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NSW background Studies: high mu samples

NSW background Studies: high mu samples. Niels van Eldik, Peter Kluit, Siyuan Sun Muon Simulation 28 March 2013, MCP 3 April. Introduction. Continuation of the backgrounds for the NSW TDR Look at the high mu ‘single bunch’ run and compare data and simulations High mu run with 2 bunches:

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NSW background Studies: high mu samples

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  1. NSW backgroundStudies:high mu samples Niels van Eldik, Peter Kluit, Siyuan Sun Muon Simulation 28 March 2013, MCP 3 April

  2. Introduction Continuation of the backgrounds for the NSW TDR Look at the high mu ‘single bunch’ run and compare data and simulations High mu run with 2 bunches: data12_8TeV.00206725.physics_MinBias.recon.ESD.f456 Simulation without cavern bkg. It includes Min Bias in time pile up events. No out of time pile-up is added. The mu is flat between 38 and 71. mc12_8TeV.107499.singlepart_empty.recon.ESD.e603_s1469_r3841

  3. Backgrounds in the muon system • Several backgrounds affecting the performance of the Muon • system can be distinguished • Prompt background of charged particles such as muon, pions and protons arriving soon after the collision (<200 nsec) • Background from the activation of the materials in ATLAS (small effect 4-10% total rate) • Background from the photons and neutrons that stay long in the detector and eventually convert (decay) and leave a signal in the detectors • These background have different characteristics: they can leave a • single hit or leave signals in different sequential detector layers. • The “random” single hit background is affecting the “total” hit rate • significantly; however in the pattern recognition random hits are • easily removed. It is e.g. more important to simulate are the signals • In different sequential detector layers.

  4. Questions How well is the simulation describing the data? Of course the “singles” will be very difficult to model well. How is the situation for the backgrounds that leave signals in different sequential detector layers. How well are these described? Are these mainly prompt particles arriving within 200 nsec? And therefore well-described by our current pile-up simulation? Try to address these questions comparing data and simulations to achieve a more profound understanding of the backgrounds. The high mu sample allows to study the Minimum bias Background in time pile-up in the Muon System for data & MC. This can be extrapolated to the situation with many bunches: e.g. the standard physics runs. In that case out of time pile up and cavern background become relevant. The question is how much of the background is due to pile-up (and described by the ATLAS pile-up simulation) and how much to cavern background.

  5. #Vertices in data and MC . Data and MC are quite close

  6. Clustering algorithm Clustering algorithm: aim to classify the hits of neighbouring tubes. Important is the depth; how many sequential layers have been fired. For the MDTs done per multilayer. So maximum depth is 3 or 4. For the CSCs this is max 4. For the trigger chambers the clustering is performed per gas gap max 2 RPCs and 2 or 3 for TGCs. Cluster multiplicity = depth; For the MDTs an ADC cut at 50 is used. For the CSC a charge threshold of 50 k is used. So in e.g. layer 1 each hit has 2 neighbours in the same layer and 2 in the next layer and 3 in third layer The algorithm e.g. will flag a hit as “single” if it has NO neighbours in the other layers. O O O O O O O O O O O O O O O O O O O O O depth

  7. Clustering algorithm: singlets etc. Here one cluster is found with multiplicity = 2 It consist of three hits: each of them will have multiplicity = 2 One cluster with multiplicity = 1 O O O O O O O O O O O O O O depth In all the histograms a loop over the MDT/RPC/TGC/CSC hits is performed and e.g. the multiplicity is filled. This means that in the above case three hits will go in the mult=2 bin and 1 hit in the mult=1 bin.

  8. Clusters associated to a muon • The clusters are divided into two groups; • Clusters associated to a muon • Background clusters (so not associated to a muon) • In the reconstruction three types of muons are made: • - Combined muons that have a ID track + MS track • - Standalone muons that have no ID track and MS track • Segment tagged muons that have an ID track and associated segments • The minimum bias data that is discussed here has in general soft or low pT muons; The modeling of this background is quite difficult and is of the 10-20% level. • It is e.g. clear that the data/MC agreement for clusters for muons from a Z with a pT above 20 GeV is much better and typically a few percent or better.

