1. NIPHAD meeting, Jan. 2006 by Sascha Caron How do we trigger beauty ? The Silicon Track Trigger at D0 The Silicon Track Trigger at D0 and ideas for ATLAS

2. Why trigger on beauty? – Outline Sascha Caron page 1 The story begins: Motivation for D0 and ATLAS The D0 and ATLAS trigger systems The D0 and ATLAS trigger systems The Silicon Track Trigger at D0 Improving the Silicon Track Trigger B-jet identification algorithms Summary

3. Tevatron instantaneous luminosity will reach 300E30 (in 1/cm^2*1/sec), a factor 3 increase from the current situation.  Triggering (thus online event selection) increasingly important for optimal performance (Important to note for ATLAS: Events which are lost in the trigger are lost forever!) Dijet background for Higgs  bb or the important calibration process (Z  bb) are in some channels so high that even triggering becomes difficult (at D0 e.g. HZ  vvbb)  Solution : Trigger on b-jets !  Solution : Trigger on b-jets ! Why trigger on beauty? – The Story begins Sascha Caron page 2

4. NIKHEF interest – My Marie Curie proposal Idea: Having the most efficient b-jet trigger can be “the advantage” for an early Higgs discovery at ATLAS o Test and implement b-jet trigger at D0 o Transform this knowledge (via Freiburg?) to ATLAS Why trigger on beauty? – The Story begins Sascha Caron page 5

5. B trigger at DØ Sascha Caron page 3 Find b-events early to keep high efficiency at an acceptable rate Find b-events early to keep high efficiency at an acceptable rate Eventspersecond QCD E T >30 GeV dijet production Goals Z->bb, HZ->bbvv, H->bb, etc. Z->bb, HZ->bbvv, H->bb, etc. maybe B physics b-jets E T >30 GeV Z-> b bbar Higgs->b bbar ZH-> bbvv, bH->bbb etc. 10-100 0.1 0.01

6. DØ in Run II The Silicon Track Trigger is based on information of the : Silicon Microstrip Tracker Central Fiber Tracker The Silicon Track Trigger at D0 Sascha Caron page 4

7. L1 Trigger decision time decision time about 4 μs 2000 Hz 1000Hz 50 Hz 2.5MHz L2 Trigger decision time decision time about 200 μs L3 Trigger decision time decision time about 50 ms D0 Trigger System p p bunch crossing frequency ¯ o Hardware based o tracks made with central fiber tracker, muons, New calorimeter trigger Provides jets, electrons taus (with shower shape) o Hardware/Software o simple jets, electrons, muons, taus o Silicon Microvertex improved tracks (STT)  L2 global processor combines information (e.g. STT tracks for very fast B-id) o Software based o partial event reconstruction (also simple B-id) The Silicon Track Trigger at D0 Sascha Caron page 5

8. L1 Trigger decision time decision time about 2.5 μs 75000 Hz 2000Hz 200Hz40MHz L2 Trigger decision time decision time about 10 ms (50*D0 time) using a farm of 1000 CPUs L3 Trigger decision time decision time about 1s Per event ATLAS Trigger System p p bunch crossing frequency o Hardware based o consists of calorimeter trigger, muon trigger and Central trigger processor Identification of calo. depositions as jets, electrons, taus Muon trigger: muons Position is recorded as Regions of Interest (RoI) o software algorithms (C++ code) o algorithms use portion of data defined in terms of RoI o track fitting using code Pixel and SCT hits as input (or TRT only) o Idea to run a b-tagging algorithm o Software based o (modified) offline algorithms have Full access to event data  event reconstruction Silicon Tracking in the Trigger at ATLAS Sascha Caron page 6

9. SiliconMicroDetector CentralFiberTracker L2STT L1CTT L2CTT L2Global other L2 pre- processors Detector Level 1 Level 2 Level 1 Detector Data flow chart L2 tracks The Silicon Track Trigger at D0 Sascha Caron page 7

10. Principal Idea B decay length is mm Impact parameter (2d in x-y plane) B decay products o Silicon Improved Tracks with 2d impact parameter 2d impact parameter o Select events with large impact parameter tracks Interaction point is mean beam spot The Silicon Track Trigger at D0 Sascha Caron page 8

11. 96’ SVT trigger proposal at CDF (first result about 2002) 98’Proposal (by Boston, Stony Brook, FSU, etc.) to build such a trigger at D0 Design algorithms for clustering of Silicon hits, track fitting Design and built trigger cards (FPGAs, DSPs) Write a trigger simulation (same algorithms as online) until 2004: Commissioning (do we understand the data, does the data agree with the simulation, do the trigger tracks agree with the offline (fully reconstructed) tracks) Can we further improve the trigger? How to use the trigger? old NIKHEF interest: top, Z  bb, ATLAS? How do you get such a trigger? Sascha Caron page 9

