Introduction

• Nonleptonic weak decays of charmed mesons have unique features that make them excellent laboratories to study light-quark spectroscopy.
• The decays of \(D\) mesons into three hadrons exhibit a rich resonance structure at low energies, below 2 GeV.

They are, in consequence, a valuable tool for the investigation of QCD in such poorly understood regime, in which perturbation theory is not suitable.

The \(D^{+}\) meson is the lightest known particle containing charm quarks,  its mass is known to be 1869.62\(\pm\)0.20 MeV

Other approaches relying on phenomenological models are required for the description of these decays.

 One widely used model is the so-called isobar model (IM), derived in the Sixties, considerably before the development of QCD. 

Weak decay of charmed mesons

http://dx.doi.org/10.1103/PhysRevD.102.076012

http://dx.doi.org/10.1103/PhysRevD.98.056021

Introduction (Back up)

Research Question and Objectives

• Specific Objectives

1.  Perform a toy Dalitz plot fit of the \(D^{+}\longrightarrow K^{-}K^{+}K^{+}\) decay channel using the GooFit framework and the known parameters obtained with a fit using the isobar model.
2. Reproduce the known results for the \(D^{+}\longrightarrow K^{-}K^{+}K^{+}\) Dalitz plot fit employing the decay amplitude of the first version of the Triple-M and the GooFit framework.
3. Codify the last version of the Triple-M and perform some toy Dalitz plot fits using the GooFit  toolkit.
4. Pre-select the \(K^{-}K^{+}K^{+}\) candidates from the trigger samples by means of the slope-difference and PID variables, in order to remove clone-tracks and cross-feeds from other decay channels, respectively.
5. Match the kinematic variables of the preselected \(K^{-}K^{+}K^{+}\) simulated candidates with those of the preselected data candidates using the Gradient Boosted Reweighter algorithm.
6. Reduce the combinatorial background remaining in the preselected data sample by employing a multivariate selection. The TMVA package will be used.

Introduction: Toys and fits

Selection: Data preparation and Data analysis

Jan.

Mar.

Feb.

Nov.

Oct.

Sep.

Aug.

Jun.

May

Apr.

Mar.

Feb

Jan.

2022

2023

Research Question and Objectives

• Specific Objectives

7. Graph the purities obtained when different cuts are applied on the chosen classifier variable obtained from the multivariate selection.

8. Make fits for the invariant mass distribution of the \(D^{+}\longrightarrow K^{-}K^{+}K^{+}\) candidates using RooFit in order to determine the signal and background yields, and also to define the signal region for the Dalitz plot fit.

9. Compute a map of the total detection efficiency over the \(D^{+}\longrightarrow K^{-}K^{+}K^{+}\) Dalitz plot phase space.

10. Determine the background model by inspecting the sidebands of the \(D^{+}\longrightarrow K^{-}K^{+}K^{+}\) signal.

11. Perform the final Dalitz plot fit of the \(D^{+}\longrightarrow K^{-}K^{+}K^{+}\) decay channel using the Triple-M formalism.

12. Study the fit results and compare them with those known for the isobar model and the first version of the        Triple-M.

Methodology

A methodology proposal -including strategies, steps and tools- for each of the specific objectives presented. Here only some of them are discussed. 

• Specific Objective #4

Original data Dalitz plot

Dalitz plot after a cut on the SDV

Slope difference variables (SDV)

Methodology

Methodology (Back up)

• Slope difference variables distribution

Methodology

• Specific Objective #8

Roofit Toolkit

Methodology

• Specific Objective #8

Schedule

• Timeline

• 2022

The schedule has been splitted according to the specific objectives proposed, and for each one of them a period of one or two months has been given depending on the complexity (some of them have to developed in parallel). 

Schedule

• 2023