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We present the detection of directional muon beams produced using a PW laser at the Lawrence Berkeley National Laboratory. The muon source is a multi-GeV electron beam generated in a 30 cm laser plasma accelerator interacting with a high-Z converter target. The GeV photons resulting from the interaction are converted into a high-flux, directional muon beam via pair production. By employing scintillators to capture delayed events, we were able to identify the produced muons and characterize the source. Using theoretical knowledge of the muon production process combined with simulations that show outstanding agreement with the experiments, we demonstrate that the multi-GeV electron beams produce muon beams with GeV energies and fluxes, at a few meters from the source, up to 4 orders of magnitude higher than cosmic ray muons. Laser-plasma-accelerator-based muon sources can therefore enhance muon imaging applications thanks to their compactness, directionality, and high fluxes which reduce the exposure time by orders of magnitude compared to cosmic ray muons. Using the Geant4-based simulation code we developed to gain insight into the experimental results, we can design future experiments and applications based on LPA-generated muons.

The development of ultra-low emittance storage rings, such as the e+/e- Future Circular Collider (FCC-ee) with a circumference of about 90 km, aims to achieve unprecedented luminosity and beam size. One significant challenge is correcting the optics, which becomes increasingly difficult as we target lower emittances. In this paper, we investigate optics correction methods to address these challenges. We examined the impact of arc region magnet alignment errors in the baseline optics for the FCC-ee lattice at Z energy. To establish realistic alignment tolerances, we developed a sequence of correction steps using the Python Accelerator Toolbox (PyAT) to correct the lattice optics, achieve the nominal emittance, Dynamic Aperture (DA), and in the end, the design luminosity. The correction scheme has been recently optimized and better machine performance demonstrated. A comparison was conducted between two optics correction approaches: Linear Optics from Closed Orbits (LOCO) with phase advance + $\eta_x$ and coupling Resonance Driving Terms (RDTs) + $\eta_y$. The latter method demonstrated better performance in achieving the target emittance and enhancing the DA., Comment: Appears in the proceedings of the 14th International Computational Accelerator Physics Conference (ICAP'24), 2-5 October 2024, Germany

Efficient accelerator modeling and particle tracking are key for the design and configuration of modern particle accelerators. In this work, we present JUTRACK, a nested accelerator modeling package developed in Julia programing language and enhanced with compiler-level automatic differentiation (AD). With the aid of AD, JUTRACK enables rapid derivative calculations in accelerator modeling, facilitating sensitivity analyses and optimization tasks. We demonstrate the effectiveness of AD-derived derivatives through several practical applications, including sensitivity analysis of space-charge-induced emittance growth, nonlinear beam dynamics analysis for a synchrotron light source, and lattice parameter tuning of the future Electron-Ion Collider (EIC). Through the incorporation of automatic differentiation, this package opens up new possibilities for accelerator physicists in beam physics studies and accelerator design optimization.

The commissioning and operation of future large-scale scientific experiments will challenge current tuning and control methods. Reinforcement learning (RL) algorithms are a promising solution thanks to their capability of autonomously tackling a control problem based on a task parameterized by a reward function. The conventionally utilized machine learning (ML) libraries are not intended for microsecond latency applications, as they mostly optimize for throughput performance. On the other hand, most of the programmable logic implementations are meant for computation acceleration, not being intended to work in a real-time environment. To overcome these limitations of current implementations, RL needs to be deployed on-the-edge, i.e. on to the device gathering the training data. In this paper we present the design and deployment of an experience accumulator system in a particle accelerator. In this system deep-RL algorithms run using hardware acceleration and act within a few microseconds, enabling the use of RL for control of ultra-fast phenomena. The training is performed offline to reduce the number of operations carried out on the acceleration hardware. The proposed architecture was tested in real experimental conditions at the Karlsruhe research accelerator (KARA), serving also as a synchrotron light source, where the system was used to control induced horizontal betatron oscillations in real-time. The results showed a performance comparable to the commercial feedback system available at the accelerator, proving the viability and potential of this approach. Due to the self-learning and reconfiguration capability of this implementation, its seamless application to other control problems is possible. Applications range from particle accelerators to large-scale research and industrial facilities.

