Jet Substructure at High Luminosity - Nevis … Substructure at High Luminosity Author: Luz ......

Columbia University Summer 2013 REU

August 3rd, 2013

Jet Substructure at High Luminosity

Author:Luz Jimenez-Vela

Advisor:Dr. Emily Thompson

Abstract

This paper introduces pile-up in ATLAS, the impact of mitigationof pileup on large-R jets via grooming techniques, and the perfor-mance of grooming in the context of pileup. It takes a closer lookat the trimming algorithm as the grooming technique used and jetsubstructure variables at high luminosity using two mu-scan samples.The observations show prosperous results for potential new physics asthe LHC turns on again in 2015 with its nominal energy of 7TeV fromeach beam.

Contents

Preface 3The Large Hadron Collider . . . . . . . . . . . . . . . . . . . . . . . 4ATLAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5The Standard Model of Particle Physics . . . . . . . . . . . . . . . 7

1 Introduction 71.1 Pileup in ATLAS . . . . . . . . . . . . . . . . . . . . . . . . . 71.2 The Impact of Mitigation of Pile-up on Large-R Jets . . . . . 81.3 Performance of Grooming in the Context of Pileup[7] . . . . . 91.4 The Trimming Algorithm . . . . . . . . . . . . . . . . . . . . . 91.5 Jet Substructure Variables . . . . . . . . . . . . . . . . . . . . 10

2 Delphes 112.1 Understanding Parameters . . . . . . . . . . . . . . . . . . . . 122.2 Understanding Output of Data . . . . . . . . . . . . . . . . . 13

3 ATLAS Simulations 133.1 No Jet Grooming vs Trimming . . . . . . . . . . . . . . . . . . 15

3.1.1 Jet Mass . . . . . . . . . . . . . . . . . . . . . . . . . . 153.1.2 nClusters . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.2 50ns vs 25ns . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173.2.1 Jet Mass . . . . . . . . . . . . . . . . . . . . . . . . . . 173.2.2 nClusters . . . . . . . . . . . . . . . . . . . . . . . . . 183.2.3 Subjets . . . . . . . . . . . . . . . . . . . . . . . . . . 193.2.4 nRejected . . . . . . . . . . . . . . . . . . . . . . . . . 20

3.3 fcut = 0.05, Rsub = 0.3 vs fcut = 0.03, Rsub = 0.2 . . . . . . . . 213.3.1 Jet Mass . . . . . . . . . . . . . . . . . . . . . . . . . . 213.3.2 nClusters . . . . . . . . . . . . . . . . . . . . . . . . . 223.3.3 nSubjets . . . . . . . . . . . . . . . . . . . . . . . . . . 233.3.4 nRejected . . . . . . . . . . . . . . . . . . . . . . . . . 24

3.4 Optimal Threshold Values vs Varying Threshold Values . . . . 253.4.1 nClusters: No Jet Grooming . . . . . . . . . . . . . . . 253.4.2 nClusters: Trimmed - Standard Parameter . . . . . . . 263.4.3 nRejected: Trimmed- Standard Parameter . . . . . . . 273.4.4

√d12: No Jet Grooming . . . . . . . . . . . . . . . . . 29

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3.4.5√d23: No Jet Grooming . . . . . . . . . . . . . . . . . 29

3.4.6 N-subjettiness τ21: No Jet Grooming . . . . . . . . . . 303.4.7 N-subjettiness τ32: No Jet Grooming . . . . . . . . . . 30

4 Jet Mass: Not Jet Grooming & Trimmed 314.1 ATLAS Simulation for Approval . . . . . . . . . . . . . . . . . 31

5 Conclusion 32

6 Acknowledgements at CERN 33

7 Recommendations for Upcoming Summer REU Students 34

8 Bibliography 35

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Preface

The particle physics laboratory, CERN, where the Large Hadron Collideris located, was founded in 1954 by twelve countries (Belgium, Denmark,France, the Federal Republic of Germany, Greece, Italy, the Netherlands,Norway, Sweden, Switzerland, the United Kingdom, and Yugoslavia) whoseplan was to reenergize physics in Europe after its wars.Today, it has 20member states, the twelve above as well as Austria, Spain, Portugal, Finland,Poland, Czechoslovak Republic, Hungary, and Bulgaria.[1] In addition to its20 member states, many non-European countries are also involved, includingthe US with 44 US institutions participating in the ATLAS project.[2] Thelaboratory is located in Meyrin, Switzerland, and sits on the Franco-Swissborder near Geneva.The name CERN comes from the French acronym “Conseil Europeen pourla Recherche Nucleaire,” or European Council for Nuclear Research, wherethe word “nuclear” comes from the 1952 purpose of understanding the insideof the atom.

