Speaker
Description
Optimization of detector design using AI/DL, either through exploiting fully differentiable programming models or through the help of intelligent agents has been a growing field of interest in the recent years. This was spearheaded by the MODE collaboration (https://mode-collaboration.github.io/) and other research groups (https://doi.org/10.3390/particles8020047). While full end-to-end design of experimental setups has shown to be extremely challenging due to the many (often non-differentiable) constraints, AI/DL can be a huge asset in the optimization of detector design parameters within certain boundaries.
In ATLAS, particularly, the potential replacement of radiation damaged components after Run-4 offers a great possibility to leverage AI/DL in order to find optimal design parameters that take multiple inputs into account. We propose to use the case study of the replacement of the two innermost pixel layers of the ATLAS ITk, anticipated to take place in the long shutdown before Run-5 of the HL-LHC campaign, to develop the tools and models that allow to explore novel technologies and design phase space. We plan to integrate this with a rapid feedback loop including novel reconstruction/regression models and potential physics performance of the different detector concepts. In a second step, we plan to generalize the developed workflows to a more heuristic approach of physics experiment design. We then try to target design aspects of the future FCC detector concepts, and while we plan to first focus on tracking detectors, but - if applicable - plan to extend the process beyond the tracking devices. Particularly drawing from the experience and strong involvement in the design, construction and operation of the ATLAS LAr Calorimeters, the CERN ATLAS Team is leading the R&D on noble-liquid ionization calorimetry for future colliders. The work is currently performed within ALLEGRO (https://allegro.web.cern.ch/), a general-purpose detector concept for the future FCC-ee accelerator, We anticipate to integrate reconstruction, computational and physics performance into the optimization process.
CERN group/ Experiment
CERN ATLAS Team
| Working area | Area 7: Experimental Technologies |
|---|---|
| Project goals | Demonstrate the potential of using AI/DL for achieving the optimal design of detector components first within a strongly constraint phase space, prepare more general approaches for detector design, including integrated feedback loops from reconstruction and physics performance evaluations. |
| Timeline | Year 1: review of existing strategies for AI/DL assisted detector design, and implementation of a first 2 layer optimization testbed (eventually using the Open Data Detector). Year 2: establishment of a full optimization cycle and application to the ATLAS ITk innermost layer replacement, generalization to other detector devices, and detector (e.g. FCC) detectors Year 3: application on FCC detector design optimization |
| Available person power | 0.5 FTE |
| Additional person power request | 36 GRAP months, 36 DOCT months |
| Is this an already ongoing activity? | No |
| Indicative hardware resources needs | Training resources and resources for parameter optimization (GPU allocations), CPU resources needed for full simulation |