Preliminary Results on n e / n t selection at DUNE FD. CP violation & n t physics perspectives. Thomas Kosc (kosc@ipnl.in2p3.fr) PhD student at IPNL (France) Supervisor : Dario AUTIERO Thesis : Development of the CP violation search analysis in DUNE and assessment of the related systematics. � 1 Thomas Kosc / IPNL (Lyon, France)
A priori discussion • Some work on tau selection in previous meetings: Herilala/Miriama thesis. ~80% of n e CC rejection and ~60% of n t CC ( t —>e - ) kept, based on GENIE (version ?) and the ROOT Toolkit for Multivariate Analysis. n t appereance at NOMAD (R. Petti). Likelihood approach, up to ~1% background contamination ? • Elaborate an analysis of n t detection, based on simulations (GENIE v3.0.2). Outlook of CP violation analysis ( n t background rejection) and n t physics ( n t selection). • This talk focuses only on CC n t with t —>e - +2 n (easy channel and background of n e CC, but only ~17% of total branching ratio). Other channels require further analysis. The n t come from the oscillation of n µ . • n e are restricted to beam contamination at production point (without oscillation). n e from oscillation will depend on PMNS parameters, included later. • Rely on GENIE only. t decay ? n t cross section ? Comparison with other generators ? � 2 Thomas Kosc / IPNL (Lyon, France)
Tools & Method • DUNE FD setup is implemented only via the `lux. See the `ile I used: histos_g4lbne_v3r5p4_QGSP_BERT_OptimizedEngineeredNov2017_neutrino_LBNEFD_fastmc.root . Results are `lux dependent, any comparison should be careful with using the same `lux. • Neutrino cross sections, GENIE pre-computed `ile NULL_G1802a00000-k500-e1000 (see “Associated data release” on http://www.genie-mc.org/ ). • Two `iles generated and used for the time being: oscillated n µ —> n t and unoscillated n e contamination. Both at FD. FSI_Emeas nue_flux 15 10 − nue_flux nue_flux × Entries Entries 346856 346856 0.35 45 Mean Mean 4.463 4.463 Std Dev Std Dev 4.639 4.639 n e 40 35 / POT ν 30 0.3 e 2 s / m Two `iles generated. 25 e ν Unosc 20 ν 15 0.25 τ Right : distributions 10 CC Total cross-section on Argon ν 5 µ 1.2 ) -1 0 n e/ n t .GeV of total kinetic 0 5 10 15 20 25 30 0.2 Energy (GeV) ν e 2 1 cm FLUX at -38 /E (10 energy in the `inal 0.8 σ 0.15 CROSS SECTION on Ar 0.6 FD state interaction 0.4 0.1 nutau_fluxosc 12 10 − nutau_fluxosc nutau_fluxosc × 0.2 (neutrons removed). Entries Entries 5603837 5603837 2.622 2.622 5 Mean Mean Std Dev Std Dev 1.237 1.237 0 0 1 2 3 4 5 6 7 8 9 / POT energy E (GeV) 0.05 ν µ 4 n t 2 s / GeV / m 3 0 τ ν Oscillated 0 5 10 15 20 25 30 2 Energy (GeV) 1 0 0 5 10 15 20 25 30 Energy (GeV) • No reconstruction effects (smearing) implemented yet. Should come in a near future. • Easier to start with n e from beam contamination rather than oscillated n µ —> n e � 3 Thomas Kosc / IPNL (Lyon, France)
Kinematics (neutrons removed !) • Basic idea: distinguish CC n e from CC n t with t - —> e - + n e + n t using kinematical criteria (NOMAD). • Transverse plane kinematics (TPK) (remove uncertainties due to incoming neutrino momentum). n e CC in TPK n t CC in TPK with t - —>e - • Kinetic variables at play: Electron n e - p e- p e- f em K e - = kinetic energy of the electron. Missing f he f em f he momentum ( tr ) , , = tranverse ( tr ) p e − ( tr ) p had p miss p miss momenta. f hm Missing Jet Angles between transverse f hm Jet momentum p had momentum f he , f hm , f em . p had p miss ( tr ) − p had ( tr ) p asym = p e − ( tr ) + p had ( tr ) p e − Small missing momentum, so f he close to 180°. Unseen neutrinos increase the missing m o m e n t u m a n d ch a n ge th e a n g l e s distributions. � 4 Thomas Kosc / IPNL (Lyon, France)