12. How can the tracking be improved? o Tracks found at L1 with the Central Fiber Tracker are used to define roads into the Silicon o Silicon hits are clustered o Track is re-fit within the road (IP, χ 2 ) within about 50 µs (IP, χ 2 ) within about 50 µs The Silicon Track Trigger at D0 IP resolution ≈ 50 μm ≈ 50 μm Sascha Caron page 10 Silicon detector Fiber Tracker select event by a cut on IP

13. Silicon Track Trigger cards Road data to Fiber Road Card - Receives and distributes L1 tracks - Communicates with Trigger framework framework SMT data to Silicon Trigger Cards Cards - Perform clustering and cluster- road matching - Clusters SMT hits + pedestal correction - Axial clusters are matched to the roads Fitting done in Track Fit Card - receives road and axial clusters - convert to physics coordinates via LUT - perform track fit φ(r)= b/r+kr+φ 0 by minimizing chi 2 = Σ i ( φ i - φ(r)/σ i ) 2 - beam spot correction - output tracks to L2CTT The Silicon Track Trigger at D0 Sascha Caron page 11

14. Commissioning  How does the online clustering work? Compare ADC weighted centroids (online to offline clustering) Sascha Caron page 12 Clustering strips as a function of channel and chip id with 2 treshholds

15. Clustering agrees between simulation and hardware. Sascha Caron page 13 Commissioning  How does the online clustering work?

16. At the beginning puzzling results (have to understand offline clustering) Sascha Caron page 14 Commissioning  How does the online clustering work?  Differences all understood : -Bugs in geometry files found -Occupancy cut offline but not online (if more than 25% strips of a SVX chip above treshhold) -Pedestal corrections different (still don’t know why),  After applying this to online clustering 95 % agreement!

17. Dominant systematic effects on b are beam spot size 30μm and silicon resolution 18μm added into σ 0 ( a=53GeV μm ) STT ansatz for impact parameter significance= b/db calculation: Parameterization of the error db : However the “significance” of the impact parameter heavily depends on the goodness of the fit (we may have taken the wrong hits into the fit) -> Then chi2 gets worse STT track quantities Sascha Caron page 15

18. Performance studies STT tracks Impact Parameter resolution Correlation to full D0 RECO p T (GeV) Resolution in μ m Purity Including beam spot size 30 μm and 18 μm SMT resolution STT track quantities -- performance Sascha Caron page 16

19. Compare real STT data with MC  Usual MCs (with Multiple Interaction) to clean to describe the data Sascha Caron page 17 Commissioning  tracks described by MC? Why? - Complete Silicon (small effect) effect) - Multiple Interactions better describable with data describable with data - SMT and track occupancy is luminosity dependent = max(IP/sigma(IP)) LEARN SOMETHING FOR ATLAS!!

20. Compare real STT data with MC  Usual MCs (with Multiple Interaction) to clean to describe the data Sascha Caron page 18 Commissioning  tracks described by MC? Why? - Complete Silicon (small effect) effect) - Multiple Interactions better describable with data describable with data - SMT and track occupancy is luminosity dependent  Known effect now, will be reduced with new silicon layer and modification of track selection LEARN SOMETHING FOR ATLAS!!

21. B-Identification methods Idea: Use a very fast L2 b-tagging method to increase performance for e.g. HZ->vvbb, Z->bb events to be run on L2_global processor (time constrain<20 µs)  built 1 discriminating quantity to cut on instead of cutting on maximum impact parameter significance S Compare different b-id methods or invent new ones Sascha Caron page 19

22. ELIP method Event Lifetime probability (call it ELIP)  Like the old ALEPH method 1) Derive probability p i for each good track to come from the primary vertex Significance distribution is non-gaussian -> p is derived via p (S) =∫ ∞ S pdf(x) dx using a QCD background MC -> can be done very fast via pre-defined lookup tables 2) Derive probability P for each event that all good tracks come from the vertex (or derive prob. That ∏ p i may happen: Time needed to derive P is quite significant (2 loops) Is that needed? No!!  INVENT BETTER ALGORITHMS

23. Best – algorithm MULM method Significance is heavily dependent on the goodness of the track fit Goodness the track fit given by scaled chi2 (less pt dependent) Idea: Include chi2 information in discriminator by using 2d p(S,chi2) pdfs This degrades tracks with large chi 2 while still using the full information provided by the STT. LEARN SOMETHING FOR ATLAS!! Sascha Caron page 21

