Bump Hunt on 2016 Data

Transcription

1 Bump Hunt on 216 Data Sebouh Paul College of William and Mary HPS Collaboration Meeting May 3, / 24

2 Outline Trident selection Effects of cuts on dataset Comparison with 215 dataset Mass resolutions: A MC. Mollers from data, MC Radiative fraction Bump hunting Background models Most significant bump found Signal yields Upper limits on coupling Blinded Unblinded (projection) Homework 2 / 24

3 Trident Event Selection Criteria Preliminary cuts GBL Tracks If > 3 shared hits, use the track with the best fit χ 2. Track-cluster match χ 2 < Clusters on opposite sides of Ecal. Pair1 trigger Accidental background reduction FEEs: p e < 1.75 GeV. ( 75% E beam ) psum < 2.76 GeV. ( 1.2 E beam ) Track fit χ 2 < 4 tcluster t track 55 ns < 4.5 ns. WAB reduction cuts Positron track has a hit in L1. Positron d < 1.1 mm Cluster time difference < 2 ns. Radiative cut: p sum > 1.4 GeV ( 61% E beam ) *(most of these cuts are based on Omar s trident event selection for 1.5 GeV, scaled up for 2.3 GeV) 3 / 24

4 Effects of the cuts on the Data Invariant Mass 3 preliminary mass_pre electron Entries pz < pz < e+7 Mean.625 Std Dev.2459 Track χ 2 < 4 7 Cluster dt preliminary cluster_dt_pre electron Entries pz < e pz < 2.76 Mean.4851 Std Dev Track χ 2 < t -t tr -55 <4.5 cl positron L1 positron d < 1. 6 t -t tr -55 <4.5 cl positron L1 positron d < cluster dt < 2 pz > cluster dt < 2 pz > Invariant Mass (GeV) Cluster dt (ns) 4 / 24

5 Invariant Mass Spectrum of Selected Events events / bin 6 All cuts (linear scale) all cuts Entries Mean.674 Std Dev events / bin All cuts (log scale) all cuts Entries Mean.674 Std Dev Invariant Mass (GeV) Invariant Mass (GeV) 5 / 24

6 Comparison between 215 and 216 datasets ) nb.5 MeV σ ( Invariant Mass (GeV) ) nb.5 MeV σ ( Invariant Mass (GeV) 6 / 24

7 Calculating Mass Resolution Tridents / A s Generated O(k) A events in MC with m A at (5,, 15, 2, 25) MeV. Applied similar cuts to MC as were applied to data Fit each spectrum to crystal ball function mass resolutions fit to 3rd order poly of mass Mollers (for corroboration): Data from upass single skim. Monte-Carlo events generated with s trigger. Cuts are mostly from SVT (two slides from now) Out-of-time background ( t track > 4 ns) subtracted from signal. fit moller candidates to a signal gaussian plus a wider background gaussian. 7 / 24

8 Mass Resolution: A Monte Carlo Arbitrary Units MC A' Mass Resolutions 1.2 σ = MeV Invariant mass (GeV) 8 / 24

9 Moller Cuts single trigger both track fit χ 2 /d.o.f. < 5 both tracks d < 1.5 mm both tracks p < 1.75 GeV track time difference < 2 ns ( 2σ t track ) p sum between 1.75 GeV and 2.6 GeV only one cluster: xcluster < 8 mm no positrons 9 / 24

10 Out-of-time Moller background subtraction Out-of time Subtraction for Mollers n events 8 in-time out-of-time background subtracted Invariant Mass (GeV) / 24

11 Mass Resolution: Mollers Data Mollers MC Mollers # of events 8 σ =.133 µ =.4768 # of events 35 3 σ =.3 µ = Invariant Mass (GeV) Invariant Mass (GeV) 11 / 24

12 Mass Resolution MC A' Data Mollers MC Mollers scaled mass resolution Invariant mass (GeV) 12 / 24

13 Radiative Fraction Purpose A cross-section is proportional to radiative cross section Necessary for calculating upper limit on A coupling ɛ 2 Procedure Monte-Carlo: tri-trig (total) and RAD (radiative) Both from MadGraph 5. Applied similar kinematic cuts to those used on data Found lower cut on p sum such that events above the cuts. Calculated ratio of cross sections σ rad cut. σ rad σtotal σ total is maximized for for events passing the 13 / 24

14 Radiative Fraction dσ/dpsum (nb/. MeV) σ of events above Psum cut (nb) Cross Sections 4 35 total h1t h Entries Mean Std Dev rad Psum (GeV) Cumulative Cross-Sections 4 h1t_cumulative Entries total Mean Std rad Dev Psum cut (GeV) nb) relative significance ( radiative fraction rad cumulative total cumulative Peak at 1.45 GeV h_cumulative Entries 353 Mean 1.61 Std Dev Psum cut (GeV) rad total Average f : 9.3% rad Psum (GeV) 14 / 24

15 Background Models and Fitting Parameters I am currently testing 3 types of background models. (x = mass of A ) Name Formula Parameters poly n a i x i a...a n i= [ N ] exp ( poly ) exp a i x i a...a n [ i= N ] exp poly e kx a i x i k, a...a n i= Also varying window-size to mass-resolution ratio f, and polynomial orders n. 15 / 24

16 Background Models Several pieces of information will go into my decision of which model/window-size to use: pull := (mean yield) - (injected signal) (sigma yield) p-value calculated for data fits for toy signals minimize median upper limit for toy distributions (while keeping all other things reasonable) *For the slides in this presentation I use: model: poly order: n = 5 window size = 19σ M * This is NOT a finalized decision. 16 / 24

17 P-values Local p-value p min (m A' =.28 = 119. MeV) A' mass hypothesis (GeV) *Look Elsewhere Effect not accounted for yet (wait for it...). 17 / 24

18 Most significant bump (.119 GeV) A RooPlot of "Invariant Mass (GeV)" Events / ( 5e-5 ) Invariant Mass (GeV) 18 / 24

19 Look Elsewhere Effect Global vs. Local p-values Local p-value p p local global =.28 = σ 2 2σ 1 1σ 1 Global p-value 19 / 24

20 Signal Yields (using % of dataset) Signal Yield Signal Yield 3 Unconstrained Upper Limit Observed Signal Yield Median Upper Limit Power-Constrained Upper Limit Invariant Mass (GeV) 2 / 24

21 Upper limit on coupling (blinded dataset) Upper Limits on Coupling Coupling 2 4 up 2 = S up /m A' f rad B/ m 2N eff α 3π 5 Unconstrained Upper Limit Median Upper Limit Power-Constrained Upper Limit Invariant Mass (GeV) 21 / 24

22 Projected Upper Limit on Coupling (full dataset) 22 / 24

23 Projected Upper Limit on Coupling (full dataset) 23 / 24

24 Homework: Figure out why the discrepency in moller resolutions between data and monte-carlo is so large: Alignment problems in data? underestimation of multiple scattering in Monte-Carlo? Improve efficiency: L1 efficiency for e in data is 84% Is problem from track fitting or SVT pulse fitting? Improvements on fitting invariant mass spectrum: Does exp poly perform better than poly? Which order polynomial? Window size? After pass1, ask ourselves: are we ready to unblind yet? if yes... We unblind We publish I graduate. 24 / 24