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Enhancements in Analysis Framework for NanoDSTs: Background Estimation and Improvements

This document outlines the latest enhancements in the analysis framework for nanoDSTs, highlighting improvements made to the procedure, nodes structure, and background estimation methodologies. It offers a comprehensive overview of the current analysis process, including the production of nanoDSTs, event and particle selection, and the use of real data to create effective fake dimuon samples for background estimation. Key improvements, including memory leak investigations and analysis macros, are also discussed, providing insight into how to optimize the efficiency and effectiveness of the framework.

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Enhancements in Analysis Framework for NanoDSTs: Background Estimation and Improvements

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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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