Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection December 11, 2006 Track Selection for T-Alignment studies Louis Nicolas EPFL Monday Seminar December 11, 2006; LPHE T-station alignment framework Effects of misalignments Solving the alignment problem Track selection and monitoring tool for the T-station alignment Some variables and results Conclusion

Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 2/23 December 11, 2006 LHCb detector The whole detector will have to be aligned I concentrate on the Inner Tracker for now The Outer Tracker should follow shortly

Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 3/23 December 11, 2006 Different types of misalignments Aligned detector Internal misalignments Global shifts or rotations Global distortions Other possible effects: scaling,...

Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 4/23 December 11, 2006 Alignment framework Upstream processing (tracking algs, etc…) AlignTools Align Data objects Align Algorithms Stores/DBs Transient event store Transient detector store Conditions DB Solving Track Selection Track Model Update Book-keeping of alignable geometry etc… Tracking info Geometry info iterate LHCb Brunel environment Job done in collaboration between LPHE, CERN, NIKHEF, Zurich and Heidelberg © A. 1 st LHC Detector Alignment Workshop, CERN, 09.06

Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 5/23 December 11, 2006 Use of residuals to recover the motion and rotation of the detection planes. For N planes, 6N parameters to determine Figure of merit:  2 =  (r i /  i ) 2 How to spot a misalignment? © A. Tracking & Alignment Workshop, Zurich, Misalignments change the tracks' path and hence the vertices. The vertex position is of utmost importance for the precise measurements of the Standard Model physics parameters.

Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 6/23 December 11, 2006 Several solving methods are combined Global/Collective modes not constrained: singularities appear in the solving procedure These singularities must be avoided (use constraints on alignment params) Global fit: correlate different planes Alignment problem M  a = b One method: Diagonalize matrix: With M = U T D U ==>  a = U T D -1 U b Hierarchical alignment: Stations Boxes Layers Ladders Solving the mathematical problem © A. Tracking & Alignment Workshop, Zurich, 07.06

Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 7/23 December 11, 2006 σ ~ 0.51%σ ~ 0.90% Bias © J. Tracking & Alignment Workshop, Lausanne, Example of the effect of misalignments on Physics σ ~ 23 MeV σ ~ 41 MeV Worst Misalignment: T1 and T3 moved in Opposite directions in all the 6 d.o.f. of 0.5 mm for translations and 0.5 mrad for rotations

Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 8/23 December 11, 2006 Motivations of the track selection and monitoring tool Physics motivations: Select proper tracks for alignment Reject ghost tracks Select isolated tracks Provide monitoring variables for the alignment e.g. ladder and box overlaps (see next slides) Technical motivation: Set a tool usable as pre-filter for tracks used for alignment studies Steps to perform : Save a standard monitoring NTuple (used later as monitoring tool) Offline analysis of this NTuple Define a set of cuts in order to achieve the physics motivations Use this set of cuts in the track selection tool Configuration: Code developed in LHCb reconstruction software packages Analysis done using ~25k B --> J/  (-->  ) K s events

Quality selection: residuals and pull Pull of the residual for non-ghost tracks: Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 9/23 December 11, 2006 Plot of hits residuals:  not measurable (1.58 for black curve above) The large peak is not quite understood yet

Quality selection: pull of momentum Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 10/23 December 11, 2006 Fraction of non-ghost tracks vs. track momentum:  = 1.14 Understood by the tracking experts Pull of the momentum for non-ghost tracks:

Quality selection: Track – Muon matching We want to match track with muons: In case of no B field, we know that the particles reaching the muon chambers have a momentum of p > 5 GeV Loop on all muon tracks Extrapolate track and muon track to a defined z (set as property) Calculate a chi2 of the position of these states: (X_track-X_muon)²   X_track   X_muon ) + (Y_track- Y_muon)²   Y_track   Y_muon ) If chi2 < C (C is a property) set flag to 1 ==> 1.55 % of all tracks matched to muons for B->J/psi(mu mu) Ks data Warning: This is not a matching efficiency! Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 11/23 December 11, 2006

Fraction of non-ghost tracks vs. number of holes (layers with no hits): (we want the best possible tracks for alignment, i.e. only tracks with 0 holes will be selected) Quality selection: number of holes Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 12/23 December 11, 2006

