About

The PyPWA Project aims to develop a software framework that can be used to perform parametric model fitting to data. In particular, Partial Wave and Amplitude Analysis (PWA) of multiparticle final states. PyPWA is designed for photoproduction experiments using linearly polarized photon beams. The software makes use of the resources at the JLab Scientific Computer Center (Linux farm). PyPWA extract model parameters from data by performing extended likelihood fits. Two versions of the software are develop: one where general amplitudes (or any parametric model) can be used in the fit and simulation of data, and a second where the framework starts with a specific realization of the Isobar model, including extensions to Deck-type and baryon vertices corrections.

Tutorials (Step-by-step instructions) leading to a full fit of data and the use of simulation software are included. Most of the code is in Python, but hybrid code (in Cython or Fortran) has been used when appropriate. Scripting to make use of vectorization and parallel coprocessors (Xeon-Phi and/or GPUs) are expected in the near future. The goal of this software framework is to create a user friendly environment for the spectroscopic analysis of linear polarized photoproduction experiments. The PyPWA Project software expects to be in a continue flow (of improvements!), therefore, please check on the more recent software download version.

What can PyPWA do?

  • Likelihood fitting with ChiSquared and Log Likelihood

  • Simulation using the Monte-Carlo Rejection Sampling method

  • Multi-variable binning for 4 vector particle data (in GAMP Format)

  • Convert and mask data between similar data types

  • Load data into an HDF5 dataset

Further Reading

Team Members

Current PyPWA Staff

Previous PyPWA Staff

High School Interns

  • Ryan Wright HS

    • Ran Amplitude benchmarks on the XeonPhi

Citations

  • Roger Barlow. Extended maximum likelihood. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 297(3):496–506, 1990.

  • Ph R BEVINGTON. Data reduction and error analysis for the physical sciences. Technical Report, McGraw-Hill, 1969.

  • Suh Urk Chung. Spin formalisms. Technical Report, CERN, 1971.

  • WT Eadie, D Drijard, FE James, M Roos, and B Sadoulet. Statistical methods in experimental physics, 2nd reprint. 1982.

  • M Jacob and Gr C Wick. On the general theory of collisions for particles with spin. Annals of Physics, 7(4):404–428, 1959.

  • F James. Minuit reference manual, cern program library long writeup d506. James and M. Winkler, MINUIT User’s Guide, CERN, 1994.

  • Fred James, Matthias Winkler, and others. Minuit user’s guide. MIGRAD CERN, 2004.

  • David JC MacKay and David JC Mac Kay. Information theory, inference and learning algorithms. Cambridge university press, 2003.

  • J Orear. Notes on statistics for physicists (1958). UCRL-8417, 1982.

  • Carlos W Salgado and Dennis P Weygand. On the partial-wave analysis of mesonic resonances decaying to multiparticle final states produced by polarized photons. Physics Reports, 537(1):1–58, 2014.

  • K Schilling, P Seyboth, and G Wolf. On the analysis of vector-meson production by polarized photons. Nuclear Physics B, 15(2):397–412, 1970.

  • John Skilling. Nested sampling. In AIP Conference Proceedings, volume 735, 395–405. AIP, 2004.

  • Charles Zemach. Use of angular-momentum tensors. Physical Review, 140(1B):B97, 1965.