Line: 1 to 1

# Master by Research (MRes) project of Bogdan Mishchenko :"Simulation, reconstruction, Physics sensitivity studies for the SiD(Sillicon Detector) "

Line: 18 to 18
SiD detector mentioned above is the one of two detector concepts proposed for ILC. Below you can see layout of the detector. My thesis is focused mostly on the Tracker subsystem. Please refer to the report that will be attached later on for more details on it.

>
>

## Generation/simulation chain

Prior simulation of detector response of the SiD, we need to generate particles. In the simple case it can be running script that specifies PDG number of particle, 4 vector of particle and other usefull parameters.

### How to generate file with blank of particles

Assuming that you have installed lcgeo(linear collider geometry package). If not, follow instruction on https://twiki.ppe.gla.ac.uk/bin/view/LinearCollider/GlaSiDGettingStarted . On the fresh login to terminal execute :
source /cvmfs/sft.cern.ch/lcg/releases/gcc/4.8.4/x86_64-slc6/setup.sh
source /cvmfs/ilc.desy.de/sw/x86_64_gcc48_sl6/v01-17-10/init_ilcsoft.sh
cd lcgeo/
python example/lcio_particle_gun.py (where you specify PDG and incident angle \theta  and number of generated particles and other parameters)
ddsim --compactFile=SiD/compact/SiD_o1_v01/SiD_o1_v03.xml --runType=batch --inputFile mcparticles.slcio -N=1 --outputFile=testSiD_o1_v03.slcio


Changed:
<
<

>
>

## Digitization/reconstruction chain

### MC TruthTracking

Aim of the MC(Monte Carlo) Truth Tracking is to get make sure that we can get sensible results from analysis of the data from the simulation/reconstruction chain.

#### How to produce Hit pull distributions

Deleted:
<
<
Aim of the MC Truth Tracking is to get make sure that we can get sensible results from analysis of the data from the simulation/reconstruction chain.
Below you can find Pulls distribution track parameters, which indicate good performance of the MC Truth Tracking:

• pull_residuals_40.jpg:
Changed:
<
<

## Pattern recognition

>
>

### Pattern recognition

Pattern recognition is a field of applied mathematics, which aim is the classification of objects. For instance, position measurements along particle trajectories can be classified as different physical objects. Please refer to the report that will be attached later on for more details on it.