  9. RPC multiplicities Entries / event BM 0 BM 1 BO MC

  10. RPC positions Entries / event BM 0 BM 1 BO Peaks absent in data

  11. RPC Timing in data and MC Entries / event Note unit is tics Data has longer tail . MC window shifted and smaller MC has a larger fast peak

  12. RPC Timing in data and MC Entries / event MC is shifted to data . Observe more fast component in MC: it arrives after the prompt component (see prompt muon tracks peak at 30 tics) The data has a longer tail (longer life time component.

  13. RPC data vs MC It is clear that a precise modeling of the time structure and the multiplicities is not easy. The non-prompt peak has its maximum 30 nsec afterwards and has an exponential decay with a lifetime of ~25 nsec. They come from low momentum particles that takes a long time to reach the RPC (loop in the field etc.) Another very important aspect is the ‘absence’ of events in the data in the region of 0-60 nsec. These events would be due to a long lifetime (e.g. 25 μsec) component of the previous bunch due to e.g. cavern background. The fraction of cavern background events in the full RPC readout window for doublets is measured to be: 6.5 10-3 For an adequate (RPC) simulation with many bunches it is important to simulate both the short and longer time structure. The distributions of events in data and MC show in MC peaks that are ‘absent’ in data.

  14. RPC data vs MC Situation for background clusters (data/MC ratio) HighMu unassociated RPC cluster multiplicities single doublet doublet+triplet RPC M0 0.87648 0.757905 0.757905 RPC M1 0.92048 0.793519 0.793519 RPC BO 1.58985 1.19762 1.19762 Situation for on track clusters HighMu on track RPC cluster multiplicities single doublet doublet+triplet RPC M0 0.672325 0.830437 0.830437 RPC M1 1.56256 0.772878 0.772878 RPC BO 0.704683 0.791829 0.791829 Modeling is a the 20% level (except singles) both for background and track clusters.

  15. TGC multiplicities Entries / event EM 1 EI EM 0

  16. TGC positions data Entries / event EI EM 1 EM 0 A side C side

  17. TGC positions MC Entries / event EI EM 1 EM 0 A side C side

  18. TGC Timing in data Entries / event doublets On track singlets Inner Middle

  19. TGC Timing in MC Entries / event Inner +25 nsec peak is much lower in MC Middle

  20. TGC data vs MC The TGC simulation suffers from mismodeling of the multipicities (see next slide for quantitative estimate). The +25 nsec component is too small and also the prompt component shows a different multiplicity. Notice the ‘absence’ of events in the data in the region of -25 nsec. These events would be due to a long lifetime (e.g. 25 μsec) component of the previous bunch. Before/In time doubles: Inner = 2.1 10-3 Middle = 3.2 10-3 This means that the cavern background in the TGC data is completely neglible. For an adequate TGC simulation with many bunches it is important to simulate both the short and longer time structure.

  21. TGC data vs MC Situation for background clusters (data/MC ratio) HighMu unassociated Tgc cluster multiplicities single doublet doublet+triplet TGC I 0.708412 1.30644 1.30644 TGC M0 6.05833 2.56487 2.34065 TGC M1 3.10919 2.20493 2.20493 Situation for track clusters HighMu on track Tgc cluster multiplicities single doublet doublet+triplet TGC I 0.528441 1.22631 1.22631 TGC M0 1.09185 1.04012 1.06119 TGC M1 0.981467 1.1177 1.1177 The background clusters show large deviations in the EM TGCs up to a factor 2. For the EI the modeling is at the 30% level. The track clusters are modeled at the 20% level.

  22. CSC multiplicities Entries / event Singles in MC (data/MC): 0.90 Triplets+Fourlets: 1.2 The time structure looks mismodeled; I don’t know if this has any impact. For t = 0 no events are found in data. Indicating that events from the previous bunch are heavily suppressed.