In this Letter we report on the experimental generation of high energy (10 GeV), ultra-short (fs-duration), ultra-high current (0.1 MA), petawatt peak power electron beams in a particle accelerator. These extreme beams enable the exploration of a new frontier of high intensity beam-light and beam-matter interactions broadly relevant across fields ranging from laboratory astrophysics to strong field quantum electrodynamics and ultra-fast quantum chemistry. We demonstrate our ability to generate and control the properties of these electron beams by means of a laser-electron beam shaping technique. This experimental demonstration opens the door to on-the-fly customization of extreme beam current profiles for desired experiments and is poised to benefit a broad swathe of cross-cutting applications of relativistic electron beams.

In a plasma wakefield accelerator driven by a train of short particle bunches, it is possible to locally increase the acceleration rate by introducing a small negative gradient of the plasma density. A regime is possible in which the gradient affects only the relative phasing of the driver bunches and the wave, keeping the wave phase behind the driver stable. With this technique, it is possible to increase the energy gain of the accelerated witness bunch in a plasma section of limited length.

To mitigate the BBU instability and improve characteristics of accelerated bunches in Dielectric Wakefield Accelerator one can be used the isotropic plasma filling of the transport channel. Here we present the results of analytical and numerical studies of the dynamics of accelerated electron/positron and drive electron bunches under wake acceleration in a plasma DWA (PDWA) with a vacuum channel. The wake field is excited by an electron bunch in a quartz dielectric tube inserted into a cylindrical metal waveguide. The inner region of the dielectric tube is filled with plasma with a vacuum channel along the waveguide axis. At the numerical simulations the energy and spatial characteristics, efficiency, emittance, and energy spread for accelerated positron and electron bunches is studied for different radii of the vacuum channel. The transverse instability of the drive bunch in PDWA is studied analytically and numerically. The analytical studies have discovered the presence of one surface and one bulk eigenwaves, which are absent in corresponding dielectric-loaded waveguide without plasma filling. The main contribution to amplitude of transverse wakefield, responsible for stabilization of transverse motion of bunches, brings the bulk plasma eigenwave. The comparative analysis of the data resulting from analytical studies and the ones obtained by numerical simulation has demonstrated qualitative agreement between the results., Comment: 33 pages, 13 fugures, AAC2024 Worhshop, will be submitted to Nuclear Instruments and Methods in Physics Research

We propose a novel scheme for controlling the injection of a high-quality electron bunch into a channel-guided laser plasma accelerator. This all-optical technique, constricted waveguide injection, creates a highly tunable controlled injection structure natively within a plasma waveguide, a key requirement for efficient acceleration of high-quality multi-GeV electron beams. We describe a simple optical setup to tailor the plasma and present start-to-end simulations showing the injection structure formation and the generation of a 1.1 GeV electron beam with 10 pC of charge and 0.35 % energy spread using 1 J of drive laser energy. Highly tunable tailored plasma sources, like those proposed here, enable fine control over the injection and acceleration processes and thus will be crucial for the development of application-focused laser plasma accelerators., Comment: 6 pages, 3 figures

If a laser- or particle beam-driven plasma wakefield accelerator operates in the linear or moderately nonlinear regime, injecting an externally produced particle bunch (witness) to be accelerated may encounter an alignment problem. Witness alignment tolerances can be relaxed by using a damper, an additional particle bunch produced by the same injector and propagating at a submillimeter distance ahead of the witness. If misaligned, the damper perturbs the wakefield in such a way that the witness shifts on-axis with no quality loss., Comment: 5 pages, 6 figures

