The physics objects that are used in ATLAS, those that ultimately result from the collisions, are reconstructed at varying levels of quality. There are software tools that determine, after event collection, the quality of the object (i.e., the level of quality at which the object is detected). There are many effects that influence the quality of an object, including but not limited to the trustworthiness of the object’s identification and whether or not any detector subsystems had experienced any issues that affected their performance during object detection.
For a data analysis, it is important to skip events with objects of quality that is too poor to trust.
A good example of objects that have a quality assessment is hadronic jets
(we will learn more about jets later in the tutorial). Jets can be reconstructed
from a primary vertex from which the beam of ionizing radiation originated.
Like electrons and muons, the trustworthiness of identifying a jet is noted
by its “working point”, which spans from loose to medium to tight, each
indicating progressively stricter requirements.
The default event cleaning that the CP Algorithms handles is the removal of
events that contain jets with no primary vertex and/or are of loose quality.
To include this event cleaning in our job, add the following lines to the
config.yaml file:
# Apply common event quality requirements
EventCleaning:
# prune events containing jets with no primary vertx,
# prune events containing loose-likelihood jets,
# etc.
runEventCleaning: True
Try running again and look for changes in your output. In particular, check
how many events are accepted by the VertexSelection and JetCleaning filters.
Once you are satisfied that your code is working correctly, save your progress by committing and pushing your code changes.