Missing ET Resolution in Minimum Bias data at 7 TeV

K.Wang & R. Keeler University of Victoria

*http://www.phys.ufl.edu/~matchev/LHCJC/cern-lhc.jpg

s

Outline

1.1 Introductions - LHC1.2 Introduction - ATLAS

2. Missing Energy

3. Minimum Bias3.1 Data3.2 Run 1535653.3 Rates3.4 1 Vertex MET Resolution

4. Pile up4.1 Definitions4.2 Rates

4.3 In Data

5. MET Resolution

6. Comparing Datasets

7. Further Work

8. Conclusions

1.1 Introduction - LHC

CERN – European Organization for Nuclear Research, formed in 1954

Important source of Particle physics research ever since

Constructed in the tunnel originally used for LEP the LHC is the most powerful particle accelerator built to date. Center of mass energies in hadron collisions to reach 14 TeV center of mass energy, with peak luminosity 1034 cm-2s-1

The success of many hypothetical extensions to the Standard Model rests on the success of the LHC

As of March 2010 7 TeV collisions have occurred

3.5 pb-1 of real data is now available for analysis

Parameter Performance Goals Run Analyzed Current August

CM energy 14 TeV 7 TeV 7 TeV

Peak Luminosity 1034 cm-2s-1 0.014*1030 cm-2s-1 6.7*1030cm-2s-1

Bunch Pairs 2808 2 48

Bunch Spacing 25 ns 45 μs Trains

Beam Current 584 mA ~0.05 mA ~8.5 mA

1.2 Introduction - ATLAS

General purpose spectrometer of proton-proton collisions

The detector is designed to fulfill many objectives – it is a high precision test of qcd, flavor physics and electroweak theory. In addition it is of course, searching for evidence of new physics, Higgs, SUSY, Heavy gauge bosons...

The parameters of the LHC puts stringent requirements on its detectors

Fast electronics, high granularity

High hermiticity (angular coverage)

Good particle momentum resolution and reconstruction efficiency

Efficient triggering on low momentum objects while rejecting background

Good particle identification

*http://www.slipperybrick.com/wp-content/uploads/2006/12/atlas-detector.jpg

*http://brock.physik.uni-bonn.de/atlas.php?lang=en

Liquid Argon Calorimeter

*http://www.boston.com/bigpicture/2008/08/the_large_hadron_collider.html

2 Missing Energy

An interaction occurs in the ATLAS detector, the final state particles diverge outwards from the interaction point

The particles are seen by the calorimetry and the triggering system records it – this is now an event

Sum of final state vector momenta from an interaction

Given that for highly energetic events

The resultant vector tells you about what your detector missed and about its orientation

If our detector was 100% efficient and had perfect resolution MET should be 0.

A good measurement of missing transverse energy is crucial to many studies. In particular searches for new physics (SUSY, extra dimensions) all depend a great deal on very good MET measurements for a given interaction.

True missing energy is of course the smoking gun of new physics

E2=p2c2

m2c 4≈p2c2

2 Missing Energy

In minimum bias data MET is reconstructed using the calorimeters with corrections for muons

E xmiss

=− i=1N cell Ei sinicosi

E ymiss

=− i=1N cell Ei sinicosi

Emiss=Ex

miss2E y

miss

2

Due to granularity (calorimeter has ~187 000 cells), it is vital to suppress noise contributions.

The sum over Ncell

is restricted to topological clusters which have received hits, there is an algorithm that determines what goes into N

cell based on noise levels

Cells flagged by ATLAS as noisy are also not included.

2 Missing Energy

The ntuple variables we used to analyze MET are “refined final” - i.e. MET_RefFinal_et

These are the recommended analysis variables

The MET variables in ATLAS are reconstructed in a step by step process.

First the muon contribution is obtained from the muon spectrometer reconstruction

Then there is a correction for energy losses in the cryostat

Finally the contributions from each reconstructed particle in the collision are added on to create the final term

The contributions of MET_RefFinal, which we used are shown on the right

MET_Ex(y)MissRefEle Contribution from cells in electrons

MET_Ex(y)MissRefGamma Contribution from cells in gamma

MET_Ex(y)MissRefTau Contribution from cells in tau-jets

MET_Ex(y)MissRefJet Contribution from cells inside jets

MET_Ex(y)MissRefMuon Contribution from cells in calorimeters crossed by

muons

MET_Ex(y)MissCellOut Contribution from cells in Topo outside objects

MET_Ex(y)MissMuonBoy Contribution from the Staco muon collection

MET_Ex(y)MissCryo Cryostat correction from ConeTopoJets

= =

MET_RefFinal Linear sum of above

*Atlas TWiki

We can parametrize the MET resolution in terms of the total energy deposited in the detector.

This is the sum of the scalars of the energy.

The d3pd variable is MET_RefFinal_sumet

Its construction is similar to that for MET_RefFinal_et.

