Physics

GooFit 2.1 introduces the full-featured Python bindings to GooFit. These bindings mimic the C++ usage of GooFit, including bindings for all PDFs, and also provide NumPy-centric conversions, live Jupyter notebook printing, pip install, and more. Most of the examples in C++ are provided in Python form, as well.

Several other API changes were made. Observables are now distinguished from Variables and provided as a separate class. Both these classes are now passed around by copy everywhere.¹ The three and four body amplitude classes have been refactored and simplified. OpenMP is now supported via homebrew on macOS; GooFit is one of the only packages that currently can build with OpenMP on the default macOS compiler. Eigen is now available, and CLI11 has been updated to version 1.3.

GooFit 2.1 will receive continuing support while development on GooFit 2.2 presses on with a new indexing scheme for PDFs.

The next version of the premier CUDA/OpenMP fitting program for HEP analysis, GooFit 2.0, has been released. GooFit is now easy to build on a wide variety of Unix systems, and supports debuggers and IDEs. GooFit is faster, has unit tests, and working examples. More PDFs and examples have been added, as well as newly released example datasets that are downloaded automatically. GooFit now has built in support for MPI, and can use that to deploy to multiple graphics cards on the same machine. A new command line parser (CLI11) and drastically improved logging and errors have made code easier to write and debug. Usage of GooFit specific terminology is now reduced, using standard Thrust or CUDA terms when possible, lowering the barrier for new developers. A new Python script has been added to assist users converting from pre 2.0 code.

The file structure of GooFit and the build system have been completely revamped. The fake nvcc features have been removed, as have the rootstuff copies of ROOT classes. PDFs are now organized by type and compile and link separately. Multiple PDF caching support has improved. The build system now uses CMake and manages external libraries.

A new feature of the CMake build system is GooFit Packages, which are complete packages that can be added to GooFit and built, allowing analysis code to live in a separate location from GooFit, rather than the old method of simply forking GooFit and adding your analysis manually. A GooFit Package can be made into an example trivially. See this package for an example.

GooFit 2.0 will receive continuing support while development on GooFit 2.1 presses on.

Links:

GooFit on GitHub • GooFit webpage • API documentation

There is a package for making Feynman diagrams in LaTeX. Unfortunately, it is old and dvi latex only. If you are using pdflatex or lualatex, as you should be, it does not work. Even in regular LaTeX, it’s a bit of a pain. Why is there not a new package for pdflatex? Turns out, you don’t need one. Due to the powerful drawing library Tikz, you can create any diagram easily, and can customize it completely. For example:

I realized that CRY did not have a CMake based install option, so including it in a GEANT4 cmake project might not be obvious. This is how you would do it in your CMakeLists.txt:

This is intended as an example to demonstrate the use of overloading in object oriented programming. This was written as a Jupyter notebook (aka IPython) in Python 3. To run in Python 2, simply rename the variables that have unicode names, and replace truediv with div.

While there are several nice Python libraries that support uncertainty (for example, the powerful uncertainties package and the related units and uncertainties package pint), they usually use standard error combination rules. For a beginning physics class, often ‘maximum error’ combination is used. Here, instead of using a standard deviation based error and using combination rules based on uncorrelated statistical distributions, we assume a simple maximum error and simply add errors.

To implement this, let’s build a Python class and use overloading to implement algebraic operations.