Quality selection: number of shared hits Fraction of non-ghost tracks vs. number of shared hits: (we want the best possible tracks for alignment, i.e. only tracks with 0 shared hits will be selected) Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 13/23 December 11, 2006

Quality selection: isolated tracks We need isolated tracks: Reject tracks with shared hits Reject tracks with too many neighbouring hits: For each track, look at each layer if other hits found within n strips on each side of the track (n is a property): In case one hit is found in layer, loop on all clusters and compare layer and strip number In case no hit is found in layer, extrapolate track to z of layer with hole, convert extrapolated state position to local position and then to strip number and compare to all clusters If more than N hits found, reject the track (N will be a property) Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 14/23 December 11, 2006

Quality selection: isolated tracks Plot of number of neighbouring hits found inside 2 strips around track (just showing that I find neighbouring hits...): Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 15/23 December 11, 2006

Quality selection: track  2 Probability Fraction of non-ghost tracks vs.  2 Probability: It looks like nothing can be gained from here... except if you look at the ghost rate as a function of the cut on this variable: Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 16/23 December 11, 2006

Only a few preliminary cuts could be found by looking at the plots: Number of holes== 0 (we want the best possible tracks) Number of shared hits== 0 (we want isolated tracks) Probability of chi2> 30 % (by looking at significance optimisation) No cut on momentum in case we run with magnet off With these three cuts: ==> Isolated tracks (no shared hits, cut on NNeighbouringHits doesn't change much) ==> Ghost rate reduced from % to 0.03 % ==> Selection of non-ghost tracks: efficiency = 3.25 % ==> Rejection of ghost tracks:efficiency = % Alignment will guide us for optimising efficiency vs. ghost rate e.g. would 1 % ghost rate screw up the alignment? Need simulated misaligned data to check the efficiency of the selection. Quality selection: preliminary cuts and results Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 17/23 December 11, 2006

Monitoring tool: Ladder overlaps What are ladder overlap residuals? For each IT layer, we have an overlap as follows (old drawing, sorry): Hence, for each layer, we can have up to two hits. Definition: if track has two hits in the same layer by convention: overlap residual = res(Hit higher Z) – res(Hit lower Z) What are they useful for? With the distribution of ladder overlap residuals, we can constrain and monitor the relative motion of ladders contained in a same layer. Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 18/23 December 11, 2006

Monitoring tool: ladder overlap residuals Plot of ladder overlap residuals: Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 19/23 December 11, 2006

Monitoring tool: box overlaps What are box overlap residuals? Same idea as for ladder overlaps: we want to constrain and monitor the relative motion of two boxes of the same station If hits are found in two contiguous boxes and at least 4 hits in one of the boxes: then: “Fit” piece of track in one box (we don’t want to depend on other stations) Extrapolate to other box Compare “piece of track” to hits of other box Define overlap residual as distance of closest approach Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 20/23 December 11, 2006

Monitoring tool: box overlaps  2 “fit” of the hits in the box with most hits: x track = az hit + b y track = cz hit + d ==> where y global is the y coordinate of the track state at hit z and sigma yglobal its error. Ad-hoc model for the moment to test the principle of the method. More accurate formulation to follow (e.g. using parabolic model of “track”) Minimise this  2 and get “track” parameters (a, b, c and d) Extrapolate “piece of track” to z position of hits in other box Define box overlap residual as distance between hit and fitted piece of track (several residuals per box overlap) Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 21/23 December 11, 2006

Plot of box overlap residuals: Strange double peak distribution probably comes from linear approximation to compute residual. To be confirmed. Sigma is far too large. Refinement to come... Monitoring tool: box overlap residuals Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 22/23 December 11, 2006

Conclusion The whole detector needs to be aligned in order to get good physics measurements. The VeLo already has a working software. T-station alignment software is on the way to completion: Solving tools are ready, track selection is close to be finished, global framework is nearly ready,... A track selection and monitoring tool has been set up: Cuts are there to reject the ghosts and select the good tracks. An NTuple and histograms are implemented for monitoring purposes. We're waiting for misaligned data to check if everything works properly. Louis Nicolas – LPHE-EPFL T-Alignment: Track Selection 23/23 December 11, 2006