Following the same procedure used forMET_RefFinal components above

Specifically then in terms of the physical accelerator the scalar sum of the missing transverse energy is

ET= i=1Ncell Ei sini

2 Missing Energy

3 Minimum Bias Data

Minimum bias data is data taken with an inclusive trigger

It is called minimum bias data because the goal is to trigger on and record any kind of p-p interaction with no bias

In ATLAS the minimum bias events can be triggered by the dedicated Minimum Bias Trigger Scintillators (MBTS) in coincidence with the Beam Pickup Timing Device (BPTX).

The BPTX are mounted 175 m away from the bunch crossing point in both directions in the ATLAS detector.

The BPTX allows the observer to see the bunches as they pass towards the collision point, and thereby allows monitoring of the beam.

More importantly for us, the BPTX provides the L1 trigger with accurate timing references whenever a bunch passes it. This is used as a triggering condition for minimum bias events.

The MBTS are mounted on the Liquid Argon Endcap cryostats, with a coverage of 2.1<|η|<3.8

This consists of two sets of 16 scintillator counters

The MBTS will only be functional for a short time at low luminosity as it is expected radiation damage will render them useless within several months of beam collisions

*https://twiki.cern.ch/twiki/pub/Atlas/MinimumBiasTriggerScintillatorInfo/photo.jpg

TOT=E SDDDNDCD

3 Minimum Bias Data

MB=NDDD SD

The usual experiment definition of minimum bias cross section is Non-Single diffractive events (that is Non-diffractive inelastic and double diffractive events) but ATLAS is sensitive also to single diffractive events.

The expected cross section for NSD is ~65 mb if we include single diffractive events this is ~90 mb.

The number of events as seen by the minimum triggers for the run we looked at is 21,282,442 and for the operating time this gives a rate of 148.417 Hz.

We should note there is always some bias in the data taking. In practice the detector will probably not see glancing blows, or particles that just go down the beam pipe.

Cross Section (mb) – (Pythia 6.205)

Elastic 22

Single Diffractive 28

Double Diffractive 10

Non-Diffractive Inelastic 55

Central Diffractive ~

3.1 DataRun 153565 with ~21 million events

The original raw data is reconstructed, 21 282 442 events this is the number seen by the minimum bias trigger, into

group10.perf-jets.data10_7TeV.00153565.physics_MinBias.recon.ESD.f251_JetEtMissDPDModifier000016.v4_EXT0

A skim was made on the RAW for pileup events, and then reconstructed with 89334 events -

group10.perf-jets.data10_7TeV.00153565.physics_MinBias_extractedBS.merge.ESD.o5_r1297_p161_JetEtMissDPDModifier000025.v1_EXT0

Monte carlo of minbias+pileup events do not yet exist, but has been requested

The unskimmed raw data was processed according to the following criteria for events with multiple p-p interactions – primary vertices >1* -

- a primary vertex (PV) must be compatible with the beam spot

- each PV must have more than 7 tracks with pT > 150 MeV;

- a PV must not have more than 25% of outlier tracks (to reject overlapping vertices).

This skimmed data consists almost exclusively of pileup events, in practice there are 65 1 vertex events that appear in the skimmed data

*https://twiki.cern.ch/twiki/bin/view/Atlas/InterestingSkims

3.2 Run 153565

*ATLAS Run Query

3.3 Minimum Bias – Rates

In a vacuum, the rate at the LHC is

The rate seen by the minimum bias trigger is

Also we know the number of bunches in the detector. The bunch crossing rate is

We can then calculate the events/bunch crossing rate from the minbiasrate

This is now properly – the rate of minimum bias events per bunch crossing as seen by the minbias trigger.

If we take the predicted minimum bias cross section as ~90 mb then the total number of minimum bias events predicted with a total run luminosity of 0.78 nb-1 is

Why the discrepancy with what the trigger sees? We know the total luminosity 0.78 nb-1 from machine monitors, so if we worked backwards to obtain the cross section we find

A question of efficiency?

R= pp LEventsTime

=148.417 Hz

N=∫ Ldt=9∗10−26×7.8∗1032=7.02∗107

Circumferencevelocity of bunches

×bunch pairs≈27 km2

c=22.22 kHz

minbias ratebunchrate

=0.0067eventsBC

minbias≈29mb

3.4 Minimum Bias Data

With the minimum bias data we can plot the MET Resolution plotted against the absolute value of the sum of the transverse energy deposited into the detector (~essentially the strength of the interaction)

4 Pileup - Definitions

We distinguish between in-time pileup and out-of-time pileup

We imagine as proton bunches circle around in the accelerator in any given collision we may have more then one pair of protons colliding, this is in time pileup

Alternatively if multiple proton bunches are traveling in the accelerator and the bunch spacing is small, the first collision will occur within the detection chamber. As the product particles stream outwards the next collision (proton bunches trailing behind) occurs before the initial post-collision particles can fully evacuate the chamber. This is out-of-time pileup.

In this analysis we are strictly concerned with in time pile up. There should be no out-of-time pileup due to the spacing between the bunches.

What constitutes a pileup event is determined by the number of vertices. We consider any two events with more then two vertices as being pileup (excluding decay)

To distinguish between vertices due to additional collisions and vertices due to decay, there is a D3PD variable called vxp_type. Pileup vertices have vxp_type=3.

