Automated Collider Event Selection, Plotting, & Machine Learning with AEACuS, RHADAManTHUS, & MInOS
July 14, 2022
A trio of automated collider event analysis tools are described and demonstrated, in the form of a quick-start tutorial.
AEACuS interfaces with the standard MadGraph/MadEvent, Pythia, and Delphes simulation chain, via the Root file output.
An extensive algorithm library facilitates the computation of standard collider event variables and the transformation
of object groups (including jet clustering and substructure analysis).
Arbitrary user-defined variables and external function calls are also supported. An efficient mechanism is
provided for sorting events into channels with distinct features.
RHADAManTHUS generates publication-quality one- and two-dimensional histograms from event statistics computed by AEACuS,
calling MatPlotLib on the back end. Large batches of simulation (representing either distinct final states and/or
oversampling of a common phase space) are merged internally, and per-event weights are handled consistently throughout.
Arbitrary bin-wise functional transformations are readily specified, e.g. for visualizing signal-to-background
significance as a function of cut threshold. MInOS implements machine learning on computed event statistics with XGBoost.
Ensemble training against distinct background components may be combined to generate composite classifications
with enhanced discrimination. ROC curves, as well as score distribution, feature importance, and significance plots are generated on the fly.
Each of these tools is controlled via instructions supplied in a reusable cardfile, employing a simple, compact, and powerful meta-language syntax.
How to cite
Metadata are provided both in "article" format (very similar to INSPIRE) as this helps creating
very compact bibliographies which can be beneficial to authors and
readers, and in "proceeding" format
which is more detailed and complete.