1. 1 HLT (confirmation, generic)  Idea Reconstruct only a fraction tracks In hand:  better PT estimation  signal (secondary) vertices  Data TDR DaVinci v9r3  HLT Velo + VeloTT  TrgForward tracking  Offline vertices HLT (confirmation or generic) [1st attempt] Jose A. Hernando, Frederic Teubert (03/4/04) work in progress... This is a status report

2. 2 HLT (confirmation, generic)  Previous results Selection of candidates  To get 2 B-tracks reconstructed abs(IP) [0.15,3] mm Secondary vertices (2 tracks)  95%,90% of the times the track with highest pt comes from the B  Use the 4 th first Pt candidates is enough  Create a 2 track vertex using this order of track by pt: [0,1][0,2][0,3][1,2][1,3][2,3] efficiency vs rejection  95% eff (Bpipi,BsDsK) 10 KHz  L1confirmation : log(pt(0))+log(pt(1)  Combine with z-distance between 2 vertices  Mass related with l1confirmation  Plans: What about other channels:  Neutrals, electrons?, muons!  We consider the bonus from L1!  Working on it…. Multi primary vertex  We discover they were a problem!  Many 2 nd verteces (signal) are from track of a second primary vertex  We made a treatment of the multi primary vertex Combining the variables  Cut (l1conf,Delta-Z) by “hand” to get eff vs rejettion  Can we combined in an “automatic” way to cut only in one variables? What is the b,c content?

3. 3 Efficiency vs retention Z distance vs log(pt1*pt2)Eff vs retention KHz B0->  ; Bs->DsK (black) B0->  ; (white) Bs->DsK what did it happen to the bonus (10 KHz)?: we are checking it

4. 4 Dealing with multi primary verteces  Multiple primary vertices After L1-conf the sample has a good number of 2 primary vertices! We can see it in the z-distance between the primary and the secondary  Secondary vertex made with tracks from the second primary vertex  Method to deal with multi primary vertices 1.Use off-line primary vertices 2.Associate each track to the primary vertex with smaller abs(IP) 3.Compute the # of candidates in abs(IP) window [0.15,3] mm for each vertex 4.Consider the primary the one with more candidates 5.Consider only the candidates of that primary vertex

5. 5 Combining variables: flattening Flatting the mim bias Xflat = F(x) log(pt1*pt2) Z-distance (SV-PV)

6. 6 The phi-angle variable: flattening cos(phi)  Phi angle: Angle in the transverse plane between:  The separation of the secondary-primary vertex  The total momentum (of the 2 tracks) in the secondary vertex  About the variables: L1-confirmation + bonus if the most powerful Z-Distance is the less powerful (still some long tail…) Phi is working good They are “quite” independent  How to combine them into one variable? Xflat = F(x) Flatting the mim bias

7. 7 Combining flat variables  Combining flat variables: Easy to see the correlation, easy to define a cut  Mim.bias(background) space “flat” if they are independent  To make a one variable only Define the distance in that space to a point (1.,1.) dis = sqrt(x^2+y^2) And flat this variable again  Easy to add more variables Improvement: weights for variables (x/w), and center the signal (x0,y0)

8. 8 L2 variable: efficiency, retention, c and b content  L2 trigger (“improved l1confirmation”) We need to take into account the # events with no secondary vertex (mim.bias ~15%) We maybe can go up to 8 KHz, with efficiency >90% In the “by hand” cut we will be able to get a little more efficiency  Retune and weight more l1confirmation  Generic algorithm There is a region where the l2 variable has a large (50,70) % of (b,b+c) content  How much on KHz is that?, should we keep all this events?, how much time we save?

9. 9 Efficiency vs retetion  L2 efficiency vs rejection 8KHz with 92% efficiency (Bpipi,BsDsK), 5KHz with 90,~85% L1- more weight? Delta-Z benefices BsDsK and penalizes Bpipi, Phi benefices again Bpipi

10. 10 Conclusions and plans  Conclusions Treatement of the multi primary vertices, seems ok A method to combine different variables  Flat variables  Distance in the flat-space Preliminary: 8 KHz, 92% eff  But… the bonus Prelimainary:  A fraction of mim.bias with 50%(70%) of b(b+c) content?  Note: We use TDR data, DaVinci v9r3 We use tracking TrgForward (HLT) We use Offline vertices!  Plans More statistics The other channels (neutrals,e,mus) How many c/b we have after?  Do we have a generic algorithm? Revisit-tune the flat variable method  To Play We do the full analysis in python We have a data in python We have developed a python module  (HIPYS similar to PAW) It is really SIMPLE and FAST to work with python!!!!

11. 11 HIPYS  Python is a great language… to make analysis Python (clay), C++ (stone)  Python is: An scrip and a OO lenguage Dynamically typed Interpreted (interactive) Has heterogeneous container Has solved the persistency:  But.. The I/O is slow  So intuitive to use  To make the flat variables: No way I do in PAW Maybe… in C++ But it is so…easy in Python If only I will have some kind of “PAW”  I do it! PUB Histo1d,2d,xyNtuple Manager (~HBOOK) Plotter (~PAW) GNUplot Ranges(~cuts)  I have a kind of PAW (HIPYS) in PYTHON Create, display, 1d,2d,scatter histograms I can create and display histogram form ntuples I can apply cuts in ntuples variables I can manipulate data ntuples:  Slice in rows, columns, add more columns  Make a histogram from a column I can manipulate data histograms:  Get the range (different size of bins) and contents DATA, ANALYSIS, “KUMACS” AND… I PLAY INTERACTIVE… ALL IN PYTHON …