The GAMBIT Universal Model Machine: from Lagrangians to Likelihoods

TL;DR

The paper presents GUM, a tool that automates the generation of code for the GAMBIT framework from Lagrangian models, facilitating comprehensive physics analyses including collider and dark matter studies.

Contribution

GUM automates code generation for GAMBIT from symbolic Lagrangian models, integrating multiple tools and interfaces for broad physics applications.

Findings

Successfully added a Majorana fermion dark matter model to GAMBIT using GUM.

Demonstrated the full workflow with a worked example and a model fit.

Enhanced the accessibility and efficiency of model implementation in GAMBIT.

Abstract

We introduce the GAMBIT Universal Model Machine (GUM), a tool for automatically generating code for the global fitting software framework GAMBIT, based on Lagrangian-level inputs. GUM accepts models written symbolically in FeynRules and SARAH formats, and can use either tool along with MadGraph and CalcHEP to generate GAMBIT model, collider, dark matter, decay and spectrum code, as well as GAMBIT interfaces to corresponding versions of SPheno, micrOMEGAs, Pythia and Vevacious (C++). In this paper we describe the features, methods, usage, pathways, assumptions and current limitations of GUM. We also give a fully worked example, consisting of the addition of a Majorana fermion simplified dark matter model with a scalar mediator to GAMBIT via GUM, and carry out a corresponding fit.