The Catchment Area of Jets
By Matteo Cacciari, G. Salam, G. Soyez. 2008
987 citations recorded by the source corpus.
The area of a jet is a measure of its susceptibility to radiation, like pileup or underlying event (UE), that on average, in the jet’s neighbourhood, is uniform in rapidity and azimuth. In this article we establish a theoretical grounding for the discussion of jet areas, introducing two main definitions, passive and active areas, which respectively characterise the sensitivity to pointlike or diffuse pileup and UE radiation. We investigate the properties of jet areas for three standard jet algorithms, kt, Cambridge/Aachen and SISCone. Passive areas for single-particle jets are equal to the naive geometrical expectation πR 2 , but acquire an anomalous dimension at higher orders in the coupling, calculated here at leading order. The more physically relevant active areas differ from πR 2 even for single-particle jets, substantially so in the case of the cone algorithms like SISCone with a Tevatron Run-II split–merge procedure. We compare our results with direct measures of areas in parton-shower Monte Carlo simulations and find good agreement with the main features of the analytical predictions. We furthermore justify the use of jet areas to subtract the contamination from pileup.
Subfields: Nuclear and High Energy Physics, AI for Physical Sciences
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Nuclear and High Energy Physics
- Cms Collaboration
- Atlas Collaboration
- A. Larkoski
- Benjamin Nachman
- I. Moult
- Jesse Thaler
- G. Salam
- M. Schönherr
In AI for Physical Sciences
- Atlas Collaboration
- Cms Collaboration
- Jesse Thaler
- A. Larkoski
- Benjamin Nachman
- F. Krauss
- G. Salam
- Michael Spannowsky
Citations in the corpus
- Learning Particle Physics by Example: Location-Aware Generative Adversarial Networks for Physics Synthesis (2017, methodology): “Cells with positive energy are assigned to jets using the anti-kt clustering algorithm [26] with a radius parameter of R = 1.”
- A new method to distinguish hadronically decaying boosted Z bosons from W bosons using the ATLAS detector (2015, neutral): “Tracks are associated with jets using ghost association [40].”
- Measurement of the differential cross section for top quark pair production in pp collisions at \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin} (2015, background): “Subsequently, the remaining neutral-hadron component from pileup is accounted for through jet energy corrections [33].”
- Performance of jet substructure techniques for large-R jets in proton-proton collisions at $ \sqrt{s}=7 $ TeV using the ATLAS detector (2013, background): “Ghost-association [94, 95] provides a much more appropriate matching of the tracks to the calorimeter subjets for this scenario.”
- Soft drop (2014, background): “Roughly speaking, the active jet area [108] is ' πR2 g.”
- Boosted objects and jet substructure at the LHC. Report of BOOST2012, held at IFIC Valencia, 23rd–27th of July 2012 (2013, methodology): “2. Boosted objects at the LHC 13 To evaluate the eect of this correction, the transverse momentum ratio R p T is introduced as R p T = p match T p T ˆA = p T corr T : (3) Here Ais the catchment area [67] of the pile-up jet, and pmatch T is the matching truth jet p T. The matching criterion is simila”
- Evidence for the associated production of the Higgs boson and a top quark pair with the ATLAS detector (2017, neutral): “of the muon candidate’s track, respectively, and is corrected for leakage from the electron’s shower and for the ambient energy in the event [91,92].”
- Looking Inside Jets (2019, methodology): “R 3. JETS AND JET ALGORITHMS 32 Figure 3.2: Jets obtained with the Cambridge/Aachen (left) and anti-kt (right) algorithms with R = 1. The shaded regions correspond to the (active) catchment area (see [64]) of each jet. While the jets obtained with the Cambridge/Aachen algorithm have complex boundari”
- Measurement of the photon identification efficiencies with the ATLAS detector using LHC Run-1 data (2016, neutral): “[24,25].”
- Les Houches 2017: Physics at TeV Colliders Standard Model Working Group Report (2017, methodology): “The standard approach for most applications is the area–median subtraction method [455, 681, 682, 837] (potentially using the ConstituentSubtractor [686] for subtracting pileup from jet shapes).”