SEARCH FOR LONG-LIVED PARTICLES IN THE ATLAS HADRONIC CALORIMETER IN ASSOCIATION WITH LEPTONS OR JETS

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SEARCH FOR LONG-LIVED PARTICLES IN THE ATLAS HADRONIC CALORIMETER IN ASSOCIATION WITH

LEPTONS OR JETS

Victoria Sánchez Sebastián

on behalf of the CalRatio analysis team

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INTRODUCTION

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CalRatio+X EB request - date V. Sánchez Sebastián

We are performing a search for neutral LLPs decaying hadronically in the calorimeter produced in association with leptons or jets, three channels:

➤ Displaced jet (DJ) + 2 jets

➤ DJ + leptons:

• DJ + W ( or )

• DJ + Z ( or )

All these channels use the NN displaced jet tagger from the two CalRatio jets analysis to identify LLPs [ref]

Signature-driven search using several benchmark models Main backgrounds: SM multijets, non-collision

backgrounds, W/Z+jets, single and pair production of top quarks

→ eν μν

→ ee μμ

ggF: Φ( → SS)

LLP

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INTRODUCTION

2

ggF: Φ( → SS)

DJ+2jets

➤ DJ + leptons:

• DJ + W ( or )

• DJ + Z ( or )

Signature-driven search using several benchmark models Main backgrounds: SM multijets, non-collision

backgrounds, W/Z+jets, single and pair production of top quarks

→ eν μν

→ ee μμ

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INTRODUCTION

2

DJ+leptons

➤ DJ + leptons:

• DJ + W ( or )

• DJ + Z ( or )

Signature-driven search using several benchmark models Main backgrounds: SM multijets, non-collision

backgrounds, W/Z+jets, single and pair production of top quarks

→ eν μν

→ ee μμ

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We are performing

INTRODUCTION

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DISPLACED JET + LEPTONS CHANNELS

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Signature is at least one displaced jet in the Calorimeter + prompt Z/W boson, benefits from:

➤ Single displaced object search

➤ Use SM lepton triggers: high eﬃciency, no need of specific trigger

➤ Use same framework and tools (e.g. previous analysis’ per-jet NN) developed for two CalRatio jets analysis

Main backgrounds are:

➤ DJ+Z: Z+jets

➤ DJ+W: Z+jets, W+jets, ttbar and Wt

ANALYSIS OVERVIEW

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Lepton triggers

DJ+W: single lepton DJ+Z: single OR di-lepton

BDT

Separates signal & bkg Multiple trainings

depending on boost and kinematics

Preselection

&

Event Cleaning

Final selection

Signal

significance

&

limit-setting

ABCD method

(data-driven bkg estimation)

key: Filtering, Calculation

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LEPTON TRIGGERS

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Lowest unprescaled single and dilepton triggers for each year used: only triggers that were fully active and

unprescaled during the full year are considered (full list in backup). In MC16a, diﬀerent trigger requirement in 2015 and 2016 accounted for using “RandomRunNumber” obtained after PRW.

➤ DJ+W: single lepton triggers

➤ DJ+Z: single OR di lepton triggers

Lepton triggers OR CalRatio triggers increase ~1% signal eﬃciency, decided not to use them

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Preselection for DJ+leptons channels:

➤ Lepton triggers

➤ Z or W reconstruction (see box on the right): orthogonal selections + common preselection:

➤ At least one trackless (min ) un-calibrated jet in the event with GeV: condition required to store events that fire lepton triggers in EXOT15

➤ Event > 0.5 (calculated over clean jets)

➤ DJ preselection: clean jet with highest low-ET NN signal score ( ), require

• Trackless: min , and GeV

• Low-ET NN signal score > 0.4

• Jet cleaning cuts: logRatio > -1, -3 ns < time < 15 ns, not in Tile Calorimeter gap region

ΔR(jet, tracks) > 0.2 p_T > 40

∑ ΔR_min(^jet,tracks)

j^sig^1,^l ΔR(jet, tracks) > 0.2 p_T > 50

j^sig^1,^l j^sig^1,^l

PRESELECTION AND EVENT CLEANING

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DJ + W: reconstruction:

• Exactly one lepton

• > 50 GeV

• lepton GeV

• GeV

DJ + Z: reconstruction:

