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A key challenge in particle accelerators is to achieve high peak intensity. Space charge is particularly strong at lower energy such as during injection and typically limits achievable peak intensity. The beam stacking technique can overcome this limitation by accumulating a beam at high energy where space charge is weaker. In beam stacking, a bunch of particles is injected and accelerated to high energy. This bunch continues to circulate, while a second and subsequent bunches are accelerated to merge into the first. It also allows the user cycle and acceleration cycles to be separated which is often valuable. Beam stacking is not possible in a time varying magnetic field, but a fixed field machine such as an Fixed Field Alternating Gradient Accelerator (FFA) does not sweep the magnetic field. In this paper, we describe experimental demonstration of beam stacking of two beams at KURNS FFA in Kyoto University. The momentum spread and intensity of the beam was analysed by study of the Schottky signal, demonstrating stacking with only a slight increase of momentum spread of the combined beams. The intensity of the first beam was, however, significantly reduced. RF knock-out is the suspected source of the beam loss.

A key challenge in particle accelerators is to achieve high peak intensity. Space charge is particularly strong at lower energy such as during injection and typically limits achievable peak intensity. The beam stacking technique can overcome this limitation by accumulating a beam at high energy where space charge is weaker. In beam stacking, a bunch of particles is injected and accelerated to high energy. This bunch continues to circulate, while a second and subsequent bunches are accelerated to merge into the first. It also allows the user cycle and acceleration cycles to be separated which is often valuable. Beam stacking is not possible in a time varying magnetic field, but a fixed field machine such as an Fixed Field Alternating Gradient Accelerator (FFA) does not sweep the magnetic field. In this paper, we describe experimental demonstration of beam stacking of two beams at KURNS FFA in Kyoto University. The momentum spread and intensity of the beam was analysed by study of the Schottky signal, demonstrating stacking with only a slight increase of momentum spread of the combined beams. The intensity of the first beam was, however, significantly reduced. RF knock-out is the suspected source of the beam loss.

Computer simulations are invaluable tools for scientific discovery. However, accurate simulations are often slow to execute, which limits their applicability to extensive parameter exploration, large-scale data analysis, and uncertainty quantification. A promising route to accelerate simulations by building fast emulators with machine learning requires large training datasets, which can be prohibitively expensive to obtain with slow simulations. Here we present a method based on neural architecture search to build accurate emulators even with a limited number of training data. The method successfully emulates simulations in 10 scientific cases including astrophysics, climate science, biogeochemistry, high energy density physics, fusion energy, and seismology, using the same super-architecture, algorithm, and hyperparameters. Our approach also inherently provides emulator uncertainty estimation, adding further confidence in their use. We anticipate this work will accelerate research involving expensive simulations, allow more extensive parameters exploration, and enable new, previously unfeasible computational discovery.

The ESSnuSB experiment aims to measure the leptonic CP phase \\(\\delta_{CP}\\) with an unprecedented resolution by probing neutrino oscillations at the second oscillation maximum. In the present work, the complementarity between the long-baseline neutrino program and atmospheric neutrinos is investigated for ESSnuSB. By simulating atmospheric neutrino events equivalent of 5.4 Mt\\(\\cdot\\)year exposure, the resolution for \\(\\delta_{\\rm CP}^{}\\) is found to improve from \\(7.5^\\circ\\) (\\(6.7^\\circ\\)) to \\(7.1^\\circ\\) (\\(6.5^\\circ\\)) at \\(1\\sigma\\)~CL for \\(\\delta_{\\rm CP}^{} = -90^\\circ\\) (\\(+90^\\circ\\)) with respect to super-beam neutrinos, resolving also the degeneracies arising from neutrino mass ordering. These findings highlight the synergies that exist between super-beam neutrinos and atmospheric neutrinos in ESSnuSB.

The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accelerator complex, detectors and physics for a future muon collider. In 2023, European Commission support was obtained for a design study of a muon collider (MuCol) [3]. This project started on 1st March 2023, with work-packages aligned with the overall muon collider studies. In preparation of and during the 2021-22 U.S. Snowmass process, the muon collider project parameters, technical studies and physics performance studies were performed and presented in great detail. Recently, the P5 panel [4] in the U.S. recommended a muon collider R&D, proposed to join the IMCC and envisages that the U.S. should prepare to host a muon collider, calling this their \"muon shot\". In the past, the U.S. Muon Accelerator Programme (MAP) [5] has been instrumental in studies of concepts and technologies for a muon collider.

The International Muon Collider Collaboration (IMCC) [1] was established in 2020 following the recommendations of the European Strategy for Particle Physics (ESPP) and the implementation of the European Strategy for Particle Physics-Accelerator R&D Roadmap by the Laboratory Directors Group [2], hereinafter referred to as the the European LDG roadmap. The Muon Collider Study (MuC) covers the accelerator complex, detectors and physics for a future muon collider. In 2023, European Commission support was obtained for a design study of a muon collider (MuCol) [3]. This project started on 1st March 2023, with work-packages aligned with the overall muon collider studies. In preparation of and during the 2021-22 U.S. Snowmass process, the muon collider project parameters, technical studies and physics performance studies were performed and presented in great detail. Recently, the P5 panel [4] in the U.S. recommended a muon collider R&D, proposed to join the IMCC and envisages that the U.S. should prepare to host a muon collider, calling this their \"muon shot\". In the past, the U.S. Muon Accelerator Programme (MAP) [5] has been instrumental in studies of concepts and technologies for a muon collider.

Ionic structure of high pressure, high temperature fluids is a challenging theoretical problem with applications to planetary interiors and fusion capsules. Here we report a multi-messenger platform using velocimetry and in situ angularly and spectrally resolved X-ray scattering to measure the thermodynamic conditions and ion structure factor of materials at extreme pressures. We document the pressure, density, and temperature of shocked silicon near 100 GPa with uncertainties of 6%, 2%, and 20%, respectively. The measurements are sufficient to distinguish between and rule out some ion screening models.

Computer simulations are invaluable tools for scientific discovery. However, accurate simulations are often slow to execute, which limits their applicability to extensive parameter exploration, large-scale data analysis, and uncertainty quantification. A promising route to accelerate simulations by building fast emulators with machine learning requires large training datasets, which can be prohibitively expensive to obtain with slow simulations. Here we present a method based on neural architecture search to build accurate emulators even with a limited number of training data. The method successfully accelerates simulations by up to 2 billion times in 10 scientific cases including astrophysics, climate science, biogeochemistry, high energy density physics, fusion energy, and seismology, using the same super-architecture, algorithm, and hyperparameters. Our approach also inherently provides emulator uncertainty estimation, adding further confidence in their use. We anticipate this work will accelerate research involving expensive simulations, allow more extensive parameters exploration, and enable new, previously unfeasible computational discovery.

Our understanding of physical systems generally depends on our ability to match complex computational modelling with measured experimental outcomes. However, simulations with large parameter spaces suffer from inverse problem instabilities, where similar simulated outputs can map back to very different sets of input parameters. While of fundamental importance, such instabilities are seldom resolved due to the intractably large number of simulations required to comprehensively explore parameter space. Here we show how Bayesian machine learning can be used to address inverse problem instabilities, and apply it to two popular experimental diagnostics in plasma physics. We find that the extraction of information from measurements simply on the basis of agreement with simulations is unreliable, and leads to a significant underestimation of uncertainties. We describe how to statistically quantify the effect of unstable inverse models, and describe an approach to experimental design that mitigates its impact.