Figure 1: The LHC at CERN, Switzerland/France (credits: cern.ch)

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The Large Hadron Collider

The Large Hadron Collider at CERN sits on the Franco-Swiss border nearGeneva.It consists of a 27 kilometer ring of superconducting magnets with anumber of accelerating structures to boost the energy of the particles alongthe way.[1] Specifically, there are a total of 9593 magnets at 1.9 Kelvin, 1,232of them are dipole magnets and 392 of them are quadrupole magnets.[3]The LHC circulates these particles, protons, within bunches in two differentbeam pipes, each with a nominal energy of 7TeV, one moving clockwiseand the other counterclockwise.These two beams are brought together intoa collision (a crossing) at the locations where the experiment detectors arelocated.There are four main particle detectors - ATLAS, CMS, ALICE, andLHCb; ATLAS and CMS are the two biggest, general-purpose experimentswhich participated in detecting the Higgs.

Figure 2: The ATLAS detector’s components & description of its coordinates.z: beam-line direction. φ: around beam-line, on x-y transverse plane. η =−lntan(θ/2): pseudo-rapidity, on y-z plane. θ: on x-z axis. (credits: atlas.ch)

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ATLAS

The ATLAS (A Toroidal LHC Apparatus) detector is 46 meters long, 25meters high, and 25 meters wide. It is a 7,000 tonne detector sitting in acavern 100 meters below ground, closest to the main CERN site.There are four main different layers of transversethe detector that makes upATLAS: the inner detector, the electromagnetic calorimeter, the hadroniccalorimeter, and the muon detector.The inner detector records or “tracks” the particles’ trajectories of the emerg-ing particles which allows physicists to reconstruct the interactions fromwhich they originate.It is comprised of three different instruments: a pixeldetector and a semiconductor tracker made up of silicon strips which detectsthe particles’ momentum and position, and a transition radiation tracker ofgold-plated tungsten wire inside thin tubes which measures photons createdwhen the particles pass through these “straws.”[2]Adjacent to the Inner detector is first and second the electromagnetic andthe hadronic calorimeter respectively.These calorimeters measure the ener-gies of the particles including photons, electrons, protons, and neutrons; somestrongly interacting particles may pass straight to the hadronic calorimeter.Specifically, ATLAS calorimeters are the Liquid Argon Calorimeter and theTile Calorimeter.The LAC uses liquid argon to transform a particle’s inci-dent energy into a shower of particles detected by sensors while the TC usessteel plates to produce particle showers and scintillating plastic to transformparticle’s energies into light that is later digitalized.[2] The latter is a crucialstep which identifies specific particles and at times the mass of the particlewhich decayed into creating them.The outer layer is the muon spectrometer which identifies and measuresmuons momenta.The muon spectrometer measures the paths of these muonsin the magnetic fields with high precision.Generally muons come from theweak interactions or something “brand new,” which plays an important rolein such as the detection of the Higgs.[4]

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Figure 3: The four layers of the detector (credits: atlas.ch)

Figure 4: “Periodic Table” of Particles (The Standard Model) [3]

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The Standard Model of Particle Physics

Leptons, quarks, and baryons (three quarks) make up matter.There aretwelve fermion “flavors” in three generations: six leptons with neutral chargefor the three neutrinos and charge -1 for the three remaining leptons, and sixquarks with a fractional charge of either −1

3or +2

3and with color (red, green,

and blue).Bosons, which are the photons, gluons, W, and Z, carry the forces betweenthe fermions that make up matter.The electromagnetism force is mediatedby the photon and has an infinite range.The W, charged, and Z, neutral,bosons are the carriers of the weak force.Gluons, massless and with a shortrange, are the carriers of the strong force which involve the color-carryingparticles (quarks).Respectively, each of these forces are 1036, 1025, and 1038the strength of gravity.[3]

1 Introduction

1.1 Pileup in ATLAS

Before proceeding to the next introductory subsection, there are importantvariables and observables one must understand in order to comprehend theresults of the following project’s Figures. The meaning of the following vari-ables will become clearer as the report progresses and additional parametersare explained.An important note to remember: Cells make up clusters which make up jets.

Luminosity: A measure of the intensity of the beam; the density of protonsin the interaction region where the beams overlap [cm−2s−1].

Pileup: Multiple interactions which occur simultaneously within the samebunch. These interactions describe the events coming from additional proton-proton interactions, rather than from additional interactions originating fromthe same hard-scatter proton collision.The probability of producing more than one full-on hard scattering event(proton-proton collision) per bunch crossing is low, however, as the instan-taneous luminosity per bunch crossing increases, the probability of “soft”interaction between the constituent quarks and gluons of additional hard

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scattering events increases.[8]

µ: The number of interactions per bunch crossing; a measure of in-timepileup.

< µ >: The mean number of interactions per bunch crossing - over a lumi-nosity block (e.g. 25ns).

σpileupnoise (< µ >) “threshold”: The energy (noise) in a cell (or channel - seeFigure 5) from pileup vertices. Vertices are where the interactions occur.Higher values of pileup noise prevent cells with lower energy from being in-volved in the clustering.

