About meGabriel Gallardo

The University of Hong Kong — BSc, Sep 2012 - Jun 2016

First Class HonorsMajor: PhysicsMinor: Computer Science

Member of the HK Cluster at ATLAS since June 2015

Attended CERN Summer School 2015Collaborating with University of Michigan on search for electroweakSUSY in same-sign dilepton channelsMeasure electron charge misidentification

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Contents

Current analysis

Electron charge misidentificationWhat is charge misID?Tag-and-probe methodLikelihood methodClosure tests in MCpT correction for charge flipped electronsSystematic uncertainties

Public speaking

MiscellaneousHonors and fellowshipsSkills

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Current analysis

χ±1

χ02

˜/ν˜/ν

p

p

ν/`

`/ν

χ01

`/ν

`/ν

χ01

χ±1

χ02

W

Zp

p

χ01

`

ν

χ01

`

`

Signal: two same-sign lepton with missing transverse momentumI Missing transverse momentum from χ0

1 and νI Two leptons: for the case where one of the three leptons cannot be

identified because it is very softI Same sign two leptons: for smaller Standard Model backgroundI Potentially more sensitive for small mass splitting

(χ±1 [χ0

2]− χ01 . 50GeV)

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Backgrounds to SS2L channel

Preliminary estimate of background inthe same-sign two electron channel. March 2016

By Dongliang Zhang (UMichigan)

OS electron pairs with one charge misID electron is a majorbackground

Need to have a reliable estimate of this contribution

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What is charge misID?

Bremsstrahlung Track mis-reconstruction

1. Electrons interact with detectormaterial to produce bremsstrahlungphoton

2. The photon undergoes pairproduction

3. The conversion electron ismistakenly identified as the primaryelectron

Direction of curvaturedifficult to determine fornear-straight tracks ofpT > 150 GeV electrons

Figures from Giulia Gonella, Albert-Ludwigs-Universitat Freiburg, ATLAS5 / 16

Tag-and-probe method

Initially tried the tag-and-probe method from Run 1 1

On Z → e−e+ eventsI Must produce opposite sign pairs!

Select dielectron events where the invariant mass is within the Z-masswindow

Impose tight conditions on a tag electron to minimize the probabilityof its charge being misidentified

Assuming the charge of the tag electron is correct, we look at thecharge of the probe electron to determine the rate of charge misID:

εT&P =Number of same-sign probes

Total number of probes

1The ATLAS Collaboration. “Electron reconstruction and identification efficiency measurements with the ATLAS detector

using the 2011 LHC proton-proton collision data.” In: The European physical journal. C, Particles and fields 74.7 (Jan. 2014),p. 2941

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Tag-and-probe results

hEtaMediumErrorEntries 2150Mean 1.797RMS 0.4702

|η|0 0.5 1 1.5 2 2.5

0.005

0.01

0.015

0.02

0.025

hEtaMediumErrorEntries 2150Mean 1.797RMS 0.4702

(on medium cut)ηCharge misidentification as a function

—— From tag-and-probe—— MC Truth

September 2015, on a Z → ee MC sample7 / 16

Likelihood method

Switched to likelihood method to avoid the bias introduced by assumingthe tag electron has correct charge

Z → e+e− events used: each e falls into a binI p = P(e1correct)P(e2wrong) + P(e2correct)P(e1wrong)I p = (1− εi )εj + (1− εj )εi

I P(bothwrong) term ignored because ε ∼ 10−3 � ε2 ∼ 10−6

NexpSS = np

Binomial distribution of seeing nss same-sign eventsCn

nsspnss(1− p)n−nss

As n is large, p is small, we can approximate by Poisson distribution:

L(εi , εj ) ≡ P(nss|εi , εj ) =(Nexp

SS )nss e−N

expSS

nss!

