Contact
Luigi Favaro
Position
Postdoc
Address
Centre for Cosmology, Particle Physics and Phenomenology - CP3
Université catholique de Louvain
2, Chemin du Cyclotron - Box L7.01.05
B-1348 Louvain-la-Neuve
Belgium
Université catholique de Louvain
2, Chemin du Cyclotron - Box L7.01.05
B-1348 Louvain-la-Neuve
Belgium
Office
UCL member card
Projects
Research directions:
Experiments and collaborations:
Active projects
Non-active projects
Data analysis in HEP, astroparticle and GW experiments
Particle Physics
Phenomenology of elementary particles
Research and development of new detectors
Technology Transfer
Particle Physics
Phenomenology of elementary particles
Research and development of new detectors
Technology Transfer
Experiments and collaborations:
Active projects
Development of a framework for fast simulation of a generic collider experiment: Delphes
Jérôme de Favereau, Christophe Delaere, Pavel Demin, Luigi Favaro, Andrea Giammanco, Vincent Lemaitre
Observability of new phenomenological models in High Energy experiments is delicate to evaluate, due to the complexity of the related detectors, DAQ chain and software. Delphes is a new framework for fast simulation of a general purpose experiment. The simulation includes a tracking system, a magnetic field, calorimetry and a muon system, and possible very forward detectors arranged along the beamline. The framework is interfaced to standard file format from event generators and outputs observable analysis data objects. The simulation takes into account the detector resolutions, usual reconstruction algorithms for complex objects (FastJet) and a simplified trigger emulation. Detection of very forward scattered particles relies on the transport in beamlines with the Hector software.
Observability of new phenomenological models in High Energy experiments is delicate to evaluate, due to the complexity of the related detectors, DAQ chain and software. Delphes is a new framework for fast simulation of a general purpose experiment. The simulation includes a tracking system, a magnetic field, calorimetry and a muon system, and possible very forward detectors arranged along the beamline. The framework is interfaced to standard file format from event generators and outputs observable analysis data objects. The simulation takes into account the detector resolutions, usual reconstruction algorithms for complex objects (FastJet) and a simplified trigger emulation. Detection of very forward scattered particles relies on the transport in beamlines with the Hector software.
Machine-learning Optimized Design of Experiments
Luigi Favaro, 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 also participate to the very closely related activities of the "Codesign" work package in the EUCAIF network.
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: See updated list here: https://mode-collaboration.github.io/ For EUCAIF, see: https://eucaif.org/.
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 also participate to the very closely related activities of the "Codesign" work package in the EUCAIF network.
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: See updated list here: https://mode-collaboration.github.io/ For EUCAIF, see: https://eucaif.org/.
Non-active projects
Publications in IRMP