σpileupnoise (< µ >= µ): σ is optimal for this pileup number.

Figure 5: Graph to pictorially describe “threshold” (credits: [9])

1.2 The Impact of Mitigation of Pile-up on Large-RJets

Jets are defined as a spray of particles, of energy, or both. Specifically, theyhave been historically used as agents of the quarks and gluons that are pro-duced in the primary collisions at high energy colliders. High-luminosityconditions at the LHC lead to having unrelated soft particles contaminatingthe jets in the detectors, making it more difficult to resolve the particlesoriginating from the hard interaction. Boosted objects, when particles are

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produced with significant transverse momentum, pT ≥ mT , leading to lo-calized decay products in a small angular region of a detector, are speciallyaffected.Where decays are fully contained within individual large-radius jets,these high-luminosity conditions lead to a diminished mass resolution weak-ening the sensitivity to new physics processes.A potential new physics process is the decay of a new heavy gauge boson,the Z’→ tt̄. In this case the boosted objects are boosted tops, which involvethe heaviest quark of the standard model of particle physics with an approx-imate mass of 172 GeV. In order to remove contributions to a given jet thatweakens the sensitivity to new physics processes, many jet algorithms havebeen designed. In ATLAS, there are three specific jet algorithm modifica-tions, also known as jet “grooming” that are studied: mass-drop filtering,trimming, and pruning. The trimming algorithm is used for this project’sobservations.[6]

1.3 Performance of Grooming in the Context of Pileup[7]

There are three jet reconstruction algorithms used by ATLAS. The kt algo-rithm which clusters jets with smallest pT first, the Cambridge-Aachen(C/A)algorithm which combines jets based on their angular separation, and theanti−kt algorithm which clusters jets with largest pT first. The latter algo-rithm’s jets tends to be more circular in (η, φ) space & more robust thankt jets in high multiplicity environments. ATLAS utilizes anti−kt jets asstandard in physics analysis.[6]

1.4 The Trimming Algorithm

Contamination from pile-up, multiple parton interactions (MPI) - part of theunderlying event (UE) - and initial-state radiation (ISR) in the reconstructedjet is often softer than the outgoing partons related to the hard-scatter andtheir final-state radiation (FSR). The trimming algorithm takes advantageof this fact by completely removing the softer components from the final jet.Using a kt algorithm, the trimming procedure begins with an anti−kt R=1.0jet to create subjets of size Rsub. It then removes any subjets with pT i/p

jetT <

fcut, where pT i is the transverse momentum of the ith subjet, and fcut is aparameter of the method. The remaining components form the trimmed jet,e.g fcut = 0.05 and Rsub = 0.3 has been used by ATLAS as a standard andis one of the parameters used in this project.[6]

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Figure 6: The jet trimming procedure (credits: [6])

1.5 Jet Substructure Variables

Jet mass: The jet mass is essential for the search of boosted, high-massparticles; it is a powerful discriminant between signal and background. Theequation to calculate it comes from the energies and momenta of its compo-nents.

(mjet)2 = (∑i

Ei)2 − (

∑i

pi)2, (1)

where Ei and pi are the energy and three-momentum of the ith jet con-stituent. At the detector level the jet mass is calculated by summing overthe topo-clusters or tracks used for jet reconstruction. At the particle level,the jet constituents are stable particles.[6]

nClusters: The number of clusters.nSubjets: The number of subjets.nRejected: The number of subjets rejected during the trimming process.

Splitting Scales√d12,√d23: The kt distance of the final step & second to

the last step, respectively, in combining two subjets into the final jet (given inEq. (2)). They can be used to distinguish heavy particle decays, tending to bereasonably symmetric, from asymmetric splittings in light quark or gluon jets.The expected value for a heavy particle two-body decay is approximately√d12 ≈ mjet

2, whereas jets from the parton shower of light quarks and gluons

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will tend to exhibit a steeply falling spectrum for both splitting scales.√dij = min(pT i, pTj)×∆Rij, (2)

where ∆Rij is the distance between the two subjets.[6]

N-subjettiness τ21, τ32: The variables τN are observables related to thesubject multiplicity and are calculated by clustering the constituents of thejet with the kt algorithm and exactly requiring N subjets to be found. There-fore, they describe how well jets can be described as containing N or fewer ktsubjets by assessing the degree to which constituents are localized near theaxes of these subjets:

τN =1

d0

∑k

pTk ×min(δR1k, δR2k, ..., δRNk), with d0 ≡∑k

pTk ×R (3)

where R is the jet radius parameter in the jet algorithm, pTk is the pT of theconstituent k, and δRik is the distance from the subjet i to constituent k.

τ21 = τ2τ1

and τ32 = τ3τ2

can be used to provide discrimination between jetsformed from the parton shower of light quarks or gluons and jets containingtwo hadronic decay products (e.g. from Z-bosons) or three hadronic decayproducts from boosted top quarks.

