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Analysis with nanoDSTs

Analysis with nanoDSTs. Making nanoDSTs Scheme Last improvements Analyzing nanoDSTs Current procedure Analysis Framework : MWGana A new macro for background estimation. Analysis with nanoDSTs. NanoDST scheme 2 Ttrees : T1  run information : 1 entry per selected run

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Analysis with nanoDSTs

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  1. Analysis with nanoDSTs • Making nanoDSTs • Scheme • Last improvements • Analyzing nanoDSTs • Current procedure • Analysis Framework : MWGana • A new macro for background estimation F. Fleuret - LLR

  2. Analysis with nanoDSTs • NanoDST scheme • 2 Ttrees : • T1  run information : 1 entry per selected run • T  event information : 1 entry per selected event • TrigLvl1 node • PHGlobal node : Zvertex, BBC, ZDC, run number, … • PHMuoTracks node : Muon (and dimuon) tracks information • Guideline : Keep It Small & Simple • Add variables and information within one of the existing nodes • Don’t add node unless it’s necessary F. Fleuret - LLR

  3. Analysis with nanoDSTs • Last improvements • Add a branch for mutoo (Chun & Sean) • Changes for fun4all (Vi-Nham & Fred) • Add Fcal (MVD?) info (Jane) • Add dMuiPseudoTrigger info (Hiroki) • Memory leak investigation (Jason) For fun4all For mutoo Old framework • Fun4all troubles : • lost cuts on tracks • no output track’s cut information New framework F. Fleuret - LLR

  4. Analysis with nanoDSTs • Current analysis procedure • Produce nanoDSTs : DSTs nanoDSTs (nanoDSTs size / DSTs size < 0.2 %) • Produce ntuple with analyze.C : • A compiled macro. • Few minutes to go thru all nanoDSTs. • Output = a root ntuple. • Produce plots with plots.C : • A root macro. • Less than a minute to go thru data. • No specific library to be loaded. F. Fleuret - LLR

  5. MWGana Analyze/ analyze.C makefile MWGana/ Dimuons/ dimuons.h background.h Singlemuons/ Plots/ plots.C • Analysis framework proposal • « CVSify » the analysis code • analyze.C • Access the nanoDSTs • Create output ntuples • Provide various information • dimuons.h(TChain* T, TNtuple* ntuple) • Apply event selection • Apply particle selection • Fill dimuons ntuple • background.h(TChain* T, TNtuple* ntuple) • Build background spectra • Fill background ntuple F. Fleuret - LLR

  6. Background estimation • Study of the Background coming from p‘s (main source) and K’s decays. • So far : Nsignal = N+- - (N++ + N--) dN/dMmm Goal : Use singlem events to estimate the background Mmm Mmm F. Fleuret - LLR

  7. A new background estimation • Material : Real data • Sample : pp 2002 • Event Selection : • 2 m trigger (1 deep + 1 shallow) • | ZBBC | < 38 cm • Statistics : • Events w/ at least 2 tracks :455 m+m- / 202 m+m+ / 108 m-m- • Events w/ 1 track only : 25658 singlem+ / 17782 singlem- • Create fake dimuons samples : • Pick randomly 10000 single m events from the 43440 single m events sample • Create combinatorial dimuons from these 10000 single m events, with | ZBBC1 – ZBBC2 | < 5cm  ~ 6 M 2m F. Fleuret - LLR

  8. Likesign dimuons Mass spectra true dimuons : N++/N-- = 1.87 ± 0.31 fake dimuons : N++/N-- = 1.952 ± 0.003 A new background estimation m+m+ m+m+ dN/dMmm Mmm Mmm m-m- m-m- Mmm Mmm F. Fleuret - LLR

  9. A new background estimation • Fake dimuons • Spectra’s shapes dN/dMmm m+m+ m+m+ / m+m- Mmm Mmm m-m- m-m- / m+m- Mmm Mmm m+m- m+m-  m+m+ +m-m- Mmm F. Fleuret - LLR

  10. A new background estimation • Fake dimuons • Opposite signs .vs. Like signs Fake dimuons : N++, N--, N+-known dN/dMmm m+m+ Mmm m-m- Mmm N+- = 0.955 x (N++ + N--) m+m- Mmm F. Fleuret - LLR

  11. A new background estimation • Normalisation with true dimuons m+m+ N+- = 0.955 x (N++ + N--) = 0.955 x (202 + 108)= 296.05 dN/dMmm Mmm m-m- Mmm Use numbers of truem+m+ and truem-m- to normalize the fakem+m- spectrum m+m- Mmm F. Fleuret - LLR

  12. A new background estimation • Bkg’s shape : m+m-  m+m+ +m-m- • Bkg’s integral : N+- = 0.955 x (N+++N--) • Use of fake dimuons spectrum • Normalisation with true likesign dimuons dN/dMmm Mmm Mmm Mmm Mmm F. Fleuret - LLR

  13. Analysis with nanoDSTs : summary • Making nanoDSTs • in progress… • Analyzing nanoDSTs • CVSify the code : comments, suggestions ? • A new macro for background estimation : to be included in MWGana… F. Fleuret - LLR

  14. Background estimation • singlem • Momentum’s spectra m+ m- Pm m+ / m- Pm F. Fleuret - LLR

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