scholarly journals Coffea Columnar Object Framework For Effective Analysis

2020 ◽  
Vol 245 ◽  
pp. 06012
Author(s):  
Nicholas Smith ◽  
Lindsey Gray ◽  
Matteo Cremonesi ◽  
Bo Jayatilaka ◽  
Oliver Gutsche ◽  
...  
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Data Delivery ◽  
Future Directions ◽  
New Approach ◽  
Big Data Technologies ◽  
Python Programming ◽  
Python Package ◽  
Effective Analysis ◽  
Energy Physics

The coffea framework provides a new approach to High-Energy Physics analysis, via columnar operations, that improves time-to-insight, scalability, portability, and reproducibility of analysis. It is implemented with the Python programming language, the scientific python package ecosystem, and commodity big data technologies. To achieve this suite of improvements across many use cases, coffea takes a factorized approach, separating the analysis implementation and data delivery scheme. All analysis operations are implemented using the NumPy or awkward-array packages which are wrapped to yield user code whose purpose is quickly intuited. Various data delivery schemes are wrapped into a common front-end which accepts user inputs and code, and returns user defined outputs. We will discuss our experience in implementing analysis of CMS data using the coffea framework along with a discussion of the user experience and future directions.

scholarly journals HIPSTER

2019 ◽  
Vol 214 ◽  
pp. 06027
Author(s):  
Adrian Bevan ◽  
Thomas Charman ◽  
Jonathan Hays
Keyword(s):  
Neural Networks ◽  
High Energy Physics ◽  
High Energy ◽  
Event Recognition ◽  
The Core ◽  
Rich Information ◽  
The Rich ◽  
Core Functionality ◽  
Python Package ◽  
Energy Physics

HIPSTER (Heavily Ionising Particle Standard Toolkit for Event Recognition) is an open source Python package designed to facilitate the use of TensorFlow in a high energy physics analysis context. The core functionality of the software is presented, with images from the MoEDAL experiment Nuclear Track Detectors (NTDs) serving as an example dataset. Convolutional neural networks are selected as the classification algorithm for this dataset and the process of training a variety of models with different hyper-parameters is detailed. Next the results are shown for the MoEDAL problem demonstrating the rich information output by HIPSTER that enables the user to probe the performance of their model in detail.

A new approach to distributed computing in high energy physics

1992 ◽  
Author(s):  
Paul Avery ◽  
Chandra Chegireddy ◽  
John Brothers ◽  
Theodore Johnson ◽  
Aric Zion
Keyword(s):  
Distributed Computing ◽  
High Energy Physics ◽  
High Energy ◽  
New Approach ◽  
Energy Physics

Development of Silicon Sensor Characterization System for Future High Energy Physics Experiments

2015 ◽  
Vol 3 (1) ◽  
pp. 41-45
Author(s):  
Preeti Kumari ◽  
◽  
Kavita Lalwani ◽  
Ranjit Dalal ◽  
Ashutosh Bhardwaj ◽  
...  
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Physics Experiments ◽  
Silicon Sensor ◽  
Energy Physics
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