High energy physics (HEP) beyond colliders challenges mathematical models and their implementation in a unique way. We have to take into account complex interactions with the media the experiment takes place in. This however opens up the door to answer subtle questions not only about the HEP process studied, but also the media itself.
For example, the detection of atmospheric muons passing through various obstacles leads to the development of imaging tools (muography) for their matter density, as well as a possibility to discriminate materials traversed, based on their atomic number.
Data collected from such experiments becomes significant, and the equipment is getting accessible beyond the physics labs for applications. We build a software suite that leverages machine learning and AI technologies to carry out analysis and inference on that data with performance and modelling complexity suitable not only for further scientific work, but also meeting industry requirements.
This library implements adjoint sensitivity methods over Backward Monte-Carlo schemes in particle physics simulations. A general introduction can be found in:
- R. Grinis. Differentiable programming for particle physics simulations. to appear in JETP (2021) arXiv
with complementary material and a video workshop recorded at the QUARKS-2021 conference.
Muography is currently a very active area of research. It presents great opportunities for applications in geology, civil engineering and nuclear security to mention a few.
A differentiable model for muon transport is in preparation.
The component implements energy loss and
Coulomb scattering differential cross-sections
calculations currently for Muons and Taus.
Some examples are available as well as
benchmarks
measuring CPU/OpenMP vs CUDA performance.
We also provide bindings to PUMAS v1.1. Usage examples can be found in functional tests.
In the future, we plan to cover a wider range of particles.
(c) 2023 GrinisRIT ltd. and contributors