This crate implements the cell resampling algorithm for the elimination of negative weights in Monte Carlo collider event samples. The algorithm is described in
Unbiased Elimination of Negative Weights in Monte Carlo Samples\ J. Andersen, A. Maier\ arXiv:2109.07851
If Rust and Cargo are installed on your system, run
cargo install cres
Precompiled executables are available on hepforge.
To generate shell command completion, run
cres-generate-shell-completion SHELL
For bash and fish, command completion should work upon the next login. For other shells, the completion code is written to standard output. Consult your shell's documentation if you are unsure what to do with it. To list the supported shells, run
cres-generate-shell-completion --help
The basic usage is
cres -a JETALGO -R JETR --jetpt JETPT -o OUT.HEPMC2 IN.HEPMC2
This takes a file IN.HEPMC2 in hepmc2 format with mixed-weight
events and produces a file OUT.HEPMC2 where all event weights are
positive. The input file can be compressed with bzip2, gzip, zstd, or
lz4.
We recommend to set the jet algorithm JETALGO, jet radius
JETR, and minimum jet transverse momentum JETPT to the same values
that were used to generate the input events. The supported jet
algorithms are anti-kt, kt, and Cambridge-Aachen.
To see a full list of options with short descriptions run
cres --help
The most important options are
--max-cell-size can be used to limit the size of the generated
cells. This ensures that weights are only transferred between events
which are sufficiently similar. The downside is that not all
negative event weights will be removed.
Cell resampling is much faster with a small cell size limit. It is therefore recommended to start with a small value, for example 10, and gradually increase the value if too many negative weights are left.
--partitions the initial sample is divided into the given number
of partitions. The number of partitions has to be a power of two.
On the one hand, values larger than one accelerate the
nearest-neighbour search and are necessary to parallelise the
default search strategy. On the other hand, nearest neighbours may
be missed for cells close to partitions boundaries.
Recommended values are 1 for slow, but exact resampling, or a number up to about twice the number of cores for faster approximate resampling. Increasing the number of partitions is better than increasing the number of runs with different event input files.
--ptweight specifies how much transverse momenta affect distances
between particles with momenta p and q according to the formula
d(p, q) = \sqrt{ ptweight^2 (p\perp - q\perp)^2 + \sum (pi - qi)^2 }
With --minweight events are also unweighted in addition to the
resampling. Events with weight w < minweight are discarded with
probability 1-|w|/minweight and reweighted to sign(w) * minweight
otherwise. Finally, all event weights are rescaled to exactly
preserve the original sum of weights. The seed for unweighting can
be chosen with the --seed option.
To avoid cluttering the command line, options can be saved in an argfile. Each line should contain exactly one option, and option name and value have to be separated by '='. For example:
--jetalgorithm=anti-kt
--jetradius=0.4
--jetpt=30
The argfile can be used like this:
cres @argfile -o OUT.HEPMC2 IN.HEPMC2
The CRES_LOG environment variable allows fine-grained control over
the command line output. For example, to see the debugging output of
the jet clustering, set
CRES_LOG=jetty=debug,cres=info
See the env_logger crate for a
comprehensive documentation.
By default, cres uses as many cores as possible. For small event
samples, limiting the number of threads can be faster. You can set the
number of threads with the --threads command line option or with the
RAYON_NUM_THREADS environment variable.
To read and write ROOT ntuple files,
ROOT has to be installed and root-config has to be in the executable
path. To install cres with ntuple support, run
cargo install cres --features ntuple
Input files are recognised automatically, the output file format can
be chosen with the --outformat flag.
For full flexibility like custom distance functions cres can be used
as a library from Rust and C. For examples, see the examples
subdirectory. The Rust API is documented on
docs.rs. The C API is still limited and
only available on unixoid platforms. It will be extended on request.