MODE (for Machine-learning Optimized Design of Experiments) is a collaboration of 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.
Our aim to develop modular, customizable, and scalable, fully differentiable pipelines 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.
- Tommaso Dorigo (INFN-Padova)
- Peter Elmer (Princeton University)
- Nicolas R. Gauger (TU-Kaiserslautern)
- Pablo Martinez Ruiz del Arbol (Universidad de Cantabria)
- Roberto Ruiz de Austri Bazan (IFIC Valencia, astro-HEP)
- Pietro Vischia (Universidad de Oviedo)
- Gordon Watts (University of Washington)
Scientific Advisory Committee:
- Atilim Gunes Baydin (University of Oxford)
- Kyle Cranmer (University of Wisconsin)
- Julien Donini (Université Clermont Auvergne)
- Piero Giubilato (Università di Padova)
- Gian Michele Innocenti (CERN)
- Michael Kagan (SLAC)
- Riccardo Rando (Università di Padova)
- Kazuhiro Terao (SLAC)
- Andrey Ustyuzhanin (SIT, HSE Univ., NUS)
- Christoph Weniger (University of Amsterdam)
Princeton Local Organizers
- Maureen Carothers (Princeton University)
- Florevel Fusin-Wischusen (Princeton University)
- Andrea Rubinstein (Princeton University)
This workshop is partially supported by the joint ECFA-NuPECC-APPEC Activities (JENAA).
This workshop is partially supported by National Science Foundation grant OAC-1836650 (IRIS-HEP).