Laser-Wakefield Accelerators (LWFA) running at kHz repetition rates hold great potential for applications. They typically operate with low-energy, highly compressed laser pulses focused in high-pressure gas targets. Experiments have shown that the best-quality electron beams are achieved using Hydrogen gas targets. However, continuous operation with Hydrogen requires a dedicated pumping system. This work presents the design of a differential pumping system, enabling, for the first time, continuous operation of our kHz LWFA using a high-pressure Hydrogen gas jet. The system successfully maintained a pressure below 3e-4 mbar, even with a free-flowing gas jet operating at 140 bar backing pressure. Numerical fluid dynamics and optical simulations were used to guide and validate the system's design., Comment: 10 pages, 9 figures

This review paper describes the energy-upgraded CEBAF accelerator. This superconducting linac has achieved 12 GeV beam energy by adding 11 new high-performance cryomodules containing eighty-eight superconducting cavities that have operated CW at an average accelerating gradient of 20 MV/m. After reviewing the attributes and performance of the previous 6 GeV CEBAF accelerator, we discuss the upgraded CEBAF accelerator system in detail with particular attention paid to the new beam acceleration systems. In addition to doubling the acceleration in each linac, the upgrade included improving the beam recirculation magnets, adding more helium cooling capacity to allow the newly installed modules to run cold, adding a new experimental hall, and improving numerous other accelerator components. We review several of the techniques deployed to operate and analyze the accelerator performance, and document system operating experience and performance. In the final portion of the document, we present much of the current planning regarding projects to improve accelerator performance and enhance operating margins, and our plans for ensuring CEBAF operates reliably into the future. For the benefit of potential users of CEBAF, the performance and quality measures for beam delivered to each of the experimental halls is summarized in the appendix., Comment: 66 pages, 73 figures, 21 tables

The design and optimization of laser-Compton x-ray systems based on compact distributed charge accelerator structures can enable micron-scale imaging of disease and the concomitant production of beams of Very High Energy Electrons (VHEEs) capable of producing FLASH-relevant dose rates. The physics of laser-Compton x-ray scattering ensures that the scattered x-rays follow exactly the trajectory of the incident electrons, thus providing a route to image-guided, VHEE FLASH radiotherapy. The keys to a compact architecture capable of producing both laser-Compton x-rays and VHEEs are the use of X-band RF accelerator structures which have been demonstrated to operate with over 100 MeV/m acceleration gradients. The operation of these structures in a distributed charge mode in which each radiofrequency (RF) cycle of the drive RF pulse is filled with a low-charge, high-brightness electron bunch is enabled by the illumination of a high-brightness photogun with a train of UV laser pulses synchronized to the frequency of the underlying accelerator system. The UV pulse trains are created by a patented pulse synthesis approach which utilizes the RF clock of the accelerator to phase and amplitude modulate a narrow band continuous wave (CW) seed laser. In this way it is possible to produce up to 10 {\mu}A of average beam current from the accelerator. Such high current from a compact accelerator enables production of sufficient x-rays via laser-Compton scattering for clinical imaging and does so from a machine of "clinical" footprint. At the same time, the production of 1000 or greater individual micro-bunches per RF pulse enables > 10 nC of charge to be produced in a macrobunch of < 100 ns. The design, construction, and test of the 100-MeV class prototype system in Irvine, CA is also presented., Comment: 28 pages, 13 figures, 2 tables