  23. CSC Timing in data and MC Entries / event . MC is shifted 50 nsec

  24. MDT Timing in data: EI Entries / event Singles Doubles+Triplets+ Triplets+Fourplets . Less and less long lifetime background Trigger chamber confirmation

  25. MDT Timing in MC: EI Entries / event . No long lifetime background: due to a) not simulated b) tdc cut too close to tmax

  26. MDT Timing in data: BI Entries / event .

  27. MDT Timing in MC: BI Entries / event Artefact from tdc cut near tmax .

  28. MDT driftradii inTime: data Entries / event Most of the hits are from NON singles: tracks that cross several MDT layers .

  29. MDT driftradii inTime: MC Entries / event Most of the hits are from NON singles: tracks that cross several MDT layers .

  30. Interpreting the MDT data One can distinguish three regions: In time hits: Total = bin(350-2350) Before or Low bin(0 – 350) / Total After or High: (2350-3050) /2/Total It is clear that there exist a rather flattish background before and after the in time hits. On the next slide all the numbers for the different regions will be extracted. The question is: what are these backgrounds? a) These hits could be from the activation of the material. This contribution has been measured to be about 4% of the total hit rate. b) These hits are from the previous bunch crossing (or before that). There could also be a mixture of a) and b). The physics interpretation would be very different for high pile up. For b) the flat bkg would increase with more bunches.

  31. Interpreting the MDT data Here the fraction of cavern background events in a 1000 tdc count window is measured extrapolating from the early hit times. singles 2-3-4 3-4 EI 0.0248 0.0095 0.0034 EM 0.0330 0.0071 0.0022 EO 0.0758 0.0134 0.0043 BI 0.0351 0.0129 0.0055 BM 0.0551 0.0120 0.0030 BO 0.0802 0.0158 0.0052 One can conclude that the contribution of cavern background in the high mu minimum bias data is small: even at the singles level (maximum 8%). To extrapolate this to a standard run with 1365 bunches in and out of time pile up. A multiplication factor of about 40 should be used.

  32. MDT multiplicities data inTime hits Entries / event Inner Middle Outer Endcap . Barrel

  33. MDT multiplicities MC inTime hits Entries / event Inner Middle Outer Endcap . Barrel

  34. MDT multiplicities Data/MC Here below the data/MC ratios: single doublet triplet fourplet EI 1.15177 1.17435 0.899579 0.825976 EM 6.08963 4.71696 2.15348 EO 9.62374 5.48689 1.72233 BI 0.517381 0.643963 0.457641 0.358345 BM 0.816511 0.808801 0.60677 BO 1.86252 1.56034 1.06049 One observes quite large discrepancies in the Endcap singles in particular in the EM and EO. Also the EM and EO doublets and triplets are quite off. Note that also for the TGCs the EM modeling showed problems. Could the EM/EO discrepancy be due to the eta cut-off at 5 used in the minimum bias simulation? Corresponds to R = 94 cm @ z = 7 m or R = 160 cm @ 12 m? The BI fourplets are a factor 3 lower in data.

  35. Cross check in Z mumu data for RPC /TGC confirmation Entries / event Inner Middle Outer Endcap . Barrel

  36. MDT multiplicities Data/MCRPC TGC confirmed Here below the data/MC ratios (red curves slides 30 and 31): single doublet triplet fourplet EI 0.949009 0.898096 0.660706 0.946118 EM 4.38469 3.04211 1.66113 EO 10.8387 4.71538 2.02771 BI 1.10883 0.927998 0.604537 0.602793 BM 0.685074 0.632675 0.612075 BO 1.41732 0.953499 0.981705 Agreement data/MC improved. In particular in BI. However EM/EO is still off by a factor 1.7-2 for triplets.