Distributions ( tr ) ( tr ) p e − ( tr ) p had K e- p miss ElectronEnergy MissingP ElectronTrMomentum HadronicTrMomentum 0.35 0.35 0.35 0.35 ν ν ν ν 0.3 0.3 0.3 0.3 e e e e ν ν ν ν τ τ τ τ 0.25 0.25 0.25 0.25 0.2 0.2 0.2 0.2 0.15 0.15 0.15 0.15 0.1 0.1 0.1 0.1 0.05 0.05 0.05 0.05 0 0 0 0 0 5 10 15 20 25 30 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 0 1 2 3 4 5 6 7 8 2 2 2 Energy (Gev) Momentum (GeV/c ) Momentum (GeV/c ) Momentum (GeV/c ) ( tr ) − p had ( tr ) p asym = p e − f he f em f hm ( tr ) + p had ( tr ) p e − PtrAsymmetric tr_ele-had tr_ele-miss tr_had-miss 0.35 0.35 0.35 0.35 ν ν ν ν 0.3 0.3 0.3 0.3 e e e e ν ν ν ν 0.25 τ 0.25 τ 0.25 τ 0.25 τ 0.2 0.2 0.2 0.2 0.15 0.15 0.15 0.15 0.1 0.1 0.1 0.1 0.05 0.05 0.05 0.05 0 0 0 0 0 20 40 60 80 100 120 140 160 180 0 20 40 60 80 100 120 140 160 180 0 20 40 60 80 100 120 140 160 180 3 2 1 0 1 2 3 − − − angle (deg) angle (deg) angle (deg) � 5
Correlations, two examples. ⎡ ⎤ φ he , p miss ⎡ ⎤ ( tr ) ( tr ) , p miss ( tr ) p had ⎣ ⎦ ⎣ ⎦ (tr) (tr) (tr) P vs P vs P φ had (tr) (tr) (tr) (tr) miss (tr) (tr) P P vs P vs P miss vs P vs P φ φ he 5 had had miss miss (GeV) 180 miss miss (deg) he he Entries Entries 7593 7593 Entries Entries 7593 7593 Mean x Mean x 0.4286 0.4286 Mean x Mean x 0.432 0.432 4.5 160 Mean y Mean y 0.7196 0.7196 he had Mean y Mean y (tr) 146.1 146.1 φ P Std Dev x Std Dev x 0.34 0.34 Std Dev x 0.3404 Std Dev x 0.3404 4 Std Dev y Std Dev y 0.4811 0.4811 140 Std Dev y Std Dev y 38.35 38.35 3.5 120 3 n e CC 100 2.5 80 2 60 1.5 40 1 0.5 20 0 0 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 (tr) (tr) P (GeV) P (GeV) miss miss (tr) (tr) (tr) vs P φ P vs P (tr) (tr) miss vs P vs P φ φ (tr) (tr) (tr) (tr) had miss he P P vs P vs P 180 miss miss (deg) he he 5 had had miss miss (GeV) 924 924 Entries Entries Entries Entries 924 924 0.6628 0.6628 Mean x Mean x Mean x Mean x 0.6605 0.6605 160 4.5 he Mean y Mean y 104 104 Mean y Mean y 0.5329 0.5329 had φ (tr) 0.3871 0.3871 Std Dev x Std Dev x P Std Dev x 0.3869 Std Dev x 0.3869 4 140 Std Dev y Std Dev y 50.87 50.87 Std Dev y 0.4076 Std Dev y 0.4076 3.5 120 3 n t CC 100 2.5 80 2 60 1.5 40 1 20 0.5 0 0 0 1 2 3 4 5 6 7 8 0 1 2 3 4 5 6 7 8 (tr) (tr) P (GeV) P (GeV) miss miss � 6
Likelihood analysis • Given an event ( n e or n t ) with a set of kinematic variables, we ⎛ ⎞ L = log L S compute the likelihood ratio L . ⎜ ⎟ ⎝ ⎠ L B L S (resp. L B ) is the probability that a given kinematic variable (or a correlation of several of them) occurs for the signal (resp. background). L S for n t Convention: n t CC = SIGNAL; n e CC (beam) = BACKGROUND. L B for n e • Comparison of the signal/background likelihood distribution informs about the separability power of the kinematic variable (or of their correlation). � 7
Some likelihood plots ( ) ( ) ⎡ ⎤ φ he , p miss ( tr ) L ⎣ ⎦ ⎡ ⎤ ( tr ) , p miss ( tr ) L p had ⎣ ⎦ Sig2dLH_Pthad vs Pmiss Bck2dLH_Phihe vs Pmiss • The cut at 0 is shown as a 0.3 0.3 Cut at 0: Cut at 0: 0.699 sig kept 0.25 0.808 sig kept 0.25 matter of indication. We use 0.250 bck cont. 0.351 bck cont. 0.2 0.2 integral likelihood 0.15 0.15 distributions for more 0.1 0.1 quantitative results. 0.05 0.05 0 0 4 3 2 1 0 1 2 3 4 4 3 2 1 0 1 2 3 4 − − − − − − − − Log(Ls/Lb) Log(Ls/Lb) ( ) ( ) ⎡ φ he , p miss ⎤ ⎡ ⎤ ⎦ . φ he ( tr ) ( tr ) , p miss ( tr ) L ⎦ . K e − L p had ⎣ ⎣ WorkSigLHPhihe vs Pmiss x kele WorkSigLHPthad vs Pmiss x Phihe • Using different combinations 0.22 0.2 0.2 0.18 Cut at 0: 0.18 of the 8 variables, hard to get Cut at 0: 0.16 0.764 sig kept 0.16 0.748 sig kept 0.14 0.305 bck cont. much improvement. Cuts at 0 0.276 bck cont. 0.14 0.12 0.12 reach ~[75;80]% of n t 0.1 0.1 0.08 0.08 selection and some [25;30]% 0.06 0.06 0.04 of n e contamination. 0.04 0.02 0.02 0 0 4 3 2 1 0 1 2 3 4 4 3 2 1 0 1 2 3 4 − − − − − − − − Log(Ls/Lb) Log(Ls/Lb) � 8
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