# Difference between revisions of "Simulation Tasks - collection"

From Cuawiki

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* Compare different reconstruction algorithms | * Compare different reconstruction algorithms | ||

− | * Investigate discrimination between single photon and merged photons from pi0 decay based on cluster tower energy distribution; same for e/pi separation - possibly explore machine learning application | + | * Investigate discrimination between single photon and merged photons from pi0 decay based on cluster tower energy distribution; same for e/pi separation - possibly explore machine learning application (contact: Renee) |

* Hybrid calorimeter clustering | * Hybrid calorimeter clustering | ||

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* Angular (rapidity) dependencies: for non-projective geometry for modules with the same length, the EMCal depth seen by the particle will depend on rapidity (with 45 degrees to the beam line it will be a factor of sqrt(2) larger compared to 0 degree). We know that e/h separation will be affected (shower profile), but simple E/p matching performance for eID may be affected too (deeper EMCal - more probability for charged hadron to shower); also the affect of gaps will be different with the angle. | * Angular (rapidity) dependencies: for non-projective geometry for modules with the same length, the EMCal depth seen by the particle will depend on rapidity (with 45 degrees to the beam line it will be a factor of sqrt(2) larger compared to 0 degree). We know that e/h separation will be affected (shower profile), but simple E/p matching performance for eID may be affected too (deeper EMCal - more probability for charged hadron to shower); also the affect of gaps will be different with the angle. | ||

− | * Comparison of simulation with test beam data (electrons, pions, etc) and find the ways for simulation tuning (e.g. light transport model and/or non-uniformity map, etc.) | + | * Comparison of simulation with test beam data (electrons, pions, etc) and find the ways for simulation tuning (e.g. light transport model and/or non-uniformity map, etc.) - contacts: Petr, |

* ... | * ... |

## Revision as of 10:40, 17 May 2021

- Implement EEEMCal (contact: Carlos)

- Implement barrel homogeneous EMCal (contact: Nathaly)

- Compare different reconstruction algorithms

- Investigate discrimination between single photon and merged photons from pi0 decay based on cluster tower energy distribution; same for e/pi separation - possibly explore machine learning application (contact: Renee)

- Hybrid calorimeter clustering

- see JLab HyCal

- Light transport going beyond summing up GEANT4 hits

- Determine impact of calorimeter support structure on physics performance; define module geometry

- study/conclude on the tolerance for the size of the gaps between modules (defined by the alveolar and other support elements); also for the transition region between two calorimeters.

- Angular (rapidity) dependencies: for non-projective geometry for modules with the same length, the EMCal depth seen by the particle will depend on rapidity (with 45 degrees to the beam line it will be a factor of sqrt(2) larger compared to 0 degree). We know that e/h separation will be affected (shower profile), but simple E/p matching performance for eID may be affected too (deeper EMCal - more probability for charged hadron to shower); also the affect of gaps will be different with the angle.

- Comparison of simulation with test beam data (electrons, pions, etc) and find the ways for simulation tuning (e.g. light transport model and/or non-uniformity map, etc.) - contacts: Petr,

- ...