The "LPBkg" Package

The LPBkg package provides a Python implementation for the detection of new signals under background mismodelling algorithm, proposed in S. Algeri, 2019. The algorithm implements a unified statistical strategy to perform modeling, estimation, inference, and signal characterization under background mismodeling. The methodology proposed in S. Algeri, 2019 allows to incorporate the (partial) scientific knowledge available on the background distribution and provides a data-updated version of it in a purely nonparametric fashion without requiring the specification of prior distributions.

Further details and theory about the algorithm can be found in the "Detecting new signals under background mismodelling", [arXiv:1906.06615].

For more technical problems, please contact the author of the package Xiangyu Zhang at zhan6004@umn.edu.

For more theoretical references, please contact the author of the paper Sara Algeri at salgeri@umn.edu.

Available Implementations

At this time, the 'LPBkg' is available right out of the box to the general public for personal use in the following programming languages:

• R - Code and Tutorial
• Python - User Guide

References

Algeri S. (2019). Detecting new signals under background mismodelling. [arXiv:1906.06615]

License

The software is subjected to the GNU GPLv3 licensing terms and agreements.