Tracking and Vertex reconstruction at LHCb for Run II

Transcription

1 Tracking and Vertex reconstruction at LHCb for Run II Hang Yin Central China Normal University On behalf of LHCb Collaboration The fifth Annual Conference on Large Hadron Collider Physics, Shanghai, China May 18 th, 2017

2 Outlines Introduction to LHCb detector Track and Primary vertex reconstruction Performance New developments Summary 2017/5/18 Hang Yin, Tracking and Vertex reconstruction at LHCb for Run 2 2

3 The LHCb detector LHCb detector is a general purpose single arm forward spectrometer Designed for precision measurements for heavy flavor physics Search for new physics in CP violation and rare decay of b and c hadrons Coverage: 2< η < 5 Tracking detectors Vertex Locator (VELO): silicon micro-strip Trigger Turicensis (TT): silicon micro-strip Track stations: Inner tracker: silicon strip Outer tracker: straw drift tubes Tracking Particle ID [Int. J. Mod. Phys. A 30, ] [J. Instrum. 3 (2008) S08005] 2017/5/18 Hang Yin, Tracking and Vertex reconstruction at LHCb for Run 2 3

4 LHCb in Run II LHCb moved to real time reconstruction, alignment and calibration setup in Run-II Completely change of the strategy Offline quality of event selection performed at trigger level Requirements on tracking/vertex reconstruction The reconstruction had to be made identical for online and offline Online reconstruction should be faster without performance loss 150 khz 2017/5/18 Hang Yin, Tracking and Vertex reconstruction at LHCb for Run 2 4

5 Track reconstruction at LHCb Main track types for physics analyses Long tracks: Hits in VELO, T stations (and eventually TriggerTracker). Used in majority of analyses Downstream tracks: hits in TT and T stations. Tracks from daughters of long lived particles (e.g. Λ, K s 0 ) Two phases in LHCb tracking: Track finding/ pattern recognition and track fitting (Kalman-filter) Main challenges: Fast and efficient reconstruction at low fake rate Parallelization to speed up Machine learning to reduce fake rate 2017/5/18 Hang Yin, Tracking and Vertex reconstruction at LHCb for Run 2 5

6 Primary Vertex finding Primary vertex (PV) reconstruction consists of two steps: Seeding: find PV candidates Fitting: fit each PV seed New working points: Minimum 4 tracks in PV Accept tracks within 3.5 σ of the PV Radial distance r less than 0.2 Performance w.r.t. Run 1: 70% less global false PV reconstruction Around 2-4% better global efficiency 2017/5/18 Hang Yin, Tracking and Vertex reconstruction at LHCb for Run 2 6

7 Fake track rejection Fake tracks mainly from wrong matches between VELO and T station Remaining fake tracks from Kalman-filter χ 2 /dof cut ~ 22% Improved to ~ 14 % using neutral network (NN, Type: MLP) The output is called ghost probability Timeline: Processed offline -> Used in HLT2 -> Used in HLT1 (RunI -> > 2016) 2015: speed up by factor ~ 90, combined with Kalman-filter χ 2 /dof, increase downstream track eff 2016: reduces HLT2 combinatorics by 40%; negligible efficiency loss 2017/5/18 Hang Yin, Tracking and Vertex reconstruction at LHCb for Run 2 7

8 Ghost probability performance /5/18 Hang Yin, Tracking and Vertex reconstruction at LHCb for Run 2 8

9 Downstream tracking A new optimization of the downstream tracking for 2017 The algorithm depends on T-seed tracks BDT classifier is used to reject fake T-seeds Keep the efficiency and purity of the selected signal tracks as high as possible CPU performance Bonsai BDT (bbdt) is used to speed up the calculations Discretize input feature space and for each of bin calculate classifier response Take one number from the lookup table instead of calculating response for each tree The table has a complex structure, no possibility to fit approximation function 2017/5/18 Hang Yin, Tracking and Vertex reconstruction at LHCb for Run 2 9

10 T-seed classifier performance Classifier was trained with simulation events: B J/ψK s 0 decay Performance optimized on 2016 data sample ROC area under curve (AUC) ~ 0.86 Reject ~40% of fake T-seeds 2017/5/18 Hang Yin, Tracking and Vertex reconstruction at LHCb for Run 2 10