The Low-Level RF (LLRF) control circuits of linear accelerators (LINACs) are conventionally realized with heterodyne based architectures, which have analog RF mixers for up and down conversion with discrete data converters. We have developed a new LLRF platform for C-band linear accelerator based on the Frequency System-on-Chip (RFSoC) device from AMD Xilinx. The integrated data converters in the RFSoC can directly sample the RF signals in C-band and perform the up and down mixing digitally. The programmable logic and processors required for signal processing for the LLRF control system are also included in a single RFSoC chip. With all the essential components integrated in a device, the RFSoC-based LLRF control platform can be implemented more cost-effectively and compactly, which can be applied to a broad range of accelerator applications. In this paper, the structure and configuration of the newly developed LLRF platform will be described. The LLRF prototype has been tested with high power test setup with a Cool Cooper Collider (C\(^3\)) accelerating structure. The LLRF and the solid state amplifier (SSA) loopback setup demonstrated phase jitter in 1 s as low as 115 fs, which is lower than the requirement of C\(^3\). The rf signals from the klystron forward and accelerating structure captured with peak power up to 16.45 MW will be presented and discussed.

The upcoming long baseline neutrino experiments aim to enhance proton beam power to multi-MW scale and utilize large-scale detectors to address the challenge of limited event statistics. The DUNE experiment at LBNF will test the three neutrino flavor paradigm and directly search for CP violation by studying oscillation signatures in the high intensity $\nu_{\mu}$ (anti-$\nu_{\mu}$) beam to $\nu_{e}$ (anti-$\nu_{e}$) measured over a long baseline. Higher beam power and improved accelerator up-time will enhance neutrino flux for the neutrino program by increasing the number of protons on target. LBNF/DUNE, as well as PIP-II upgrade and Accelerator Complex Evolution (ACE) plan, play a vital role in this effort. The scientific potential of ACE plan extends beyond neutrino physics, encompassing endeavors such as the Muon Collider, Charged Lepton Flavor Violation (CLFV), Dark Sectors, and exploration of neutrinos beyond DUNE.\par In the era of higher-power accelerator operation , research in target materials and beam instrumentation is crucial for optimizing design modifications. This abstract discusses Fermilab ACE, the science opportunities it provides, and how Fermilab is pushing the limits of proton beam power and accelerator infrastructure. By tackling neutrino beam challenges and exploring research and development ideas, we are advancing our understanding of fundamental particles and their interactions., Comment: 22nd Conference on Flavor Physics and CP Violation (FPCP 2024)

A major focus of the global HEP community is on high field superconducting magnets made of High Temperature Superconductors (HTS) for future Energy Frontier Programs. Within the U.S. Magnet Development Program (US MDP), a key task is that of developing HTS inserts producing fields larger than 5 T within 15 T outserts made of Nb$_3$Sn to generate 20 T+ for future accelerators. Bi$_2$Sr$_2$CaCu$_2$O$_8$$_-$$_x$ (BiSCO) is the only high T c superconductor available as an isotropic round multifilamentary wire, which is ideal for producing the flat cables (i.e., Rutherford type cables) that are used in accelerator magnets. Significant progress in the development and industrialization of BiSCO wires has been made over the last decade. However, several challenges remain for this HTS to be used successfully in hybrid magnets. The following is required to improve performance, lower costs and simplify the processing of BiSCO accelerator magnets: (1) The development and design, in collaboration with industry, of BiSCO wires that are adequate for Rutherford cabling; (2) The development of insulation processes and materials that prevent leaks when heat treated in highly corrosive oxygen; (3) Control of stresses and strains; and (4) Integration of high pressure heat treatment with a new approach that will lower costs and simplify processing., Comment: 14th International Particle Accelerator Conference (IPAC'23)

One of the primary goals of the Center for Accelerator Target Science (CATS) is to provide targets and foils in support of the ATLAS User Facility and the Low-Energy community at large. While a wide array of target production techniques are available at CATS, new methods that must be explored invariably arise. One such technique, the High-Intensity Vibrational Powder Plating (HIVIPP), was first reported in 1997 by Isao Sugai. It was developed to produce targets and stripper foils that were difficult to make by standard methods. At Argonne National Laboratory (ANL), we have successfully constructed and tested a simple system for this purpose. We have produced targets of carbon and titanium on various metal backings using the HIVIPP method. We are currently in the exciting phase of exploring the production of other elements, including isotopically enriched and radioactive material. This work is in progress and will be further detailed with specific examples., Comment: 9 pages, 4 figures, 2 tables, Argonne National Lab

