minimum bias studies

UK ATLAS Physics Durham 06

Minimum bias studies

Craig Buttar


Outline

• Minimum bias events– Why study them?– What are they?

• Triggering– How many events do we want?– Random trigger– Minimum bias trigger scintilators (MBTS)

• Tracking– Reconstructing low pT tracks– Using pixel hits– Vertex reconstruction

• HLT• Minimum bias group• Summary, future work/open issues


Why measure min bias?

Not exactly what the LHC was built for!But….. • Physics: measure dN/d|=0

– Compare to NSD data from SppS and Tevatron

• MB samples for pile-up studies– Calorimeter– Physics analyses

• Overlap with underlying events– analyses eg VBF, Jets…

• Demonstrate that ATLAS is operational• Inter-calibration of detector elements

– Uniform events• Alignment • Baseline for heavy ions


Soft pp collisions

pp collisions at pp collisions at

√√s = 14TeVs = 14TeV

PYTHIA6.323PYTHIA6.323 PHOJET1.12PHOJET1.12

σtot

σelas

σsingle dif (AB→XB)

σ single dif (AB→AX)

σdouble dif

σnon-dif

101.5 mb101.5 mb

22.2 mb22.2 mb

7.2 mb7.2 mb

7.2 mb7.2 mb

10.3 mb10.3 mb

54.7 mb54.7 mb

119.1 mb119.1 mb

34.5 mb34.5 mb

5.5 mb5.5 mb

5.5 mb5.5 mb

4.1 mb4.1 mb

69.5 mb69.5 mb

inela

stic

eve

nts

inela

stic

eve

nts

A. Moraes et al. – SN-ATLAS-2006-057A. Moraes et al. – SN-ATLAS-2006-057

Trigger bias:Typically select non-single diffractive

(NSD) events


Event characteristics

• Event characteristics– Non-single diffractive~non-diffractive

inelastic

– Soft tracks pTpeak~250MeV

– Approx flat distribution in to ||~3 and in

– Nch~30; ||<2.5

• Trigger rates ~70mb (NSD!)

– R~700kHz @ L=1031cm-2s-1

M. Leyton


How many events?

• For dN/d require ~10k• For UE need ~20M MB events to get some with leading jets

PT~30GeV• Need to overlap with jet triggers

– Prescaled PT>25GeV• ~40M events gives ~10 hits/strip in barrel SCT• CMS quote ~18M to intercalibrate their calorimeter with MB


Random trigger

• 75ns @ L=1031cm-2s-1 gives <nx>~0.05Probability of 1 event/xing =0.05Assume 1 month of running with eff=50%For 20M events to tape from HLT1kHz L1 random triggerProcess in EF to remove empty events

eg track reconstruction20Hz output from HLT

Assumes 100% efficiency in identifying MB and rejecting backgrounds

How to identify MB in EF LaterProbably need x2 safety factors at L1 and EF

There is a built in safety factor – empty events are smallFor lower luminosity ÷10 need to increase L1 random

rate by x10 to maintain 20Hz HLT rate


MBTS

• Trigger scintillation counters mounted on end of LAr calorimeter covering same radii as ID– Cover 2<||<4

• Can be used for first data BUT!– Not rad-hard– Uses 1/8th of tilecal readout Lifetime unknown

• At L1 S/N is ‘modest’ – Now in simulation can be tuned to

measurement in the summer• Can do better at L2 with precision readout• Trigger logic not defined

– Likely to be coincidence between hits in forward and backward hemisphere

ηη=2.0=2.0

ηη=3.8=3.8interactiointeraction pointn point

Beam-pipeBeam-pipe

++ηη

pan

nel

pan

nel

MBTMBTSS


Non-diffractive+SD+DD

Non-diffractive only

Diffractive events

Coincidences in MBTSPYTHIA charged-particle level


MBTS response

Non-diffractive inelastic events (sample 005001)

Non-diffractive Non-diffractive inelastic events: inelastic events: energy is deposited energy is deposited on both sides on both sides (typically)(typically)

Single and double diffractive inelastic events (sample 005002)

Single diffractive Single diffractive events: energy is events: energy is deposited on deposited on either sideeither side

Double diffractive Double diffractive eventsevents

A.Moraes


Tracking in MB events

• Acceptance limited in rapidity and pt

• Rapidity coverage– Tracking covers ||<2.5

• pT problem

– Need to extrapolate by ~x2 Need to understand low pt

charge track reconstruction

1000 events1000 events

dNdNchch/d/d

dNdNchch/dp/dpTT

Black = Generated (Pythia6.2)Black = Generated (Pythia6.2)

Blue = TrkTrack: iPatRecBlue = TrkTrack: iPatRec

Red = TrkTrack: xKalmanRed = TrkTrack: xKalman

Reconstruct tracks with:Reconstruct tracks with: 1) pT>500MeV1) pT>500MeV 2) |d2) |d00| < 1mm| < 1mm 3) # B-layer hits >= 13) # B-layer hits >= 1 4) # precision hits >= 84) # precision hits >= 8

pT (MeV)


Track reconstruction efficiencies in minbias events

I. Dawson, K. Prokofiev

Athena 11.0.41• Started looking at primary particle reconstruction efficiencies in minbias events: → usually want to measure primary

particle distributions such as dNch/dη.

• Primary charged particles selected as those originating within a volume around interaction point

• Hit based truth matching using TrackTruthCollection (map between reconstructed and truth).– NB need to develop method

independent of truth!• Try to understand inefficiences and limits

in reconstructing tracks in this low-pT regime:→ Physics or algorithm, hardware?

(CSC11.005001.pythia_minbias.digit.RDO.v11000401)

xKalman

# charged particles produced in PV volume

# charged rec. tracks with associated PV particle εprimary =


Where is the Momentum Limit ?

