1 / 8

J/Ψ Event Selection Algorithm: Current Status and Methodology Overview

The J/Ψ event selection algorithm developed by Maciej Krauze from the Institute of Physics at the University of Silesia aims to minimize background events due to low J/Ψ multiplicity. The approach prioritizes speed and efficiency, leveraging the Transition Radiation Detector (TRD) to provide crucial information about particle trajectories and momentum. Key techniques include invariant mass calculations of particle pairs to identify and reject background events. The algorithm shows an efficiency exceeding 90%, promising improvements through additional cuts and detector data integration.

naasir
Télécharger la présentation

J/Ψ Event Selection Algorithm: Current Status and Methodology Overview

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. J/Ψevent selection algorithm - status Maciej KrauzeInstitute of Physics University of Silesia, Katowice M.Krauze, J/Ψ event selection algorithm - status

  2. Motivations • there are many background events due to very low J/Ψ multiplicity • reduction of the number of events • in order to make it possible to store the data • to make the online analysis feasible Requirements of the method • fast  because we measure at the full beam luminosity • using as less detector information as possible (currently: 3 stations of the Transition Radiation Detector) • efficient  to reduce the bulk of data passed to the next level analysis system M.Krauze, J/Ψ event selection algorithm - status

  3. Software tools • UrQMD, Pluto, Geant & ROOT • as a software base, CBM framework package was used; this package incorporates TRD detectors layout The detector layout used in our studies M.Krauze, J/Ψ event selection algorithm - status

  4. Why TRD can be usefull for background event reduction • it can provide information about the particle’s trajectory and momentum (estimation!) • it can distinguish between e+e- and hadrons (Π, p) • 95-99% of hadron rejection (depends on the particle’s momentum) • the detector has large material budget so the the multiple scattering process has an influence on obtained results • not very high resolution M.Krauze, J/Ψ event selection algorithm - status

  5. Event selection – methods & ideas • to supress as many background events as possible • to preserve the signal How? • the main selection critterion is the invariant mass value • we take every 2 particles of unlike charge within the same event and calculate the invariant mass of the pair • for the J/Ψ particles, the invariant mass of the decay pair is 3.1 GeV/c2 • if the event does not contain any pairs of invariant mass greater than 2 GeV/c2, it is REJECTED M.Krauze, J/Ψ event selection algorithm - status

  6. TRD1 TRD2 Y Target Z Transversal momentum cut • removes low-energy particles from background • removes some fraction of signal (depends on the threshold value) • to perform it we need a magnetic field and a method of momentum reconstruction Further reduction of the number of particles taken to the combinatorics (speed!) • non-bending plane cut (Y): M.Krauze, J/Ψ event selection algorithm - status

  7. Further reduction of the number of particles taken to the combinatorics (continued) • bending plane cut (X): X Target Z these two geometric cuts combined reject 75% of secondaries while only 3% of signal is lost TRD1 TRD2 1 m M.Krauze, J/Ψ event selection algorithm - status

  8. Requirements of the method • fast track finder (at present we consider ideal tracks) • precise track fitter (Kalman Filter) • momentum determination method (fast and precise) Summary and next steps • the algorithm has roughly 90% efficiency or more (depends on the parameters used) • we have to consider realistic track finder • one can use some additional cuts (Pt angle, opening angle, momentum value etc.) • to achieve greater efficiency, it may be necessary to use information from additional detector(s) M.Krauze, J/Ψ event selection algorithm - status

More Related