DEFT: A program for operators in EFT
Ben Gripaios, Dave Sutherland

TL;DR
DEFT is a Python program designed to manipulate and analyze operators in effective field theories, aiding in basis generation, redundancy checking, and basis transformation for theories like the Standard Model.
Contribution
The paper introduces DEFT, a novel Python tool that automates basis analysis and transformations for EFT operators, enhancing precision tests and new physics searches.
Findings
Successfully checked operator bases for the Standard Model
Generated operator bases from input algorithms
Facilitated basis changes and redundancy elimination
Abstract
We describe a Python-based computer program, DEFT, for manipulating operators in effective field theories (EFTs). In its current incarnation, DEFT can be applied to 4-dimensional, Poincar\'{e} invariant theories with gauge group , such as the Standard Model (SM), but a variety of extensions (e.g. to lower dimensions or to an arbitrary product of unitary gauge groups) are conceptually straightforward. Amongst other features, the program is able to: (i) check whether an input list of Lagrangian operators (of a given dimension in the EFT expansion) is a basis for the space of operators contributing to S-matrix elements, once redundancies (such as Fierz-Pauli identities, integration by parts, and equations of motion) are taken into account; (ii) generate such a basis (where possible) from an input algorithm; (iii) carry out a change of basis. We describe…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
