The snippets below demonstrate how to split MC events originally intended for the 2024 data-taking period into orthogonal parts that can be used to simulate the 2024, 2025 and 2026 data-taking periods. The splitting is done event-by-event and only depends on the event ID. The underlying algorithm is deterministic and splits events into three parts with frequencies that reflect the relative recorded luminosities in 2024, 2025 and 2026.
The unbiased behavior is strictly necessary to avoid that algorithms based on event IDs that are used downstream in analyses see a bias towards specific event IDs in any of the three years.
For instance, event splitting for ML training / validation purposes, usually done via (e.g.) event % 2 == 0 or alike, should remain statistically unaffected.
If there was a bias in the event ID distribution, this would lead to skewed training and validation datasets, which is not desirable.
- Author: Marcel Rieger
- Source: https://gist.github.com/riga/f9476f3b1477f1609683bea68ae64897
import correctionlib as clib
cset = correctionlib.CorrectionSet.from_file("mc_event_splitter.json")
splitter = cset["mc_event_splitter"]
# note that event numbers must be passed as float values
splitter.evaluate(16849655.0)
# -> 2026.0