• 2 OSSF leptons

• >60 GeV

• Leading lepton > 25 GeV

• Subleading lepton > 10 GeV

W → lν M_T(^lepton,MET)

p_T > 27 E_T^miss > 30

Z → l⁺l⁻

M(l⁺, l⁻)

p_T

placeholder of two

preselection plots

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A set of boosted decision trees are trained using XGBoost after preselection:

➤ Separate signal from background

➤ One of the two axis of ABCD plane Similar architecture and development:

➤ Input variables: combination of DJ, lepton and event level variables

➤ Training uses MC for signal and background:

• Odd-numbered events for signal, background all events included

• Signal and background input samples are randomly split into train (80%) and validation (20%) samples

• DJ+W: two trainings, signal W+ALP / low mass W+ , against combination of Z+jets, W+jets, Wt and (+ high mass selection, work-in-progress)

• DJ+Z: one training, Z+ and high mass combination, against Z+jets (+ 2 low mass selections, work-in-progress)

Φ → SS t t ¯

Φ → SS Φ → ZZ

_d

PER EVENT BDT

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Two trainings according to event topology and kinematics:

➤ W+ALP: all signal mass points

➤ Low mass W+ , GeV

Same input variables: diﬀerences due to particle boosts and model topology (full list in backup)

Φ( → SS ) m

_Φ

≤ 200

BDT: DJ+W CHANNEL

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blinded blinded

Example input variables BDT score distributions

Very similar performance test and training samples: no overfitting

Very different feature importance between two trainings due to different

behavior

…

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BDT: DJ+Z CHANNEL

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High-ET training performed, includes combination of signal samples:

➤ Z+ ( GeV)

➤ ( GeV)

Φ → SS m

_Φ

≥ 400 Φ → ZZ

_d

m

_Φ

≥ 250

Mariia to provide a

few plots

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FINAL SELECTION OPTIMIZATION

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Test additional cuts in ABCD plane to increase in

reg. A and reduce signal contamination in B, C & D

S/ B

Find optimal region A boundaries

Background in A taken

from ABCD method Control stats

error in A Maximize S/ B Control signal leakage

Example variations for W+HSS selection

Final selection marked in bold

Optimization performed using full Run 2 data

➤ For each selection, define ABCD plane with: BDT score vs , and test multiple cuts to define region A

➤ Calculate expected number of events in region A using ABCD method estimation from B, C & D

➤ Keep selections with: low stat error in A (<50%), more than 50% of signal in region A, and maximize

S/ B

blinded

∑jets

ΔR_min(tracks)

W+HSS BDT score

DJ+W preselection, Full Run 2

Correlation: -0.018

Correlation: -0.007 Construct ABCD plane with

2 uncorrelated variables

ABCD plane

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FINAL SELECTIONS

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W+ALP selection: W+HSS selection: DJ+Z high-ET selection:

➤ Final selections for DJ+lepton channels (after preselection) obtained with the selection optimization procedure:

DJ+W channel:

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As in previous CalRatio analyses, use the modified ABCD method for background estimation. Simulataneous S+B likelihood fit to all regions. Pair of uncorrelated variables used:

➤ BDT score

➤

ABCD method is tested in validation regions (VRs), orthogonal to signal region A. Checked that there is low signal contamination: reduced a factor of 10 between expected in region A and VRs.

S / B

ABCD METHOD VALIDATION

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VR-CD for W+ALP selection

Define region A’ boundaries

Move boundaries across VR and compare observed and expected number of events in

A’ using ABCD method

Agreement within statistical errors

Reg A cut in final selection

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ABCD METHOD VALIDATION

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ABCD plane validation for DJ+W WHSS selection and DJ+Z high-ET selection:

VR-CD for W+HSS selection

VR-CD for DJ+Z high-ET selection

Non-closure in two extremes,

low stats

Reg A cut in final selection

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EXPECTED LIMITS: DJ + W

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Region A signal efficiency: First limits for this model!

Does not improve 2CR

FR2 limits Improve 2CR FR2 limit (but

different production mode)

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EXPECTED LIMITS: DJ + Z, HIGH-ET SELECTION

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Region A signal efficiency:

Improve 2CR FR2 limit (but different production mode)

Generally, factor 10 improvement wrt previous CR+Z analysis limit for similar mass points (not exactly

the same in some cases)

(CR+Z analysis used 2015+2016 data)

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BACKUP SLIDES

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LEPTON TRIGGERS

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