This lightning talk will focus on introducing the new features in Uproot v5, with most focus on the newly-introduced uproot.dask function. The dask integration will be demoed through example workflows that explore all the new features of the uproot.dask function. Important options of the function's API like delaying the opening of files and variable chunk sizes will be demoed in the Jupyter...
This lightning talk will introduce fsspec-xrootd, a newly published middleware software package that allows Dask and other popular data analysis packages to directly access XRootD storage systems. These packages use the fsspec api as their storage backends and fsspec-xrootd adds an XRootD implementation to fsspec. This means that when using fsspec-xrootd, the user will be able to access XRootD...
Why Python is a good choice for making digital twins for the industry/research?
Through several examples of practical use cases the talk will present our experiences of 3D and Virtual Reality, all implemented in Python with the help of our 3D package "HARFANG 3D" :
- Human factor study of a railway station in virtual reality
- Using a aircraft simulation sandbox for AI training
-...
The scientific community using Python has developed several ways to accelerate Python codes. One popular technology is Numba, a Just-in-time (JIT) compiler that translates a subset of Python and NumPy code into fast machine code using LLVM. We have extended Numba’s integration with LLVM’s intermediate representation (IR) to enable the use of C++ kernels and connect them to Numba accelerated...
zfit is a scalable, pythonic model fitting library that aims at implementing likelihood fits in HEP. So far, the main functionality was focused on unbinned fits. With zfit 0.10, the concept of binning is introduced and allows for binned datasets, PDFs and losses such as Chi2 or Binned likelihoods. All of these elements are interchangeable and convertable to unbinned counterparts and allow for...
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 use of statistical models to accurately represent and interpret data from high-energy physics experiments is crucial to gaining a better understanding of the results of those experiments. However, there are many different methods and models that researchers are using for these representations, and although they often generate results that are useful for everyone in the field of HEP, they...
At the intersection of high energy physics, deep learning, and high performance computing there is a challenge: how to efficiently handle data I/O of sparse and irregular datasets from high energy physics, and connect them to python and the deep learning frameworks? In this lightning talk we present larcv, an open source tool built on HDF5 that enables parallel, scalable IO for irregular...
Machine learning is becoming ubiquitous in high energy physics for many tasks, including classification, regression, reconstruction, and simulations. To facilitate development in this area, and to make such research more accessible and reproducible, we present the open source Python [JetNet library][1] with easy to access and standardised interfaces for particle cloud datasets, implementations...
