This will warm up new users to what pyhf can do. Experienced users of pyhf will be able to sit back and gain a nice refresher.
The cabinetry library provides interfaces and functionality for both the creation and use of statistical models together with pyhf. Models can be built from instructions provided in a declarative configuration. A high-level inference API and visualization utilities help study and disseminate fit results.
This talk will provide an overview of...
This contribution signifies a shift in ATLAS statistical data analysis by implementing traditional fit strategies utilizing the pyhf library, alongside the cabinetry library. Leveraging a toy Supersymmetry search analysis, three fit strategies inspired by the HistFitter framework are implemented. The "background-only fit," "model-dependent signal fit," and "model-independent signal fit"...
The ABCD method is a common background estimation method used by many physics searches in particle collider experiments and involves defining four regions based on two uncorrelated observables. The regions are defined such that there is a search region (where most signal events are expected to be) and three control regions. A likelihood-based version of the ABCD method, also referred to as the...
The HEP Statistics Serialization Standard is a new format in which to
write, store, exchange and archive statistical models. It is based on
JSON and easily readable and writable for both machines and humans. It
is fully interoperable with ROOT workspaces, the current de-facto
standard in the experimental HEP community, and has a concept
implementation in Julia / BAT.jl and intends to be...
HEPExplorer is a web based viewer for high energy particle physics that allows users to view various types of plots from data formatted as HistFactory workspaces. This simple tool provides an easy-to-use and convenient way to generate plots, perform fits, and to investigate the impact of various parameters on the model performance using HistFactory workspaces.
HF Explorer is intended to be...
pyhf, in combination with cabinetry, has been used successfully in a statistical combination of searches for Beyond-Standard-Model particles by the ATLAS Combination. Since for the individual searches various different frameworks were used to perform the statistical analysis, an essential part of the combination effort consisted of validating the statistical workspaces after they had been...
I will give an overview of the HEPData project (hepdata.net), a repository for publication-related data from experimental particle physics. I will describe current support for hosting statistical models in the HistFactory JSON format used by pyhf, as well as links with analysis frameworks like Rivet and MadAnalysis 5. The HEPData software is open source under the...
The MicroBooNE detector is a liquid argon time projection chamber located on the Fermilab campus. It has excellent calorimetric and spatial reconstruction capabilities. Moreover, MicroBooNE is exposed to two neutrino beams, the Booster Neutrino Beam (on-axis) and the Neutrinos at the Main Injector beam (off-axis). These outstanding features make MicroBooNE an ideal experiment to search for...
I will discuss the use we make of published statistical models and patchsets in SModelS. I'll cover benefits as well as difficulties encountered, and wishes for future developments.
In this talk, we will provide an overview of the usage of full likelihoods through pyhf package within LHC reinterpretation studies and software such as [MadAnalysis 5][1], [SModelS][2] and [spey][3]. We will also provide a summary of future directions involving pyhf package, such as the combination of statistical models, simplified likelihoods via ML approaches and converting full...
I'll share a few thoughts on the past, present, and future of the HistFactory specification and pyhf.
The US National Science Foundation has funded a 3-year "Research Coordination Network" called FAIROS-HEP. FAIROS-HEP aims to foster the adoption of practices and cyberinfrastructure to enable reuse and reinterpretation of high energy physics (HEP) datasets. The network has funds to support international workshops and to contribute directly to cyberinfrastructure components such as INSPIRE,...
dilax is a software package for statistical inference with binned likelihoods. It focusses on three key concepts: performance, differentiability, and object-oriented statistical model building. Thus, dilax is build upon the shoulders of a deep learning giant: JAX - a popular autodifferentiation Python framework. By making every component in dilax a PyTree, each component can be...
