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International audience; Future high energy particle colliders are under study, with a first goal of 16 T dipoles, which is believed to be the practical limit of Nb3Sn magnets. Another more ambitious goal is to aim for 20 T dipoles. This very high field would require High Temperature Superconductors (HTS), such as Bi2212 or REBCO. Their substantially higher cost necessitate anyways the use of Nb3Sn for an affordable accelerator application. Therefore, hybrid designs can be proposed, where the HTS are used in the high field (16–20 T) area, and Nb3Sn are used in the low field (3Sn-HTS design generating 20 T in the bore with margin, using a block-coil concept. Several conductor options are discussed. The design also proposes stress-management solutions to deal with the large stress developing in the coils.

The radiation pattern within high energy quark- and gluon-initiated jets (jet substructure) is used extensively as a precision probe of the strong force as well as an environment for optimizing event generators with numerous applications in high energy particle and nuclear physics. Looking at electron-proton collisions is of particular interest as many of the complications present at hadron colliders are absent. A detailed study of modern jet substructure observables, jet angularities, in electron-proton collisions is presented using data recorded using the H1 detector at HERA. The measurement is unbinned and multi-dimensional, using machine learning to correct for detector effects. All of the available reconstructed object information of the respective jets is interpreted by a graph neural network, achieving superior precision on a selected set of jet angularities. Training these networks was enabled by the use of a large number of GPUs in the Perlmutter supercomputer at Berkeley Lab. The particle jets are reconstructed in the laboratory frame, using the $k_{\mathrm{T}}$ jet clustering algorithm. Results are reported at high transverse momentum transfer $Q^2>150$ GeV${}^2$, and inelasticity $0.2 < y < 0.7$. The analysis is also performed in sub-regions of $Q^2$, thus probing scale dependencies of the substructure variables. The data are compared with a variety of predictions and point towards possible improvements of such models., 30 pages, 10 figures, 8 tables, corrected authorlist

The ATLAS experiment measures the properties of particles that are products of proton-proton collisions at the LHC. The ATLAS detector will undergo a major upgrade before the high luminosity phase of the LHC. The ATLAS liquid argon calorimeter measures the energy of particles interacting electromagnetically in the detector. The readout electronics of this calorimeter will be replaced during the aforementioned ATLAS upgrade. The new electronic boards will be based on state-of-the-art field-programmable gate arrays (FPGA) from Intel allowing the implementation of neural networks embedded in firmware. Neural networks have been shown to outperform the current optimal filtering algorithms used to compute the energy deposited in the calorimeter. This article presents the implementation of a recurrent neural network (RNN) allowing the reconstruction of the energy deposited in the calorimeter on Stratix 10 FPGAs. The implementation in high level synthesis (HLS) language allowed fast prototyping but fell short of meeting the stringent requirements in terms of resource usage and latency. Further optimisations in Very High-Speed Integrated Circuit Hardware Description Language (VHDL) allowed fulfilment of the requirements of processing 384 channels per FPGA with a latency smaller than 125 ns., 14 pages, 10 figures

International audience; In the framework of studies for high energy particle colliders, design concepts for high field dipoles are being explored. In particular, relatively compact 20 T magnets can be achieved in a hybrid configuration, combining a High Temperature Superconductor (HTS) and a Low Temperature Superconductor (LTS). Preliminary concepts have been previously proposed using Bi2212 for the HTS and Nb3Sn for the LTS. One of the main difficulties of 20 T magnets is the management of the very high stresses developing during operation. The design concepts rely on a rectangular block-coil layout, which offers the advantage of aligning the conductors with the main magnetic field, therefore submitting the conductors to a perpendicular electromagnetic force for a better control of the stresses. In addition, the layout allows a specific stress management, with adequate horizontal and vertical plates to intercept the stresses. The paper presents the improvements provided to the initial concept. In terms of magnetic design, the field quality has been improved. In terms of mechanical design, the stress management has been optimized to provide a compact coil with a reduced peak stress on the HTS. Concepts for flared-end coils with joints in the coil-ends are finally presented.

The radiation pattern within high energy quark- and gluon-initiated jets (jet substructure) is used extensively as a precision probe of the strong force as well as an environment for optimizing event generators with numerous applications in high energy particle and nuclear physics. Looking at electron-proton collisions is of particular interest as many of the complications present at hadron colliders are absent. A detailed study of modern jet substructure observables, jet angularities, in electron-proton collisions is presented using data recorded using the H1 detector at HERA. The measurement is unbinned and multi-dimensional, using machine learning to correct for detector effects. All of the available reconstructed object information of the respective jets is interpreted by a graph neural network, achieving superior precision on a selected set of jet angularities. Training these networks was enabled by the use of a large number of GPUs in the Perlmutter supercomputer at Berkeley Lab. The particle jets are reconstructed in the laboratory frame, using the $k_{\mathrm{T}}$ jet clustering algorithm. Results are reported at high transverse momentum transfer $Q^2>150$ GeV${}^2$, and inelasticity $0.2 < y < 0.7$. The analysis is also performed in sub-regions of $Q^2$, thus probing scale dependencies of the substructure variables. The data are compared with a variety of predictions and point towards possible improvements of such models.