Calorimeters for the FCC-hh

Abstract

The future proton-proton collider (FCC-hh) will deliver collisions at a center of mass energy up to √ s = 100 TeV at an unprecedented instantaneous luminosity of L = 3 10 35 cm −2 s −1 , resulting in extremely challenging radiation and luminosity conditions. By delivering an integrated luminosity of few tens of ab −1 , the FCC-hh will provide an unrivalled discovery potential for new physics. Requiring high sensitivity for resonant searches at masses up to tens of TeV imposes strong constraints on the design of the calorimeters. Resonant searches in final states containing jets, taus and electrons require both excellent energy resolution at multi-TeV energies as well as outstanding ability to resolve highly collimated decay products resulting from extreme boosts. In addition, the FCC-hh provides the unique opportunity to precisely measure the Higgs self-coupling in the di-photon and b-jets channel. Excellent photon and jet energy resolution at low energies as well as excellent angular resolution for pion background rejection are required in this challenging environment. This report describes the calorimeter studies for a multi-purpose detector at the FCChh. The calorimeter active components consist of Liquid Argon (LAr), scintillating plastic tiles (Tile) and Monolithic Active Pixel Sensors (MAPS) technologies. The technological choices, design considerations and achieved performances in full Geant4 simulations are discussed and presented.

The Future Circular Collider: a Summary for the US 2021 Snowmass Process

“…Highly granular noble-liquid sampling calorimetry was proposed for a possible FCC-hh experiment [7,231,232]. It has been shown that, on top of its intrinsic excellent electromagnetic energy resolution, noble-liquid calorimetry can be optimized in terms of granularity to allow for 4D imaging, machine learning or, in combination with the tracker measurements, particle-flow reconstruction.…”

“…Currently, a granularity of ∆θ × ∆ϕ = 2.5 mrad × 8.2 mrad (5.4 mm × 17.7 mm) is foreseen in the first calorimeter compartment to optimize the π 0 rejection. Single-particle simulations of electrons and photons have resulted in a stochastic term of 8.2 % [231,232] for the standalone electromagnetic energy resolution. Exploiting the full 4D imaging information, it was demonstrated that a deep-neural-network analysis can achieve a single-π − resolution stochastic term of 37 % (B = 4 T) for such a highly granular noble-liquid calorimeter complemented with a scintillator-iron HCAL.…”

Toward the End-to-End Optimization of Particle Physics Instruments with Differentiable Programming: a White Paper

“…Machine-learning algorithms can offer this flexibility as they are typically differentiable by construction, and can also easily adapt to changing conditions. However, neither those algorithms that rely on a regular geometry (such as convolutional neural network based approaches [161][162][163][164][165]), nor algorithms based on dense neural networks alone that make no assumptions at all on the structure of the problem, are applicable: the former cannot generalize to irregular geometries, and the latter cannot fit resource constraints. In addition, recurrent algorithms cannot be made really generic, as conceptually they rely on a certain ordering of the inputs.…”

Measuring the Higgs self-coupling via Higgs-pair production at a 100 TeV p-p collider

“…A prototype of a baseline FCC-hh detector that could fulfill the above requirements has been designed by the FCC-hh collaboration [70][71][72]. The detector has a diameter of 20 m and a length of 50 m, with dimensions comparable to the ATLAS detector.…”