1. LAV efficiency studies with photons T. Spadaro* *Frascati National Laboratory of INFN

2. Progress in LAV-related activities: 1. Status of MC, digitization, and reconstruction code 2. Selection of tagged photon control sample 3. Open problems and to-do list Outline LAV Efficiency studies with photons23/3/20152

3. LAV Efficiency studies with photons23/3/2015 Clustering finalized: Algorithm grouping 2 or more adjacent blocks fired Algorithm grouping 2 o more blocks close in  and time (a.k.a. tracking) Cluster classification: Photon-like: >1 layer fired, > 1 block fired per layer in some layer MIP-like: > 1 layer fired, 1 block fired per layer Other: 1 layer fired, > 1 block fired Cluster properties: Centroid position, from z-coordinate and  -angle averages Centroid “weighted” position and time: use charge as weight x,y,z coordinate error matrix Status of LAV SW -- Clustering 3

4. LAV Efficiency studies with photons23/3/2015 Number of MIP’s : Shower’s : Other ~ 12500 : 3200 : 300 in a single burst (run 1520, trigger Q1 downscaled, 775-ns acquisition window) Evaluate spread of charges for the blocks grouped into a cluster: Normalized spread: NS = √( )/ = 1 + RMS(Q) / Status of LAV SW – Cluster flags 4 NS Shower topology MIP topology Data Run 1520

5. LAV Efficiency studies with photons23/3/2015 Possibly, we might reject events using topological criteria: - The “Shower” condition can be implemented in the firmware, at present for LAV12 - At L1, the same condition might be applied for all the LAV’s To do: quantify the gain in terms of background rejection at the trigger level Status of LAV SW – Cluster flags 5

6. LAV Efficiency studies with photons23/3/2015 Aims to select “1-gamma” K  pp0 sample for LAV efficiency evaluation Select “1-prong” events, i.e.: 1 positive track reconstructed from the 4 spectrometer chambers, or 3 chambers in the 013 configuration, with 105 < Z CDA < 165 m with CDA < 2 cm Photon efficiency – selection criteria 6 Z CDA (cm) CDA (cm) 105 < Z CDA < 165 m CDA (cm)

7. LAV Efficiency studies with photons23/3/2015 Aims to select “1-gamma” K  pp0 sample for LAV efficiency evaluation Select “1-prong” events, i.e.: 1 positive track reconstructed from the 4 spectrometer chambers, or 3 chambers in the 013 configuration, with 105 < Z CDA < 165 m with CDA < 2 cm with 15 < Momentum < 65 GeV with  2 < 3 with Extrapolation to CHOD within 5 cm wrt CHOD candidate Photon efficiency – selection criteria 7

8. LAV Efficiency studies with photons23/3/2015 Aims to select “1-gamma” K  pp0 sample for LAV efficiency evaluation Select “1-prong” events, i.e.: 1 positive track reconstructed from the 4 spectrometer chambers, or 3 chambers in the 013 configuration, with 105 < Z CDA < 165 m with CDA < 2 cm with 15 < Momentum < 65 GeV with  2 < 3 with CHOD extrapolation within 5 cm wrt x,y of CHOD candidate in geometrical association with a LKr cluster, within 3 cm Photon efficiency – selection criteria 8

9. LAV Efficiency studies with photons23/3/2015 Kinematics of the “1-prong” events, select  0 cutting at:  –  < M miss <  + 2 , i.e., 0.01 < M miss < 0.025 GeV 2 For data (run 1520):  = 0.015 GeV 2 and  = 0.005 GeV 2 Compare with MC:  = 0.014 GeV 2,  = 0.004 GeV 2 Photon efficiency – selection criteria 9 run 1520 – 750 bursts

10. LAV Efficiency studies with photons23/3/2015 Select a tagging photon from its LKr cluster: E > 3 GeV (response calibration from Giuseppe’s routines) Distance D Trk from the extrapolated track > 3 cm Distance from nearest deadcell > 2 cm Photon efficiency – selection criteria 10 run 1520 – 750 bursts Cluster distance wrt CHOD candidate (cm) Cluster energy (GeV)

11. LAV Efficiency studies with photons23/3/2015 Close the kinematics, using photon from the LKr + reconstructed p+ evaluate E miss – P miss = E(K) – E(  + ) - E(  ) - |P(K) – P(  + ) – p(  )| Photon efficiency – selection criteria 11 run 1520 – 750 bursts [P(K)-P(  + )] 2 (GeV 2 ) E miss – P miss (GeV)