2 Delphes

DELPHES, a C++ based framework for fast simulation of a generic col-lider experiment, was introduced in this project as a means to prepare forand understand the upcoming ATLAS simulations utilizing mu-scan sam-ples. This simulation includes a tracking system, embedded into a magneticfield, calorimetry and a muon system, and possible very forward detectorsarranged along the beamline. The framework is interfaced to standard fileformats and outputs observable objects for analysis, like missing transverseenergy and collections of electrons or jets. The simulation of detector re-sponse takes into account the detector resolution, and usual reconstructionalgorithms, such as FASTJET.[10]

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2.1 Understanding Parameters

Emily’s Question (an example): Using a large-R jet with Rsub = 0.8,estimate the pT cut one should make (find X: pjett > X GeV) such that thethree decay products of the top quark are roughly contained within this largeR jet.HINT : For the potential new physics process, the true angular separationbetween the W and b decay products of a top quark in simulated Z’ → tt̄(mZ′ = 1.6 TeV) events and the separation between the light quarks of thesubsequent hadronically-decaying W is approximately

∆R2m

pT, (4)

where ∆R =√

(∆y)2 + (∆ρ)2 and pT and m are transverse momentum andmass of the decay particle, respectively.

Given: mass of top quark, m≈173 GeV and ∆R as Rsub = 0.8.Using equation (4), solving for pT cut, X = 432 GeV.

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2.2 Understanding Output of Data

Jet Mass [GeV]0 50 100 150 200 250 300 350

Ent

ries

20

40

60

80

100mu0

mu50

mu140

Delphes Simulation

> 800 GeVjet

TC/A Jets R = 0.8, p

Figure 7: DELPHES - Jet Mass Delphes Simulation (credits: [Luz/Emily])

Observations: What is expected of this figure is to see that as < µ >increases, Jet Mass increases as well, however this not precisely the case inFigure 7. Instead, an inverse relationship is shown, as < µ > increases, JetMass decreases. Nevertheless, one can also notice that as the top Jet Masscurve does decrease as < µ > increases, it also broadens, possibly taking intoaccount the “total increase” of mass expected.

3 ATLAS Simulations

The following ATLAS Simulations have been divided into four different cat-egories of results. For the first category, the focus is on the variables of thediagonal (see Figures 8 & 9), σpileupnoise (< µ >= µ) (as is for categories twoand three) for both the trimmed and no jet grooming results of the jet massand nClusters for the 50ns luminosity block (bunch spacing). For the second

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category, the focus on both the 50ns (0, 40, 60, 80) luminosity block and the25ns (0, 40, 80, 140) luminosity block. For the third category, the focus ison the two trimming parameters, fcut = 0.05 with Rsub = 0.3 and fcut = 0.03with Rsub = 0.2, for both the 25ns and 50ns luminosity blocks. Lastly, thefocus of the fourth category is on the variable of fixed < µ >= 140 with threedifferent thresholds (100, 140, 200) for the 25ns case. All categories exceptthe fourth display results using standard trimming, pfracT = 0.05, Rsub = 0.3.

Note: Although graphs for mostly all variables in all categories were pro-duced, only specific results are presented in this paper due to their highersignificance to the ones that were not selected for this paper.

Figure 8: Mu-scan samples for the 50ns luminosity block (credits: [11])

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Figure 9: Mu-scan samples for the 25ns luminosity block (credits: [11] )

3.1 No Jet Grooming vs Trimming

The first category displays the difference between no jet grooming Jet Massand nClusters (on the left) and jet grooming Jet Mass and nClusters (on theright) using the trimming algorithm.

3.1.1 Jet Mass

Observations: As one would expect, as < µ > increases, the jet massincreases due to pileup (what was expected in the Delphes simulation). Itis clear that on the left the hard-scattering event displays the approximatemass of the top quark, 173GeV, and that once it is trimmed, all < µ >samples display an approximate accordance with the hard scattering event.Correction: The η value for Figure 10 should be labeled |η| = 1.2 to matchFigure 11.

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Jet Mass [GeV]0 50 100 150 200 250 300 350 400 450

Nor

mal

ized

Ent

ries

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18>=30)µ(<σ>=0, µ<

>=40)µ(<σ>=40, µ<

>=60)µ(<σ>=60, µ<

>=80)µ(<σ>=80, µ<

Simulation InternalATLAS| < 0.8η R=1.0, |tanti-k

Ungroomed

< 750 GeVjet

T p≤500

=14 TeV, 50 nss

Figure 10: No Jet GroomingJet Mass

Jet Mass [GeV]0 50 100 150 200 250 300 350 400 450

Nor

mal

ized

Ent

ries

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2>=30)µ(<σ>=0, µ<

>=40)µ(<σ>=40, µ<

>=60)µ(<σ>=60, µ<

>=80)µ(<σ>=80, µ<


=0.3sub

=0.05, RfracT

Trimmed, P

<750 GeVjet

T p≤500

=14 TeV, 50 nss

Figure 11: Trimmed Jet Mass

3.1.2 nClusters

Observations: As one would expect, as < µ > increases, the number ofclusters increases because there are more interactions involved than just thehard-scattering event, which means there are more clusters involved. Oncethe jet is trimmed, the number of clusters of the different < µ > samplesdecreases to match the hard-scattering event because the specific trimmingparameter only allows a certain number of clusters to form the final jet.

