Introduction

reboost contains both:

• a library of useful functions for applying post-processing steps “processors”,

• end-to-end tools for running the full post-processing controlled by YAML configuration files,

• a dedicated tool for computing and using scintillation optical maps.

“Using reboost processors” describes a reboost processors and how to use these for a simple simulation post-processing in a python script.

Next “The hit tier configuration file” explains how to run the full “hit-tier” (more details later) post-processing with a configuration file, in a very simple way to data processing with pygama. This provides a method to make a generic and customised simulation post-processing. This depends on generic and efficient iteration over the remage files described in “Efficient iteration over remage files”. Finally, the information from multiple systems can be combined to build events (described in “Building events”).

Tiers in reboost

The simulation workflow in remage/reboost is divided into several “tiers”, in a similar way to the pygama data processing. This is then reflected in the name of the lh5 group for each file. Currently we have the following tiers, these mirror those in pygama:

• stp: The “step” information from remage / geant4. More information can be found in the remage documentation!

• glm: Or “Geant4 lookup map”, a tool to enable efficient iteration of the remage files, not needed for all applications.

• hit: The data after grouping steps according to the physical interaction time-scale in the detector, and applying simulation post-processing steps that depend on only a single detector.

• tcm: Or “time-coincidence-map” a mapping of which hits happened simultaneously (within the detector time resolution) between different detector.

• evt: Or “event” the final output file combining the information from the various detectors/ subsystems.

The next sections describe the hit tier processing.