12. LAV Efficiency studies with photons23/3/2015 Close the kinematics, using photon from the LKr + reconstructed p+ evaluate E miss – P miss = E(K) – E(  + ) - E(  ) - |P(K) – P(  + ) – p(  )| Photon efficiency – selection criteria 12 MC 200 k  0 events [P(K)-P(  + )] 2 (GeV 2 ) E miss – P miss (GeV)

13. LAV Efficiency studies with photons23/3/2015 Extrapolate missing photon to the calorimeters Photon efficiency – towards matching 13 calorimeter ID Missing photon energy (GeV) MC 200 k  0 events run 1520 – 750 bursts IRC LKR SAC

14. LAV Efficiency studies with photons23/3/2015 Study resolution pointer, compare expected azimuth (  exp ) with that of single LAV blocks firing (  fnd ) in time (5 ns cut) Photon efficiency – towards matching 14  fnd –  exp (rad)  ) = 130 mrad MC 200 k  0 events run 1520 – 750 bursts

15. LAV Efficiency studies with photons23/3/2015 Pointer resolution not enough for an efficient tagging of LAV blocks: using nominal K momentum harmful, particularly at low energy Photon efficiency – towards matching 15 det fnd – det exp (a.u.)  fnd –  exp (rad) MC 200 k  0 events run 1520 – 750 bursts

16. LAV Efficiency studies with photons23/3/2015 In several % of cases, 2 nd photon mis-tagged to LAV, while it was hitting LKr Use LKr as veto: ask no* LKr cluster < 3 GeV, except that from pion track Match LAV activity in time (3-sigma time cut): low threshold = 98.8% high threshold = 98.2% LAV cluster (2 or more blocks) = 94.6% Photon efficiency – towards matching 16 *here, no cut on dead cell closest distance... otherwise get 1.5% bias on efficiency estimate) run 1520 – 750 bursts Efficiency

17. LAV Efficiency studies with photons23/3/2015 In several % of cases, 2 nd photon mis-tagged to LAV, while it was hitting LKr Use LKr as veto: ask no LKr cluster < 3 GeV, except that from pion track Match LAV activity in time (3-sigma time cut): low threshold = 99.4% high threshold = 99.3% LAV cluster (2 or more blocks) = 95.0% Photon efficiency – towards matching 17 MC 200 k  0 events Efficiency

18. LAV Efficiency studies with photons23/3/2015 Obviously, cluster requirement does not allow full efficiency Photon efficiency – towards matching 18 MC 200 k  0 events Expected photon energy (GeV) Number of blocks found Expected photon energy (GeV) Number of blocks found run 1520 – 750 bursts

19. LAV Efficiency studies with photons23/3/2015 MC analysis of the inefficiency, 0.6%, corresponding to 61/10329 evts Compare expected calorimeter ID with True calorimeter ID: 41 evts with no extrapolation ~ 140 MeV 6 evts to LKr (no LKr cluster found) E from 2 to 5 GeV 1 evt with no photon 13 events remaining a fraction due to conversions in straw 1 and 2 Residual LAV inefficiency in MC: 0.1% Photon efficiency – towards matching 19 True calorimeter ID Expected calorimeter ID MC  0 events

20. LAV Efficiency studies with photons23/3/2015 Too low or too high polar angle of emission might induce inefficiency from pointer resolution tails Photon efficiency – towards matching 20 Expected photon energy (GeV) Expected photon polar angle (rad) MC  0 events Data run 1520 Expected photon polar angle (rad) Expected photon energy (GeV)

21. LAV Efficiency studies with photons23/3/2015 Efficiency depends indeed on photon emission angle, both in MC... Photon efficiency – towards matching 21 Expected photon energy (GeV) MC pp0 Expected photon polar angle (rad): 24 = 99.7% 15 = 99.4% 12 = 98.2% Efficiency

22. LAV Efficiency studies with photons23/3/2015... and in data: Photon efficiency – towards matching 22 Expected photon energy (GeV) Data run 1520 Expected photon polar angle (rad): 24 = 99.2% 15 = 98.9% 12 = 97.5% Efficiency

23. LAV Efficiency studies with photons23/3/2015 Conclusions and to-do list 23 Clustering algorithm providing useful information for both data analysis and trigger purposes Photon efficiency studies in progress: one-photon selection, uses spectrometer, CHOD, and LKr information K   0 MC reproduces main properties of selected sample Pointer for photon direction with resolution insufficient for pointing to a single block (even a single station, indeed) Using LKr as a veto for additional photons helps in reducing bias MC predicts apparent inefficiency a factor of 5 larger than that intrinsic to LAV To do: Kinematic fitting, to reduce resolution