For likelihood across all bins:L =

∏i ,j L(εi , εj )

To find ε: minimize −ln(L)

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MisID rates from likelihood method

|η|0 0.5 1 1.5 2 2.5

mis

ID r

ate

4−10

3−10

2−10

1−10

< 30 (GeV)T

p≤20

Data

MC LH

MC truth

|η|0 0.5 1 1.5 2 2.5

mis

ID r

ate

4−10

3−10

2−10

1−10

< 40 (GeV)T

p≤30

Data

MC LH

MC truth

|η|0 0.5 1 1.5 2 2.5

mis

ID r

ate

4−10

3−10

2−10

1−10

< 50 (GeV)T

p≤40

Data

MC LH

MC truth

|η|0 0.5 1 1.5 2 2.5

mis

ID r

ate

4−10

3−10

2−10

1−10

< 60 (GeV)T

p≤50

Data

MC LH

MC truth

|η|0 0.5 1 1.5 2 2.5

mis

ID r

ate

4−10

3−10

2−10

1−10

< 80 (GeV)T

p≤60

Data

MC LH

MC truth

|η|0 0.5 1 1.5 2 2.5

mis

ID r

ate

4−10

3−10

2−10

1−10

< 120 (GeV)T

p≤80

Data

MC LH

MC truth

April 2016, on Z → ee MC sample and 3.2/fb of 2015 ATLAS data9 / 16

Closure tests in MC

Get number of opposite sign (OS) events and weight them with ascale factor dependent on chargeMisId rate to obtain a prediction ofsame sign (SS) events.

pss =εi (1− εj ) + εj (1− εi )

w =pss

1− pss

Plot the prediction against the number of observed SS events.

(GeV)llm20 40 60 80 100 120 140 160 180 200 2201

10

210

310

410

Observed invariant mass distribution of same sign electron pairs

Observed SS events

Predicted SS events from OS

Observed invariant mass distribution of same sign electron pairs

Exp

/Obs

0.60.8

11.21.41.61.8

2

(GeV)T

p20 40 60 80 100 120 140 160 180 200 2201

10

210

310

410

Observed leading pt distribution of same sign electron pairs

Observed SS events

Predicted SS events from OS

Observed leading pt distribution of same sign electron pairs

Exp

/Obs

0.60.8

11.21.41.61.8

2

η2− 1.5− 1− 0.5− 0 0.5 1 1.5 2

310

410

Observed eta distribution of same sign electron pairs

Observed SS events

Predicted SS events from OS

Observed eta distribution of same sign electron pairs

Exp

/Obs

0.60.8

11.21.41.61.8

2

June 2016, on a Z → ee MC sample10 / 16

pT correction for charge flipped electrons

Since electrons lose energy in bremsstrahlung, the reconstructedelectrons with misidentified charge have lower energy than those withcorrect charge

Account for this in prediction by comparing the reconstructed pT andthe pT of the original electron from Z in truth

∆pT = reconstructed pT − pT of original electron from Z

pcorrectedT = preco

T + ∆pflippedT −∆pok

T

(GeV)llm20 40 60 80 100 120 140 160 180 200 220

410

510

610

710

810

Observed invariant mass distribution of same sign electron pairs

Observed SS events

Predicted SS events from OS

Observed invariant mass distribution of same sign electron pairs

Exp

/Obs

0.60.8

11.21.41.61.8

2

(GeV)T

p20 40 60 80 100 120 140 160 180 200 220

410

510

610

710

Observed leading pt distribution of same sign electron pairs

Observed SS events

Predicted SS events from OS

Observed leading pt distribution of same sign electron pairs

Exp

/Obs

0.60.8

11.21.41.61.8

2

η2− 1.5− 1− 0.5− 0 0.5 1 1.5 2

610

710

Observed eta distribution of same sign electron pairs

Observed SS events

Predicted SS events from OS

Observed eta distribution of same sign electron pairs

Exp

/Obs

0.60.8

11.21.41.61.8

2

July 2016, on a Z → ee MC sample11 / 16

Systematic uncertainties

Method bias Variation of Z-masswindow

Rates withuncertainties

|η|0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4

mis

ID r

ate

4−10

3−10

2−10

1−10

< 60 (GeV)T

p≤50

MC LH

MC truth

LH/tr

uth

0.6

0.81

1.21.4

1.61.8

2

η0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4

4−10

3−10

2−10

< 60T

50 < pNom: 80<m<100,SB=2080<m<100,SB=080<m<100,SB=2580<m<100,SB=1575<m<100,SB=2075<m<105,SB=20