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nClusters0 20 40 60 80 100 120 140

Nor

mal

ized

Ent

ries

0

0.02

0.04

0.06

0.08

0.1

0.12>=30)µ(<σ>=0, µ<

>=40)µ(<σ>=40, µ<

>=60)µ(<σ>=60, µ<

>=80)µ(<σ>=80, µ<


Ungroomed

<750 GeVjet

T p≤500

=14 TeV, 50 nss

Figure 12: No Jet GroomingnClusters

nClusters0 10 20 30 40 50 60 70

Nor

mal

ized

Ent

ries

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08>=30)µ(<σ>=0, µ<

>=40)µ(<σ>=40, µ<

>=60)µ(<σ>=60, µ<

>=80)µ(<σ>=80, µ<


=0.3sub

=0.05, Rfrac

TTrimmed, P

<750 GeVjet

T p≤500

=14 TeV, 50 nss

Figure 13: Trimmed nClusters

3.2 50ns vs 25ns

The second category displays the difference between the 50ns luminosityblock and the 25ns luminosity block for four variables: Jet Mass, nClusters,Subjets, and nRejected. The 25ns luminosity block is related to the nominal(optimal) beam of energy of the LHC, 14TeV total from both beams.

3.2.1 Jet Mass

Observations: As expected, once the jet is trimmed, for both the 50ns and25ns luminosity blocks, the Jet Mass has a peak around 173GeV, the massof the top quark.

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Jet Mass [GeV]0 50 100 150 200 250 300 350 400 450

Nor

mal

ized

Ent

ries

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2>=30)µ(<σ>=0, µ<

>=40)µ(<σ>=40, µ<

>=60)µ(<σ>=60, µ<

>=80)µ(<σ>=80, µ<


=0.3sub

=0.05, RfracT

Trimmed, P

<750 GeVjet

T p≤500

=14 TeV, 50 nss

Figure 14: Jet Mass, 50ns Figure 15: Jet Mass, 25ns

3.2.2 nClusters

Observations: From the left to the right Figure, 50ns to 25ns luminosityblock, one can observe a bigger gap in between < µ >= 0 and the remaining< µ > values. However, although not readily apparent, there are less clustersfor the 25ns luminosity block than there are for the 50ns luminosity block.

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nClusters0 10 20 30 40 50 60 70

Nor

mal

ized

Ent

ries

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08>=30)µ(<σ>=0, µ<

>=40)µ(<σ>=40, µ<

>=60)µ(<σ>=60, µ<

>=80)µ(<σ>=80, µ<


=0.3sub

=0.05, Rfrac

TTrimmed, P

<750 GeVjet

T p≤500

=14 TeV, 50 nss

Figure 16: nClusters, 50ns Figure 17: nClusters, 25ns

3.2.3 Subjets

Observations: There are about the same number of subjets in the 50nsbunch spacing, as there are in the 25ns bunch spacing.

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nSubjets0 1 2 3 4 5 6 7 8

Nor

mal

ized

Ent

ries

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7>=30)µ(<σ>=0, µ<

>=40)µ(<σ>=40, µ<

>=60)µ(<σ>=60, µ<

>=80)µ(<σ>=80, µ<


=0.3sub

=0.05, Rfrac

TTrimmed, P

<750 GeVjet

T p≤500

=14 TeV, 50 nss

Figure 18: nSubjets, 50ns Figure 19: nSubjets, 25ns

3.2.4 nRejected

Observations: There are about the same number of number of rejectedsubjects in the 50ns bunch spacing as there are in the 25ns bunch spacing,ultimately the same observation as for the number of subjets.

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nRejected0 2 4 6 8 10 12 14 16 18 20

Nor

mal

ized

Ent

ries

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35>=30)µ(<σ>=0, µ<

>=40)µ(<σ>=40, µ<

>=60)µ(<σ>=60, µ<

>=80)µ(<σ>=80, µ<


=0.3sub

=0.05, Rfrac

TTrimmed, P

<750 GeVjet

T p≤500

=14 TeV, 50 nss

Figure 20: nRejected, 50ns Figure 21: nRejected, 25ns

3.3 fcut = 0.05, Rsub = 0.3 vs fcut = 0.03, Rsub = 0.2

The third category displays the difference between the two trimming param-eters used in this project: fcut = 0.05 with Rsub = 0.3 and fcut = 0.03 withRsub = 0.2. The following observed variables are Jet Mass, nClusters, nSub-jets, and nRejected. Unlike the previous two categories where the reasonfor not displaying the results for the splitting scales and N-subjettiness wasbecause there was no difference between the two corresponding Figures beingcompared, the reason for not displaying these results here is because for thefinal plots of the project made, all splitting scales and N-Subjettiness sub-structures were only plotted using the standard trimming. Here the resultsare also only displayed for the 25ns luminosity block since the same patternsof results were observed for the 50ns luminosity block.