In addition there is a dummy vertex in every event, these we ignore.

4.1 Pileup

We expect the number of vertices in a collision to go as a poisson distribution

Examining the skimmed data, which contains all pile up events that passed the appropriate cuts we can fit the number of 2, 3, 4 vertices events to a Poisson distribution

If we extrapolate back to 1 vertex events, there would presumably be (2.78±0.03)*106 - that passed those same cuts

P=A

n e−

n !

A=4.71±0.05×107

=6.29±0.03×10−2

Vertices Events

2 87399

3 1841

4 29

P=A e−

x−

2

A=3.98±0.02×103

=3.8±0.3

=33.0±0.2

5 MET Resolution

We can quantitatively analyze the difference between the MET resolutions with respect to the number of vertices by approximating the statistics with an acceptable function.

This does become problematic if one does not have enough statistics

MET _ RefFinal _ et=A[MET _ RefFinal _ sumet ]

Vertex A α (10-1)

1 18.00±0.04 5.030±0.004

2 17.1±0.8 5.06±0.04

3 6±3 5.9±0.4

4 (2.8±0.1)*103 1±2

6 Comparing the Datasets

In order to understand the effects of multi-interaction events with respect to single interaction events we have to make sure the skimmed and unskimmed data sets are actually consistent with one another

Any comparison that we make then to the original data set (unskimmed) wouldn't make sense unless we applied the same cuts.

For example – if we use the Poisson distribution to extrapolate back to however many 1 vertex events are expected – that number would only correspond to the number of 1 vertex events that passed the cuts above and not the complete number of 1 events in the unskimmed data.

We have to make sure the two data sets (skimmed and unskimmed) are consistent.

6 Comparing the Datasets

How do we do this?

The skimmed data should be completely contained within the original unskimmed data

Let's work with a few variables (in principle any number of variables present in both d3pds should do)

1. Number of Tracks per Vertex (vxp_nTracks)2. Sum of transverse momentum per Vertex (vxp_sumPt)3. Sum of energy per vertex (vxp_E)

We want to know if the vertices we find in the skimmed data are consistent with what is in the unskimmed data.

For the skimmed data create 3 plots which consist of 1, 2 and 3 of the above but per event rather then per vertex (sum up the vertices for every event).

For the unskimmed data create plots of 1, 2 and 3 as they are.

Generate random numbers according to the unskimmed distributions above in accordance to the vertex information in the skimmed data ,creating generated plots of 1, 2 and 3 per event

If everything was ideal the generated distributions of 1 2 and 3 per event should match the real distributions created from the skimmed data

6.1 Problems

Obviously this doesn't work perfectly well...yet

What is wrong? This tells us our cuts are not correct.

Also the above consistency test was not made on the full unskimmed data set but only a portion of them (so the first thing to try is to run over all the data!)

The easiest way to solve this is to find the person who did the reconstruction and ask him/her!

So far no luck in that department, but also have not tried very hard

But since the data was skimmed from the RAW it's possible the reconstructed d3pds lack the necessary tools for skimming

The stupid way of doing this would be to skim the original ~21 million d3pd dataset for pileup events, then try to match variables in the JetETMiss skim to our skim, presumably one would see signs of cuts

For example in the JetETMiss d3pd one may find there are no vertices with less 7 tracks, indicating that such a cut has to be made the unskimmed data.

Rinse and repeat this until our skim is reduced to ~89k pileup events. Then reskim the ~21 million events, this time apply the pileup cuts to 1 vertex events

In this manner we could then obtain all 1 vertex events cut according to the pileup criteria

7 Life is a work in progress

Obtain all 1 vertex events that pass the skimmed dataset cuts (Make correct cleaning and selection cuts as prescribed by JetEtMiss Group)

Monte Carlo of minbias+pileup does not yet exist. Once these are made we can compare real data results to simulations

If we can correctly apply the cuts to the original data, a Kolomogorov-Smirnov test is the quantitative description of the probability that two distributions originate from the one and same underlying distribution. This applies to the consistency tests in the previous couple of slides.

Look at more data?

8 Conclusions

We have looked at the MET resolution in 7 TeV minimum bias data coming from ATLAS with respect to the effects of pileup

We validated the consistency of the data by examining various physical parameters and checking they made sense

The form of the MET resolution as parametrized by total transverse energy output into the detector does not seem to change with increasing pileup -

Although at greater then 3 vertices we do not have enough statistics to say for certain

As a function of total energy deposited in the calorimeter, the MET resolution for 1, 2 and 3 vertex seem to go roughly as the square root of the former.

We predicted the average number of interactions from the skimmed minimum bias data to be-

Predictions of minimum bias cross section from MC do not seem to match the cross section seen by the trigger system. There is probably a question of efficiency here, this needs to be looked at more carefully.

Monte Carlo data has yet to be examined but would prove a useful check on these results

For 1 vertex events our data analysis methodology is not yet fully formulate and needs more corrections for selection and cleaning.

6.29±0.03×10−2

Acknowledgments

Technical Assistance

Vikas Bansal, Ashok Agarwal, Frank Berghaus.

References

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