η0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2 2.2 2.4

4−10

3−10

2−10

< 60T

50 < p sys⊕Stat

systematics⊕VariationMethod bias

Difference in ratesobtained likelihood

method and MC truth

Difference due toselection criteria

All uncertaintiessummed in quadrature

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Public speakingAt HKU, I’ve given public presentations on:

My work at ATLAS, for which I won“Best Presenter of 2014-2015” out of80 presenters at the poster presentationand research colloquium of the Facultyof Science, HKU (October 2015)

“Why study physics?”, to prospectiveundergraduates

My work at ATLAS, as a postersubmitted at the Physical Society ofHong Kong conference (June 2016)

My talk at the October 2015Research Colloquium of theFaculty of Science, HKU

At study group meetings of the HKU ATLAS team, I’ve given talks on:

The maximum likelihood method

Artificial neural networks

Electron reconstruction

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Public speaking

Externally, I’ve spoken about:

Supersymmetry, in the heats for FameLab2

HK 2016 (video here)

Proton therapy, at the FameLab HK 2016final (video here)

My journey in studying physics, to highschool students

My experience doing research as aundergraduate, to the press whenpromoting HKU’s new “Young ScientistScheme”

Life at CERN, to visiting high schoolstudents from Hong Kong

Sing Tao Daily | Circulation / Reach: 100,000 | 2016-01-21

Newspaper | F02 | 星島教育

Keyword Matched: 港大,香港大學,理學院

港大培育科研人才 配對導師個別指導 文憑試達31分 獲二萬元獎學金

Word Count: 805words | Image No: 1/1 | Image Size: 442cm-sq(25cm x 17.7cm) | Ad-Value: HKD62,087

Source: Wisers electronic service. This content, the trademarks and logos belong to Wisers, the relevant organizations or copyright owners.All rights reserved. Any content provided by user is the responsibility of the user and Wisers is not responsible for such content, copyrightclearance or any damage/loss suffered as a result.Print out of Wisers electronic service.

My talk at the FameLab final (top). SingTao Daily’s report on HKU’s Young ScientistScheme, featuring my experience at CERN(bottom).

2FameLab is a public speaking competition organized by the British Council wherecontestants attempt to explain a scientific concept to the general public in 3 minutes.

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Honors and fellowships

In support of my work at ATLAS, I have been given funding under:

Overseas Research Fellowship, Faculty of Science, HKU

Overseas Research Program, Department of Physics, HKU

Additionally, I have been awarded:

BSc, First Class Honors HKU June 2016Dean’s Honor List HKU 2012-2013Dean’s Honor List HKU 2013-2014Dean’s Honor List UC Irvine Fall quarter 2014Li Po Kwai Scholarships HKU 2013-2014Li Po Kwai Scholarships HKU 2014-2015HKUWW Scholarships HKU 2014-2015Reaching Out Award HKSAR Government 2014-2015

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Skills

Programming in:I C++I JavaI PythonI ROOTI bash

Other computer-related skills:I LATEXI HTMLI MathematicaI Git, SVNI Microsoft Office

Operating systems:I Mac OS XI Linux

Languages:I Native fluency in EnglishI Working proficiency in written

Chinese and CantoneseI Conversational fluency in

Putonghua

Public speakingI My latest recorded talk:

youtu.be/mTpwQCaYqNU

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