3.3.1 Jet Mass

Observations: As expected, for both Figures 22 & 23, the peak of the JetMass is around 173GeV, the mass of the top quark. The Figures comparedonly differ minimally.

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Figure 22: Jet Mass fcut = 0.05,Rsub=0.3

Figure 23: Jet Mass fcut=0.03,Rsub = 0.2

3.3.2 nClusters

Observations: As expected, as fcut and Rsub decrease (from the left tothe right figure), the number of clusters decreases. That is because thetrimming algorithm no longer “accepts” the same amount of clusters, rather,it discards them to match the “lower” trimming parameter being used. As aconsequence, this “lower” trimming parameter also seems to provide a bettergrooming for these nCluster results.

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Figure 24: nClusters fcut = 0.05,Rsub = 0.3 Figure 25: nClusters fcut = 0.03, Rsub=0.2

3.3.3 nSubjets

Observations: The observed results for the number of subjets are aboutthe same as those for the number of clusters. As the parameter is of smallerfcut and Rsub, the number of subjets decreases, although in a smaller ratiothan the number of clusters because of the amount of lesser total subjets.

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Figure 26: nSubjets fcut =0.05, Rsub = 0.3 Figure 27: nSubjets fcut = 0.03, Rsub=0.2

3.3.4 nRejected

Observations: As the trimming parameter “decreases” from left to right aspreviously mentioned, the number of subjets rejected increases. This is aninverse relationship compared to the that of the number of subjets; as thereare less subjets due to a “smaller” trimming parameter of fcut and Rsub, moresubjets were consequently rejected.

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Figure 28: nRejected fcut = 0.05,Rsub = 0.3

Figure 29: nRejected fcut = 0.03,Rsub=0.2

3.4 Optimal Threshold Values vs Varying ThresholdValues

The fourth category displays the difference between the fixed variable < µ >with three different thresholds (100, 140, 200) with the previous categories’σpileupnoise (< µ >= µ) for the 25ns luminosity block. Six variables are observed:nClusters for both no jet grooming and trimmed jet, nRejected,

√d12,√d23,

τ21, and τ32. The last four substructure variables are observed for no jetgrooming.

3.4.1 nClusters: No Jet Grooming

Observations: As the threshold values increase, the number of clustersdecreases. This observation is expected from the definition of “threshold.”The clusters that comprise the jet are made up of those cells whose energylevel corresponds to the specified thereshold. As the threshold increases,there are less cells who have that much higher energy (noise) and thereforethe number of clusters decreases.

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Figure 30: No jet grooming nClusters,σpileupnoise (< µ >= µ)

nClusters0 20 40 60 80 100 120 140

Nor

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ized

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ries

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0.02

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0.12

>=100)µ(<σ>=140,µ<

>=140)µ(<σ>=140,µ<

>=200)µ(<σ>=140,µ<


Ungroomed

<750 GeVjet

T p≤500

=14 TeV, 25 nss

Figure 31: No jet grooming nClusters,fixed < µ >

3.4.2 nClusters: Trimmed - Standard Parameter

Observations: The same observations for Figures 30 and 31 can be seen forFigures 32 and 33. In addition, one can compare the differences between Fig-ures 30 and 31 with the differences between Figures 30 and 32. By comparingthese two sets of Figures one can see that increasing the threshold provides aminimal “grooming” to the jet, while seeing the full effect of grooming whenthe standard trimming is applied. Furthermore, by comparing the previoussets of Figures to Figures 32 and 33, one can observe that increasing thethreshold and applying the standard trimming parameter, results in the op-timal result of the four, more closely matching the plot of the hard-scatteringevent.

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Figure 32: Trimmed nClusters,σpileupnoise (< µ >= µ)

nClusters0 10 20 30 40 50 60 70

Nor

mal

ized

Ent

ries

0

0.01

0.02

0.03

0.04

0.05

0.06>=100)µ(<σ>=140,µ<

>=140)µ(<σ>=140,µ<

>=200)µ(<σ>=140,µ<


=0.3sub

=0.05, Rfrac

TTrimmed, P

<750 GeVjet

T p≤500

=14 TeV, 25 nss

Figure 33: Trimmed nClusters, fixed< µ >

3.4.3 nRejected: Trimmed- Standard Parameter

Observations: It is interesting to see that as the threshold increases, thenumber of rejected subjets also decreases, alike the number of clusters. Thisis due to the same reason why the number of clusters decreases: as thereare less cells that match the specified increased threshold level, there are lessclusters formed and consequently less clusters (and therefore jets) rejected.