• Tracker is in principle sensitive to soft tracks– Pt = 400 MeV - tracks reach end of TRT– Pt = 150 MeV - tracks reach last SCT layer– Pt = 50 MeV - tracks reach all Pixel layers

Do not need to run with low field

• Event graphics using Fatras simulation– Tools are there to tune for such tracks

150MeV

50MeV

400MeV

A.Salzburger

M.Elsing Talk in SM meeting


Low-pT track reconstruction

• A.Poppleton extending iPatRec to reconstruct tracks at lower pT:– Not clear what limit is?– Need to investigate

inefficiences and fake rates in this new pT regime – lots of testing

Reconstructed with

Athena 12.0.0

~450 particles

(CSC11.005001.pythia_minbias.digit.RDO.v11000401)

Progress so far

• Problem getting good efficiencies below pT ~ 300MeV – under investigation.

A.Poppleton, I.Dawson


Track multiplicity

X V

erte

x R

esid

uasl

Minbias Vertex Reconstruction

I. Dawson, K. Prokofiev

• How well can we reconstruct primary vertex position of a minbias event?

– A good vertex reconstruction efficiency in multi-pp bunch crossings may allow measurement of σNSD.

– Knowledge of primary vertex position can improve signal to noise, eg, H →γγ (UE to Higgs similar to MB)

• Use VxBilloirTools package to fit vertex to minbias tracks.

• Shown here are residuals (difference between reconstructed and truth)

Typically ~250μm precision


Tracking Strategy for soft Particles

• Step 1: Primary track reconstruction (= current NewTracking)– Track candidates in Pixels+SCT - ambiguity solution - TRT extension

similar in xKalman and iPatRec

• Step 2: Secondary track reconstruction (release 13)– TRT leftover segments - extension to SCT and Pixels - global ambiguity

• Step 3 (New): Soft particle reconstruction after primary vertexing– Select soft track candidates pointing directly to established primary vertex

- this would mostly reuse reconstruction tools from the common tracking framework

- dedicated search tuned specifically for very soft tracks (maybe even for pixel only tracks)

- direct control of soft tracking, no interference with default reconstruction

- Michael Leyton interested in this…



Pixel tracklets

To avoid pt-cut-off use pixel layers 0 and 1

R hits in layer 1

P.Steinberg - preliminary

22 RMinimise for layer 1 hitsrelative to layer 0

How to get efficiencies?

R hits relative to tracks


MB identification at HLT

• How to identify minimum bias in random trigger using HLT?• After offline studies, implement track reconstruction in EF• Min number of well reconstructed ID tracks

• This is (probably) ok for NSD sample but is biased

• Need to look at bias introduced ptmin~0.5GeV

• Lower pT cut-off possible but time consuming?

• Look at pixel hits • Reduce pT-bias

• Faster algorithm

• Reduce MBTS rate• At L2 and/or EF use high gain to improve S/N

• Endup with two complementary MB triggers


Minimum bias activities

• Minimum bias activities can be found at:– Min bias Twiki page:

https://uimon.cern.ch/twiki/bin/view/Atlas/MinbiasWorkingGroup– ~Weekly meetings

– Triggering

– MBTS

– Random+HLT

– Tracking

– Low-pT track

– Tracklet based

– Simulations

– minbias p+p

– Beam-gas, beam-halo

– Measurements

– Pt-spectra and dN/d– Calorimeter

Volunteers welcome!

https://uimon.cern.ch/twiki/bin/view/Atlas/MinbiasWorkingGroup

https://uimon.cern.ch/twiki/bin/view/Atlas/MinbiasWorkingGroup


Work to do

• MBTS– Measure low gain S/N for L1 – optimise logic– Invesitgate refinement at L2/EF using high gain readout

• Random– How to operate a low luminosity -- can event size be minimised?– Best way to identify non-empty events in EF– start looking at tracking

• Offline tracking– Study how to lower pT-threshold

• Investigate bias of triggers– Compare to different physics models– How to reject empty events, generate minimally biased stream

• Study use of calorimeter and LUCID– Looking at dN/d for 0

– Extend rapidity range of triggering


Extra slides


We are taking REAL DATA !

• SR1 cosmics test stand– Final TRT barrel

– Final SCT barrel

– 1 sector cabled for both

Day zero start-up looks promising!


• Offline reconstruction worked

with little problems within hours– Debug cabling…

• First results are very positive– See residuals and pulls

– No alignment or detailed

calibration yet !

– Reflects excellent mechanical

precision…

• More for Ringberg Workshop

in 2 weeks

• I am definitely very optimistic for first data taking having seen this !– Detector is on a good way !

• Pixels are recovering from cooling problem delay– Finish 3 layer system in time

First Results


Track reconstruction efficiencies in minbias events

~3.3~3.3

~2.4~2.4

Track pTrack pT T > 0.5 GeV> 0.5 GeV

~3.0~3.0

A.Moraes

• Investigating inefficiencies in minbias events at AOD level.

• Using cone method to match tracks to particles.

M.Leyton

• Involved in InDet software validation– Using InDetRecStatisitics package

• Looking at pion reconstruction efficiencies in Athena 12.0.0

11


Tracking: Startup-Initial Alignment

• Very first alignment will be based on:– Mechanical precision

– Detailed survey data

– Cosmics data (SR1/Pit)

– Minimum bias events and inclusive bb

• Studies indicate good

efficiencies after initial alignment– Example taken from T.Golling

– Precision will need Zs and

resonances to fix energy scales,

constrain twists, etc…


minimum bias studies

Documents

trigger bias

reconstructing tracks

mb eventsacceptance

l1 random rate

primary particle distributions

lowpt regime

truth matching

measure dndhh