Abhishek Chauhan, Eduardo Cortina Gil, Pavel Demin, Khalil El Achi, Andrea Giammanco, Sumaira Ikram, Maxime Lagrange, Nicolas Szilasi, Ayman Youssef, Zahraa Zaher

The general goal of this project is to develop muon-based radiography or tomography (“muography”), an innovative multidisciplinary approach to study large-scale natural or man-made structures, establishing a strong synergy between particle physics and other disciplines, such as geology and archaeology.
Muography is an imaging technique that relies on the measurement of the absorption of muons produced by the interactions of cosmic rays with the atmosphere.
Applications span from geophysics (the study of the interior of mountains and the remote quasi-online monitoring of active volcanoes) to archaeology and mining.

We are using the local facilities at CP3 for the development of high-resolution portable detectors based on Resistive Plate Chambers.

We also participate to the MURAVES collaboration through simulations (including the coordination of the Monte Carlo group), data-analysis developments (an example of the latter is the implementation and in-situ calibration of time-of-flight capabilities), and development of a new database.

We are part of the H2020-RIA project SilentBorder, which aims at developing new muon scanners at border controls. Our role in this project is to develop a parametric simulation and a ML-based detector optimization procedure.

We are also part of the H2020-MSCA-RISE network INTENSE where we coordinate the Muography work package, which brings together particle physicists, geophysicists, archaeologists, civil engineers and private companies for the development and exploitation of this imaging method.

External collaborators: UGent; Kyushu University; INTENSE Research & Innovation Staff Exchange network (Japan, Switzerland, Italy, France, Hungary); SilentBorder network (Estonia, Germany, Finland, Turkey, Italy, UK); MURAVES Collaboration including INFN, INGV, universities of Florence and Federico II Naples, UGent, VUB.

Andrea Giammanco, Maxime Lagrange, Zahraa Zaher

We are among the founders of MODE (Machine-learning Optimized Design of Experiments, https://mode-collaboration.github.io/), a multi-disciplinary consortium of European and American physicists and computer scientists who target the use of differentiable programming in design optimization of detectors for particle physics applications, extending from fundamental research at accelerators, in space, and in nuclear physics and neutrino facilities, to industrial applications employing the technology of radiation detection.

We aim to develop a modular, customizable, and scalable, fully differentiable pipeline for the end-to-end optimization of articulated objective functions that model in full the true goals of experimental particle physics endeavours, to ensure optimal detector performance, analysis potential, and cost-effectiveness.
The main goal of our activities is to develop an architecture that can be adapted to the above use cases but will also be customizable to any other experimental endeavour employing particle detection at its core. We welcome suggestions, as well as interest in joining our effort, by researchers focusing on use cases for which this technology can be of benefit.

External collaborators: University of Padova, INFN, Université Clermont Auvergne, Higher School of Economics of Moscow, CERN, University of Oxford, New York University, ULiege.