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Figure 34: nRejected, σpileupnoise (< µ >=µ)

nRejected0 2 4 6 8 10 12 14 16 18 20

Nor

mal

ized

Ent

ries

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35>=100)µ(<σ>=140,µ<

>=140)µ(<σ>=140,µ<

>=200)µ(<σ>=140,µ<


=0.3sub

=0.05, Rfrac

TTrimmed, P

<750 GeVjet

T p≤500

=14 TeV, 25 nss

Figure 35: nRejected, fixed < µ >

Observations of Figures 36 to 43: For the following four substruc-tures, splitting scales and N-subjettiness, a pattern of similar observationsemerge. All of the four substructure variables provide a comparison betweenno jet grooming from (left) optimal threshold for its corresponding < µ >to (right) fixed < µ > and varying threshold. For these variables, Figures36 to 43, one can observe that increasing the threshold provides a minimal,jet efficient“trimming” as observed on particular previous nClusters sets ofFigures.

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3.4.4√d12: No Jet Grooming

Figure 36:√d12, σ

pileupnoise (< µ >= µ)

[GeV]12d0 50 100 150 200 250 300

Nor

mal

ized

Ent

ries

0

0.01

0.02

0.03

0.04

0.05

0.06>=100)µ(<σ>=140,µ<

>=140)µ(<σ>=140,µ<

>=200)µ(<σ>=140,µ<


Ungroomed

< 750 GeVjet

T p≤500

=14 TeV, 25 nss

Figure 37:√d12, fixed < µ >

3.4.5√d23: No Jet Grooming

Figure 38:√d23, σ

pileupnoise (< µ >= µ)

[GeV]23d0 20 40 60 80 100 120 140

Nor

mal

ized

Ent

ries

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

>=100)µ(<σ>=140,µ<

>=140)µ(<σ>=140,µ<

>=200)µ(<σ>=140,µ<


Ungroomed

< 750 GeVjet

T p≤500

=14 TeV, 25 nss

Figure 39:√d23, fixed < µ >

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3.4.6 N-subjettiness τ21: No Jet Grooming

Figure 40: τ21, σpileupnoise (< µ >= µ)

21τN-subjettiness 0 0.2 0.4 0.6 0.8 1 1.2

Nor

mal

ized

Ent

ries

0

0.02

0.04

0.06

0.08

0.1

0.12 >=100)µ(<σ>=140,µ<

>=140)µ(<σ>=140,µ<

>=200)µ(<σ>=140,µ<


Ungroomed

< 750 GeVjet

T p≤500

=14 TeV, 25 nss

Figure 41: τ21, fixed < µ >

3.4.7 N-subjettiness τ32: No Jet Grooming

Figure 42: τ32, σpileupnoise (< µ >= µ)

32τN-subjettiness 0 0.2 0.4 0.6 0.8 1 1.2

Nor

mal

ized

Ent

ries

0

0.02

0.04

0.06

0.08

0.1

0.12 >=100)µ(<σ>=140,µ<

>=140)µ(<σ>=140,µ<

>=200)µ(<σ>=140,µ<


Ungroomed

< 750 GeVjet

T p≤500

=14 TeV, 25 nss

Figure 43: τ32, fixed < µ >

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4 Jet Mass: Not Jet Grooming & Trimmed

4.1 ATLAS Simulation for Approval

All of the figures presented in this paper were also presented during HighLuminosity Meetings at CERN which took place during my nine-week in-ternship. However, the one jet property which was most closely observedwas the Jet Mass. Two of the Figures in this report were chosen for poten-tial public results and are shown as “updated” in Figures 44 and 45. Theseare results based on the diagonal mu-scan samples, σpileupnoise (< µ >= µ) for< µ >=0, 40, 80, & 140.

The Trimmed Jet Mass, Figure 45, was shown on Monday, July 29, 2013, bythe deputy physics spokesperson in ATLAS as part of a Snowmass Practiceconference under the title “Future Searches for New Physics at the LHC withATLAS and CMS”:https://indico.cern.ch/conferenceDisplay.py?confId=265359

[GeV]jetm0 100 200 300 400 500

Nor

mal

ized

Ent

ries

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0.22

>=30)µ(<noise

pileupσ>=0, µ<

>=40)µ(<noise

pileupσ>=40, µ<

>=80)µ(<noise

pileupσ>=80, µ<

>=140)µ(<noise

pileupσ>=140,µ<

Simulation For ApprovalATLAS| < 1.2η LCW jets with R=1.0, 0 < |tanti-k

No jet grooming

=14 TeV, 25 ns bunch spacings

< 750 GeVjet

T500 < p

= 2 TeV)Z'

(mt t→Pythia8 Z'

Figure 44: No Jet Grooming Jet Mass [GeV] (credits: [Luz/Emily])