24. One example MC result Z-> b bbar sample: 2 L2 jets with E_T>15 GeV required Signal efficiency much larger for MULM than for all other methods due to the inclusion of chi2 dependence This is a Monte Carlo study (LIKE AT ATLAS, you know)  BUT WE LEARNED THAT WE HAVE TO DO THIS WITH DATA LEARN SOMETHING FOR ATLAS!! CUT ON B-algorithm likelihood CUT ON SIGNIFICANCE

25. ONLINE ALGORITHM Loop over the 5 ‘good’ tracks with largest IP and derive the product : Derive probability density functions of tracks in B-events : P B and non-B events : P non-B   Store their ratio into a lookup table on the L2 global processor A fast B-id algorithm for Level 2 New Idea: Combine tracks in a fast, multivariate algorithm Probabilityratio P B /P non-B Sascha Caron page 23 P B,i / P non-B,i

26. A fast B-id algorithm for Level 2 Derive performance of the STT+B-id algorithm with D0 data Data with offlineb-tags Cut method B-id algorithm Sascha Caron page 24 Data without offline b-tag Discriminator of the B-id algorithm Background efficiency Signal efficiency Events First results of a L2 b-tag algorithm

27. Summary Next step: Build a trigger for inclusive b-jets (take Z  bb as an example) ? Sascha Caron page 25 o Silicon Track Trigger at DZero works o Further improvement by up to a factor 2 with the B-id algorithm with the B-id algorithm  Impact in next trigger strategy for difficult channels

28. Why is Z  bb and an inclusive signal of any interest? b-jet/light-jet energy scale is dominant error on top mass (0.8 GeV) (error comes from frag., colour flow, semileptonic decay fraction, etc.) Z  bb signal was proposed to reduce this error: error on b-jet/light-jet scale must be < 1% to lead to delta(M_top)<0.8 GeV Sascha Caron page 26

29. Why is an inclusive signal of any interest? Current strategy for Z  bb analysis: Require muon decay (to have event triggered) + require 2 svt tagged jets (total efficiency is about 0.5 %)  We get about 2 Z  bb events per pb with one B  muonX decay (<0.2 with 2 B  muonX decays) Problem : We have to solve C B  all * P b  all + C B  muonX * P b  muonX = P Z Needs light-to-b-scale correction C b  muon X either from data or MC? Data: We can ask 2 muon tagged b-jets and solve P b  muonX + P b  muonX = P Z How many Z  bb events do we need to measure δC b  muon X  4-8 fb -1 needed to get δC b  muon X ≈ 0.5% … and then we need C B  all ….

30. Why is Z  bb and an inclusive signal of any interest? We do not want to loose against CDF in delta(M_top)  Other strategy: Measure directly C B  all via an inclusive b-jet event sample (we can apply the same b-tagger on this sample as will be used in top analysis, e.g. NN in the future, will make things simpler) Problem: Can we be trigger Z  bb events without muons in Run2b? Do we get these events through L1 and L2?

31. L1 condition at 100 E30 A first try: Idea 2 jets with new L1 Jet algorithm (also at ATLAS) Sascha Caron page 29 L1 term L1 efficiency for Z->bb inclusive (v15ttf trigger simulation,Sabine Lammers) CJT_SW2x2(2,10.0,3.2) 41% CJT_SW2x2(2,12.0,3.2) 34.2% at >2500 Hz CJT_SW2x2(2,15.0,3.2) 25.7% at 860 Hz CJT_SW2x2(2,20.0,3.2) 16.2% at 220 Hz CJT_SW2x2(2,25.0,3.2) 7.8 % at 75 Hz CJT_SW2x2(2,25.0,2.5) 7.8+- 0.8 % at 65 Hz

32. L2 rate for L1 condition L1: CJT_SW2x2(2,25.0,2.5) L2btagLikelihood cut Sascha Caron page 30

33. Inclusive b-jet trigger possible for Run2b

34. Compare with ATLAS Sascha Caron page 31 D0 and ATLAS have 40% efficiency for background reduction of 20  D0 for events!! ATLAS for jets!! (So D0 wins 2:1) (Yes this is a rough estimate and I apologize to the ATLAS L2 btag people) LEARN SOMETHING FOR ATLAS!! ATLAS Very preliminary Taken out from a talk at 24.Okt 2005

35. How do we trigger beauty? How do we trigger beauty? Within 50-100 μs a very good construction of Silicon Improved tracks + O(1 μs) construction of a discriminator via a look-up-table b-tag algorithm  Triggering beauty works! LEARN SOMETHING FOR ATLAS!!