Conveners
Plenary Session Wednesday
- Jim Pivarski (Princeton University)
- Matthew Feickert (Univ. Illinois at Urbana Champaign (US))
Recent developments in Scikit-HEP libraries have enabled fast, efficient histogramming powered by boost-histogram. Hist provides useful shortcuts for plotting and profiles based on boost-histogram. This talk aims to discuss these histogramming packages built on the histogram-as-an-object concept.
The attendees would learn how to use these tools to easily make histograms, perform various...
MadMiner is a python based tool that implements state-of-the-art simulation-based inference strategies for HEP. These techniques can be used to measure the parameters of a theory (eg. the coefficients of an Effective Field Theory) based on high-dimensional, detector-level data. It interfaces with MadGraph and "mines gold" associated to the differential cross-section at the parton level and...
zfit is a Python library for (likelihood) model fitting in pure Python and aims to establish a well defined API and workflow. zfit provides a high level interface for advanced model building and fitting while also designed to be easily extendable, allowing the usage of custom and cutting-edge developments from the scientific Python ecosystem in a transparent way.
This tutorial is an...
The talk would cover how we can find loopholes in [Awkward Array][1] using software testing methodologies. Sometimes when dealing with array manipulations we face breakpoints in the process when using certain inputs. Therefore to find out these cases, input values have to be generated automatically based on the constraints of various functions and fed to these functions in order to find out...
mplhep is a small library on top of matplotlib, designed to simplify making plots common in HEP, which are not necessarily native to matplotlib, as well as, to distribute plotting styles and fonts to minimize the amount of needed cookie-cutter code and produce consistent results across platforms.
The BumpHunter algorithm is a well known test statistic designed to find a excess (or a deficit) in a data histogram with respect to a reference model. It will compute the local and global p-values associated with the most significant deviation of the data distribution. This algorithm has been used in various High Energy Physics analyses. The pyBumpHunter package [1] proposes a new public...
We developed a python-based package that facilitates the usage of graph neural network on HEP data. It is featured with pre-defined GNN models for edge
classification and event classification. It also
contains a couple of realistic examples using GNN to solve HEP
problems, for example, top tagger (event classification) and boosted
boson reconstruction (edge classification). One can import...
Hepynet stands for "High energy physics, the python-based, neural-network framework". It's been developed to help with the neural network application in high energy physics analysis tasks. Different tasks like train/tune/apply are implemented based on popular packages used in the industry like Tensorflow. All jobs are defined by simple config files and the functionalities are collected in the...
In high energy physics, the standard convention for expressing physical quantities is natural units. The standard paradigm sets c = ℏ = ε₀ = 1 and hence implicitly rescales all physical quantities that depend on unit derivatives of these quantities.
We introduce [NatPy][1], a simple python module that levarages astropy.units.core.Unit and astropy.units.quantity.Quantity objects to define...