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[GeV]jetm0 100 200 300 400 500

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mal

ized

Ent

ries

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0.2

0.22

>=30)µ(<noise

pileupσ>=0, µ<

>=40)µ(<noise

pileupσ>=40, µ<

>=80)µ(<noise

pileupσ>=80, µ<

>=140)µ(<noise

pileupσ>=140,µ<

Simulation For ApprovalATLAS| < 1.2η LCW jets with R=1.0, 0 < |tanti-k

Trimmed

=14 TeV, 25 ns bunch spacings

< 750 GeVjet

T500 < p

= 2 TeV)Z'

(mt t→Pythia8 Z'

Figure 45: Trimmed Jet Mass [GeV](credits: [Luz/Emily])

5 Conclusion

Four categories of results were presented in this report. The first one fo-cused on how the trimming algorithm gets rid of pileup, demonstrating theefficient before and after effect of trimming. In the second category, the fo-cus was on comparing the 50ns luminosity block with the 25ns luminosityblock and observing which differences arose; there were no apparent differ-ences between the two luminosity blocks. The third category displayed thedifferences between two trimming parameters: fcut = 0.05, Rsub = 0.3 vsfcut = 0.03, Rsub = 0.2. As one “decreases” the trimming parameter, thenumber of clusters and number of subjets decrease while the number of re-jected subjets increases; the number of subjets are inversely proportional tothe number of rejected subjets. Specifically from the Figures 24 and 25,one could make a conclusion that the trimming parameter fcut = 0.03 withRsub = 0.2 might provide a better grooming than the standard trimmingfcut = 0.05, Rsub = 0.3.

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Lastly, the last category involved displaying the difference between the fixedvariable < µ > with three different thresholds and the optimal thresholdfor the specified < µ >, σpileupnoise (< µ >= µ). Here the conclusion is thefollowing. As the threshold is increased, the nClusters, nSubjets, and nRe-jected decrease. Increasing the threshold provides a minimal “trimming” ascompared to the standard trimming algorithm; increased threshold removesunwanted cells while trimming removes unwanted clusters.The Jet Mass results are bound to be public for very important reasons. TheLHC will be turned on again in 2015 with its nominal energy of 7TeV perbeam. This implies potential new physics as the one shown on these JetMass results; the decay of a new heavy gauge boson, the Z’ → tt̄.

6 Acknowledgements at CERN

I would like to thank my advisor, Emily Thompson for all the help, guid-ance, and patience she gave me during the course of this nine-week summerinternship at CERN. And in part to Lily Asquith; both of these women areinspiring, outstanding physicists. To professor Gustaaf Brooijmans for hisextended lecture on the standard model and detectors in particle physicsduring the first week of the internship. To Diedi and Jun for lending me ahand anytime that I needed it.A special thank you to professor John Parsons for the opportunity to learnfrom top world-physicists, those in our Columbia group included, at CERN.And to the National Science Foundation, for providing undergraduates theseimmense opportunities to experience hands-on physics research and motivat-ing us to keep broadening our knowledge and passion for higher education.

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7 Recommendations for Upcoming Summer

REU Students

A few tips to come better prepared to work at CERN (programming-wise):

Learn UNIX: http://www.ee.surrey.ac.uk/Teaching/Unix/index.html

Learn as much as C++ as possible: http://www.cplusplus.com/doc/tutorial/

Learn to “love” ROOT & how to make histograms: http://root.cern.ch/drupal/

Learn and get comfortable with a text editor such as Emacs (recommended):http://www.gnu.org/software/emacs/or Sublime (“cool-looking”): http://www.sublimetext.com

Learn Latex and Beamer for your presentations and future mathematics/scientificwork: http://www.latex-project.org and http://www.math.umbc.edu/ rouben/beamer/

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8 Bibliography

References

[1] http://home.web.cern.ch

[2] http://www.uslhc.us

[3] Cal Poly-1 Particle Physics Emily.pdf

[4] Sean Carroll The Particle at the End of the Universe

[5] atlas.ch

[6] ATLAS-CONF-2012-065

[7] ATLAS-CONF-2012-066

[8] http : //atlas − service − enews.web.cern.ch/atlas − service −enews/2010/news10/newsP ile− up.php

[9] “From Raw Physics to Data Results” Jamie Boyd- CERN Summer Stu-dent Lectures 2013

[10] https://cds.cern.ch/record/1166715

[11] https://twiki.cern.ch/twiki/bin/viewauth/AtlasProtected/JetEtmissMC12

[12] Previous REU student’s talks and reports for ideas on presentation:http://www.nevis.columbia.edu/reu/

[13] Side note for some figures turning more defined than others: There wereseveral updates for the two final jet mass plots which affected the outputof the other variables for the 25ns luminosity block. Therefore, I only hadthe chance of taking screen-shots of the copies I had saved in a big pdffile, as opposed to attaching the individual pdf file plot on this paper.

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