11 Forward tracking Forward tracking algorithm: Start with seed tracking in VELO. This determines (x, y, z, x, y ), only with q/p left z z One point after the magnet is enough to determine the momentum (and the full trajectory) Project all clusters into a common plane (hough-plane), using third-order polynomial, determine coefficients iteratively Very well parallelizable Calculate hough projection for two hits in one go (SSE: 128 bit width = 2 doubles) This gains in speed as the benefit of faster calculation outweighs the cost of putting 2 doubles in a vector Gain in speed: about 40% 2017/5/18 Hang Yin, Tracking and Vertex reconstruction at LHCb for Run 2 11

12 Forward tracking Trained two neural network (MLPs) For rejection of bad 4-layer-x-clusters in recovery loop For track candidate selection after stereo fit (HLT1 and HLT2) NN response and other parameters tuned with MC and minimum bias data Results: Increased efficiency Reduced fake rate considerably Decreased speed compensated in later stages due to fake track rejection LHCb-PROC /5/18 Hang Yin, Tracking and Vertex reconstruction at LHCb for Run 2 12

13 Parallelization in track fitting Tracks found by pattern recognition are fitted with a Kalman filter Kalman math is expensive (Runge-Kutta for B-field equations, or 5 5 matrix operations) largest timing contributor in HLT1 Transport covariance matrix from state k-> k+1: Similarity transform: F C F T, with C symmetric An ideal case for SIMD: need 4 (vector-) multiplications and 3 additions. Gain in speed: Write out calculation by hand : 2 Using AVX: /5/18 Hang Yin, Tracking and Vertex reconstruction at LHCb for Run 2 13

14 Track reconstruction efficiency We studied track reconstruction efficiency for each type of tracks For the long track reconstruction efficiency: the probability that a trajectory of a charged particle that has passed through the full tracking system is reconstructed Measured with tag-and-probe method, using J/ψ μμ decays [JINST 10 (2015) P02007] One of the daughters is fully reconstructed (tag), the other only partially (probe), i.e. TT-muon track Matching the probe to a full reconstructed long track The average efficiency is above 96% 2017/5/18 Hang Yin, Tracking and Vertex reconstruction at LHCb for Run 2 14

15 Vertex and decay time resolution PV resolution: A PV with 25 tracks has resolution of 77 μm in z IP resolution: IP variable is used to reduce the contribution from prompt backgrounds. At asymptotically high p T and IPx resolution is around 13 μm Decay time resolution: the typical decay time resolution in LHCb is 45 fs for a 4 track vertex 2017/5/18 Hang Yin, Tracking and Vertex reconstruction at LHCb for Run 2 15

16 Summary The track reconstruction and primary vertex finding of LHCb performs to very high standard It gives excellent performance in terms of: Track reconstruction efficiency: > 95% Mass and momentum resolution: 0.5-1% Decay time resolution: ~ 45 fs Fake track rejection New algorithms have been developed, to make preparation for a purely software-based trigger in the coming years 2017/5/18 Hang Yin, Tracking and Vertex reconstruction at LHCb for Run 2 16

17 Backup 2017/5/18 Hang Yin, Tracking and Vertex reconstruction at LHCb for Run 2 17

18 Forward tracking Forward tracking algorithm (new in 2016) contains following steps: Search window in T station defined by VELO track estimate and minimal p T Project x-hits into reference plane (create cluster) Hough transformation Fit x-cluster and remove outlies Add and fit track with stereo hits Recovery loop in HTL2 for tracking candidates with hits in only 4-layers Trained two neural network (MLPs) For rejection of bad 4-layer-x-clusters in recovery loop For track candidate selection after stereo fit (HLT1 and HLT2) 2017/5/18 Hang Yin, Tracking and Vertex reconstruction at LHCb for Run 2 18

19 Primary Vertex finding at LHCb Seeding: The points at which a sufficient number of tracks pass close to each other Select tracks with a distance of closest approach to the base track below a threshold Calculate the point of closest approach (POCA) to the base track The average POCA is determined Set of space points become seeds for fit Fit the PV seed: using an adaptive weighted least square method A weight is assigned to a track according to the value of its χ 2 IP, and Tukey constant CT: The position of the reconstructed primary vertex is determined iteratively, by minimizing Until: The shift in the Z position of the PV is less than a threshold value Sufficient tracks are associate to the PV 2017/5/18 Hang Yin, Tracking and Vertex reconstruction at LHCb for Run 2 19