimeter modules, which are optimised for energy measurements of electrons/photons and hadrons, respectively. The muon spectrometer consists of separate trigger and high-precision tracking chambers that measure the deflection of muons in a magnetic field generated by superconducting air-core toroids.The ATLAS detector selects events using a tiered trigger system [27]. The first level is implemented in custom electronics. The second level is implemented in software running on a general-purpose processor farm which processes the events and reduces the rate of recorded events to 1 kHz. Monte Carlo samplesSimulated events are used to optimise the event selection, correct the data for detector effects and estimate systematic uncertainties. The predictions of different phenomenological models implemented in t...