Logo

MadGraph allows users to choose a process for which it will generate the corresponding matrix element squared. In analytical calculations this is done by summing over spin to rewrite the Dirac spinors as momenta of the external legs. However, for a process with lots of diagrams this method will require the analysis of so many terms (rising like the number of diagrams squared) making the computation slow and inefficient. One solution, known as the Helicity Amplitude Method, is to evaluate numerically the amplitude allowing the evaluation time to be proportional to the number of diagrams. This method is performed within MadGraph using the HELAS/ALOHA subroutine. In the Helicity Amplitude Method the numerical evaluation of each amplitude needs to be done for each possible combination of helicity of any initial/final state particles. In MadGraph, this is currently done by a simple loop over all helicity combinations. However during such a loop, the same spinor/polarization vector will be recomputed multiple times for the same helicity of a given particles. The same type of identical recomputation occurs as well for some of the propagator wavefunctions which typically depends only on a subset of the helicity configuration. <br>

The purpose of this project is to exploit this fact to optimise the code. Instead of calculating the spinors multiple times the plan is to calculate all of them once and then store their values to RAM. This technique is known as recycling. MadGraph already has some optimisation in this direction but only for identical propagators between different Feynman Diagram. The drawback of recycling helicity is that it complexifies the way to filter out the computation of helicity combinations which are exactly vanishing. The current version of MadGraph uses an on-the-flight filter to avoid such useless combinations. This will not be possible in the presence of recycling and study is required on how to determine in advance which set of helicities are vanishing in order to achieve full optimisation.

Node: 
Louvain
Student: 
Kiran Ostrolenk
Date: 
April, 2020 to August, 2020