1. Taikan Suehara, ILC-Asia physics meeting, 2009/06/13 page 1 Tau-pair analysis for LoI+ Taikan Suehara ICEPP, The Univ. of Tokyo

2. Taikan Suehara, ILC-Asia physics meeting, 2009/06/13 page 2 [Observables] P(e - )=80%, P(e + )=30%, 500 fb -1 σ, A FB (bg suppression) Polarization P(  ) ↑Decay angle determination Tau-pair process σ=2600 fb -1 (e - L e + R ) σ=2000 fb -1 (e - R e + L ) radiative events: ~70% Difficulty on decay analysis

3. Taikan Suehara, ILC-Asia physics meeting, 2009/06/13 page 3 Looser tau-selection cuts – to improve statistical error (compatible with SiD). More background of Bhabha and  Better decay-mode selection by a neural network ‘Optimal observable’ for polarization measurementProgress

4. Taikan Suehara, ILC-Asia physics meeting, 2009/06/13 page 4 SM background of the mass production –2-photon and Bhabha have low statistics. Bhabha – re-preselection –Compatible with looser cut |cos(q)| < 0.95 |cos(q)|<0.96, opening angle < 15 deg ~200k events for 1 fb-1 2 photons – tautau –Preselection cuts: Opening angle 30 GeV –~150k events for about 10 fb-1 Background events

5. Taikan Suehara, ILC-Asia physics meeting, 2009/06/13 page 5 Signal increase: ~20% Evis cut changed: 40 to 70 GeV –Background level: almost the same Results are still worse than SiD about 20%... –Might be difference on tau-clustering: they accept neutral clusters Tau selection cuts

6. Taikan Suehara, ILC-Asia physics meeting, 2009/06/13 page 6 Need to separate leptonic, pinu, rhonu. (Also a1nu if possible) Neural net tried –Variables (9 params) E calo /E track (muon ID) E ECAL /(E ECAL + E HCAL ) (electron ID) E charged, E neutral,E n3 (Third-largest photon energy),N n M all, M n w/neutral hadrons, M n wo/neutral hadrons 18-10 hidden neurons, 5 output neurons –Selected among 16-10, 16, 10-5, 10 (before adding M n w/n) –Double layers give much better results –1000 epochs, a half of tau-pair (250000 events): ~5-10 hours Mode separation – 1 prong

7. Taikan Suehara, ILC-Asia physics meeting, 2009/06/13 page 7 Result of mode separation – 1p Better than SiD! SiD ILD

8. Taikan Suehara, ILC-Asia physics meeting, 2009/06/13 page 8 Need to separate a1nu. Neural net tried –Variables (8 params) E calo /E track (muon ID) E ECAL /(E ECAL + E HCAL ) (electron ID) E charged, E neutral Number of neutral particles Invariant mass of all visible decay daughters Invariant mass of charged particles Invariant mass of neutral particles 10 hidden neurons, 1 output neurons, single layer –Not optimized… Mode separation – 3 prong

9. Taikan Suehara, ILC-Asia physics meeting, 2009/06/13 page 9 Result of mode separation – 3p ILD SiD Need to improve?

10. Taikan Suehara, ILC-Asia physics meeting, 2009/06/13 page 10 Rho optimal observable

11. Taikan Suehara, ILC-Asia physics meeting, 2009/06/13 page 11 ω = (P eL (ω) - P eR (ω)) / (P eL (ω) + P eR (ω)) No P dependence at ω=0, L(R) only at ω=±1 Omega distributions Electron channelMuon channel Pion channelRhonu channel

12. Taikan Suehara, ILC-Asia physics meeting, 2009/06/13 page 12 P(e L ) = -0.591 ± 0.0067 P(e R ) = 0.502 ± 0.0076 (a1 not included) Polarization by ω

13. Taikan Suehara, ILC-Asia physics meeting, 2009/06/13 page 13 Tau selection – slightly worse than SiD NN tuning for 3-prong events Polarization value is not consistent with MC distribution (Measured: ~10% lower) –Check generator distribution –Identify experimental effects a1 (very complicated formula (wo/tau dir)) –Tau direction can be used for a1 But need to calculate ω by ourselves Paper Issues & prospects