Summer Student Projects Submission 2023

Incorporating energy-loss into the fast detector simulation program SGV

Not scheduled

20m

Description

The next big particle collider project is foreseen to be an e+e- Higgs factory.
Various detector concepts with advanced detector technologies have been
proposed for such colliders, aiming to probe the Standard Model to
unprecedented precision. In order to optimise the physics performance of
these detectors, sophisticated reconstruction algorithms are being developed
and bench-marked on detailed simulations.

However, also fast detector simulation and reconstruction is needed. Such
programs should be several orders of magnitude faster than the full
simulation, to allow to produce enough statistics also in very high
cross-section background processes. Also, in order to serve as a testing-bed
for modified or new detector concepts they should be able to
well simulate the detector response without the need to have pre-existing
fully simulated samples to parameterise.

A program fulfilling these needs is SGV, developed and maintained at DESY.
Currently, elastic scattering of charged particles as the pass through
the detector material is included in SGV, but not the energy-loss.
The project consists of adding a correct treatment of this effect into
SGV.

If time allows, a second topic would be to modify SGV such that it can
call the event generator Whizard internally, thereby avoiding the need
to externally store - potentially huge - samples of generated events.

Physics / Computing / Engineering Content of the project : 50% / 50% / 0%

• Physics: Understanding the process of energy-loss of charged particles
  passing matter in particular the fluctuations of the loss. How this depends
  on both the properties of the particle, and the detector material.

• Computing: Understanding and implementing quite complex algorithms based
  on their mathematical formulation.

Special Qualifications:

Basic skills programming. Knowledge of Root and modern Fortran will
be helpful, but not required. Good knowledge in linear algebra and in statistics/probability theory