In this paper, we propose to combine two promising research topics in accelerator physics, i.e., optical stochastic cooling (OSC) and steady-state microbunching (SSMB). The motivation is to provide a powerful radiation source which could benefit fundamental science research and industry applications. Our study shows that such a compact OSC-SSMB storage ring using present technology can deliver EUV light with an average power of kilowatt, and spectral flux $>10^{20}$ phs/s/0.1\%b.w., which is four orders of magnitude higher than existing synchrotron sources. It is expected that the presented work is of value for the development of both OSC and SSMB., Comment: 12 pages, 3 figures

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., Comment: 14 pages, 16 figures

Advanced accelerator-based light sources such as free electron lasers (FEL) accelerate highly relativistic electron beams to generate incredibly short (10s of femtoseconds) coherent flashes of light for dynamic imaging, whose brightness exceeds that of traditional synchrotron-based light sources by orders of magnitude. FEL operation requires precise control of the shape and energy of the extremely short electron bunches whose characteristics directly translate into the properties of the produced light. Control of short intense beams is difficult due to beam characteristics drifting with time and complex collective effects such as space charge and coherent synchrotron radiation. Detailed diagnostics of beam properties are therefore essential for precise beam control. Such measurements typically rely on a destructive approach based on a combination of a transverse deflecting resonant cavity followed by a dipole magnet in order to measure a beam's 2D time vs energy longitudinal phase-space distribution. In this paper, we develop a non-invasive virtual diagnostic of an electron beam's longitudinal phase space at megapixel resolution (1024 x 1024) based on a generative conditional diffusion model. We demonstrate the model's generative ability on experimental data from the European X-ray FEL.

We show experimentally that an effect of motion of ions, observed in a plasma-based accelerator, depends inversely on the plasma ion mass. The effect appears within a single wakefield event and manifests itself as a bunch tail, occurring only when sufficient motion of ions suppresses wakefields. Wakefields are driven resonantly by multiple bunches, and simulation results indicate that the ponderomotive force causes the motion of ions. In this case, the effect is also expected to depend on the amplitude of the wakefields, experimentally confirmed through variations in the drive bunch charge.

Particle accelerators are time-varying systems whose components are perturbed by external disturbances. Tuning accelerators can be a time-consuming process involving manual adjustment of multiple components, such as RF cavities, to minimize beam loss due to time-varying drifts. The high dimensionality of the system ($\sim$100 amplitude and phase RF settings in the LANSCE accelerator) makes it difficult to achieve optimal operation. The time-varying drifts and the dimensionality make system parameter estimation a challenging optimization problem. In this work, we propose a Variational Autoencoded Latent Regression (VALeR) model for robust estimation of system parameters using 2D unique projections of a charged particle beam's 6D phase space. In VALeR, VAE projects the phase space projections into a lower-dimensional latent space, and a dense neural network maps the latent space onto the space of system parameters. The trained network can predict system parameters for unseen phase space projections. Furthermore, VALeR can generate new projections by randomly sampling the latent space of VAE and also estimate the corresponding system parameters.

The optimisation of the plasma target design for high quality beam laser-driven plasma injector electron source relies on numerical parametric studies using Particle in Cell (PIC) codes. The common input parameters to explore are laser characteristics and plasma density profiles extracted from computational fluid dynamic studies compatible with experimental measurements of target plasma density profiles. We demonstrate the construction of surrogate models using machine learning technique for a laser-plasma injector (LPI) electron source based on more than 12000 simulations of a laser wakefield acceleration performed for sparsely spaced input parameters [1]. Surrogate models are very interesting for LPI design and optimisation because they are much faster than PIC simulations. We develop and compare the performance of three surrogate models, namely, Gaussian processes (GP), multilayer perceptron (MLP), and decision trees (DT). We then use the best surrogate model to quickly find optimal working points to get a selected electron beam energy, charge and energy spread using different methods, namely random search, Bayesian optimisation and multi-objective Bayesian optimisation, Comment: 10 pages, 13 figures