  37. MDT positions data inTime hits Entries / event Inner Middle Outer Endcap . Barrel

  38. MDT positions MC inTime hits Entries / event Inner Middle Outer Different Endcap . Different everywhere in Barrel Barrel

  39. MDT positions in Time data Endcap A/C Entries / event Inner Middle Outer A side . Note more singles in out of time: shapes different. Clearest in BI C side

  40. MDT positions data out of Time hits Entries / event Inner Middle Outer Endcap . Note more singles in out of time: shapes different. Clearest in BI Barrel

  41. MDT multiplicities Data/MCfor muons in Min bias Situation for track clusters (data/MC ratios): single doublet triplet fourplet EI 0.989387 0.985336 0.699158 0.968425 EM 1.24788 0.817229 1.08022 EO 0.812902 0.628878 1.00825 BI 0.818483 1.45467 0.788385 0.968876 BM 1.77719 2.66773 0.886615 BO 0.777897 0.443689 0.805779 One observes a quite reasonable data/MC agreement In particular for the I fourplets and MO triplets: 10-20% The doublets multiplicities are in agreement for Endcap at the level of 20-40%. For the Barrel the discrepancies go from 0.4 to 2.7

  42. Overall picture / summary • Modeling of the background clusters: • RPC at the level of 20% (doublets) • TGC EI 30% EM0-1 factor 2.5 off (2-3) • MDT Barrel: factor 0.3-1.5 (3-4) • MDT Endcap: EI 20% EM-EO factor 1.7-2.2 (3-4) • Rather large mis-modeling in BI (0.3) EM/EO (2). • Using RPC or TGC confirmed clusters the BI mis-modeling improves. The EM/EO remains problematic. • The modeling of the track/muon clusters is much better. • For RPCs, TGCs and MDTs an agreement at the level of 20% is measured.

  43. Z mumu data and simulation Here compare data and simulations for Z mumu; Zmumu Period D: data12_8TeV.periodD.physics_Muons.PhysCont.DESD_ZMUMU.repro14_v01 Simulation without cavern background, including full pile-up mc12_8TeV147807.PowhegPythia8_AU2CT10_Zmumu.recon.ESD.e1169_s1469_s1470_r3658 Only for 1 plot: Simulation with pile-up and cavern background FLUGG. This is the sample used for Siyuan Sun his studies. Pile-up L = 3 103 + 20 cavern bkgs. mc11_valid106047.PythiaZmumu_no_filter.recon.ESD.e1009_s1474_s1476_r3399

  44. MDT positions data Zmumu Entries / event Inner Middle Outer Endcap . These are very similar to the high mu plots (next slide)! Barrel

  45. MDT positions data high mu Entries / event Inner Middle Outer Endcap . Barrel

  46. MDT positions MC Zmumu Entries / event Inner Middle Outer Endcap . Also MC Zmmu and high mu is quite similar which is ‘expected’ Barrel

  47. MDT positions MC high mu Entries / event Inner Middle Outer Endcap . Barrel

  48. MDT positions MC FLUGG+pile-up Entries / event Inner Middle Outer Endcap . FLUGG (with pile-up L = 3 103 + 20 cavern bkgs) is quite different Barrel

  49. Qualitative picture • The backgrounds in the Z mumu pile up sample are pretty similar in shape to the high mu background sample. • In Z MC with pile-up one can observe the same thing. • Note that in the data plots one will have pile-up plus cavern background. • There is substantial mismodeling in the high mu MC; the same mismodeling is propagated into the Z pile up MC. • FLUGG with pile-up does not seem to improve this. • One could try a model where the ratios of data/MC for high mu are used to correct for the mismodeling in Z data/MC.

  50. Endcap and NSW: segment clusters • In order to understand the backgrounds in the Endcap and NSW further MDT studies were performed using the high mu data and MC samples listed on slide 2. • A segment cluster analysis was developed on the basis of the MDT clusters (that are made per multilayer see slide 6); and a search was performed for clusters in several multilayers within one MDT chamber. • This allows to find in a very robust way – based on tubes so not using the actual drift time – segment clusters. • This then allows to study EI, EM and EO segment clusters. On the basis of these segment clusters a search for EI-EM and EM-EM and finally EI-EM-EO matched segments clusters is performed.

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