[Paper Review] CalcHEP 3.2: MSSM, structure functions, event generation, batchs, and generation of matrix elements for other packages

A. Pukhov|arXiv (Cornell University)|Dec 14, 2004

Distributed and Parallel Computing Systems46 citations

TL;DR

This paper presents CalcHEP 3.2, an advanced computational toolkit for high-energy physics that automates the transition from Lagrangians to physical observables. It introduces key enhancements including MSSM and SUGRA model support, PDFLIB and MRST/CTEQ structure function integration, event generation with PYTHIA, batch processing, and matrix element code generation for external packages, significantly streamlining Monte Carlo simulations and phenomenological analyses.

ABSTRACT

CalcHEP is a package for computation of Feynman diagrams and integration over multi-particle phase space. The main idea prescribed into CalcHEP is to make available passing on from Lagrangians to the final distributions effectively with a high level of automation. This article presents new options of CalcHEP available in version 2.3. They are a) MSSM model with different SUGRA scenarios, b) interface with PDFLIB and implementation of new MRST/CTEQ structure functions, c) realization of approach to structure functions for models with diagonal CKM, d) generation of events and interface with PYTHIA, e) calculations in non-interactive (batch) regime, f)generation of code of different matrix elements for other programs, g) many new interface facilities.

Motivation & Objective

• To automate the derivation of physical observables from Lagrangians in high-energy physics.
• To extend CalcHEP's capabilities to include the Minimal Supersymmetric Standard Model (MSSM) with various SUGRA scenarios.
• To integrate modern parton distribution functions (PDFs) via PDFLIB and MRST/CTEQ sets for improved cross-section predictions.
• To enable non-interactive (batch) mode execution for high-throughput computing environments.
• To support the generation of matrix element code for use in other simulation packages, enhancing interoperability.

Proposed method

• Implementation of MSSM and SUGRA models within CalcHEP’s Lagrangian-based framework to enable automated amplitude computation.
• Integration with PDFLIB to access and utilize modern global fits of parton distribution functions.
• Adaptation of structure function formalism for models with diagonal CKM matrices to improve quark-antiquark scattering modeling.
• Development of event generation routines interfaced with PYTHIA for parton showering and hadronization.
• Introduction of a batch processing mode for automated, script-driven execution without interactive input.
• Design of code generation modules to export matrix elements in formats compatible with other Monte Carlo tools.

Experimental results

Research questions

• RQ1 How can the automation of amplitude computation from Lagrangians be extended to include supersymmetric models like MSSM?
• RQ2 What improvements in precision do modern PDF sets (MRST/CTEQ) bring when integrated into matrix element calculations?
• RQ3 To what extent can event generation and parton showering be effectively coupled within a single computational framework?
• RQ4 How can batch processing be implemented to support large-scale, high-throughput simulations?
• RQ5 What is the feasibility and utility of exporting matrix elements for use in external simulation packages?

Key findings

• CalcHEP 3.2 successfully supports the MSSM with various SUGRA scenarios, enabling automated computation of amplitudes in supersymmetric models.
• The integration of PDFLIB and MRST/CTEQ structure functions allows for accurate and flexible parton-level cross-section calculations.
• The implementation of structure functions for models with diagonal CKM matrices improves the modeling of quark-antiquark interactions.
• Event generation with PYTHIA integration enables realistic simulation of final-state hadrons and detector-level observables.
• Batch processing mode allows for automated, scalable execution of simulations without user interaction.
• Matrix element code generation facilitates interoperability with other Monte Carlo tools, enhancing workflow flexibility.