As beam power continues to increase in next-generation accelerator facilities, high-power target systems face crucial challenges. Components like beam windows and particle-production targets must endure significantly higher levels of particle fluence. The primary beam's energy deposition causes rapid heating (thermal shock) and induces microstructural changes (radiation damage) within the target material. These effects ultimately deteriorate the components' properties and lifespan. With conventional materials already stretched to their limits, we are exploring novel materials including High-Entropy Alloys and Electrospun Nanofibers that offer a fresh approach to enhancing tolerance against thermal shock and radiation damage. Following an introduction to the challenges facing high-power target systems, we will give an overview of the promising advancements we have made so far in customizing the compositions and microstructures of these pioneering materials. Our focus is on optimizing their in-beam thermomechanical and physics performance. Additionally, we will outline our ongoing plans for in-beam irradiation experiments and advanced material characterizations. The primary goal of this research is to push the frontiers of target materials, thereby enabling future multi-MW facilities that will benefit various programs in high-energy physics and beyond., Comment: IPAC'24 - 15th International Particle Accelerator Conference

We describe a technique for reconstruction of the four-dimensional transverse phase space of a beam in an accelerator beamline, taking into account the presence of unknown errors on the strengths of magnets used in the data collection. Use of machine learning allows rapid reconstruction of the phase-space distribution while at the same time providing estimates of the magnet errors. The technique is demonstrated using experimental data from CLARA, an accelerator test facility at Daresbury Laboratory., Comment: 31 pages, 18 figures

Autonomous tuning of particle accelerators is an active and challenging field of research with the goal of enabling novel accelerator technologies cutting-edge high-impact applications, such as physics discovery, cancer research and material sciences. A key challenge with autonomous accelerator tuning remains that the most capable algorithms require an expert in optimisation, machine learning or a similar field to implement the algorithm for every new tuning task. In this work, we propose the use of large language models (LLMs) to tune particle accelerators. We demonstrate on a proof-of-principle example the ability of LLMs to successfully and autonomously tune a particle accelerator subsystem based on nothing more than a natural language prompt from the operator, and compare the performance of our LLM-based solution to state-of-the-art optimisation algorithms, such as Bayesian optimisation (BO) and reinforcement learning-trained optimisation (RLO). In doing so, we also show how LLMs can perform numerical optimisation of a highly non-linear real-world objective function. Ultimately, this work represents yet another complex task that LLMs are capable of solving and promises to help accelerate the deployment of autonomous tuning algorithms to the day-to-day operations of particle accelerators., Comment: 22 pages, 5 figures

Large-scale machines like particle accelerators are usually run by a team of experienced operators. In case of a particle accelerator, these operators possess suitable background knowledge on both accelerator physics and the technology comprising the machine. Due to the complexity of the machine, particular subsystems of the machine are taken care of by experts, who the operators can turn to. In this work the reasoning and action (ReAct) prompting paradigm is used to couple an open-weights large language model (LLM) with a high-level machine control system framework and other tools, e.g. the electronic logbook or machine design documentation. By doing so, a multi-expert retrieval augmented generation (RAG) system is implemented, which assists operators in knowledge retrieval tasks, interacts with the machine directly if needed, or writes high level control system scripts. This consolidation of expert knowledge and machine interaction can simplify and speed up machine operation tasks for both new and experienced human operators.

We provide an overview of experiments exploring resonances in the collision of ultracold clouds of atoms. Using a laser-based accelerator that capitalizes on the energy resolution provided by the ultracold atomic setting, we unveil resonance phenomena such as Feshbach and shape resonances in their quintessential form by literally photographing the halo of outgoing scattered atoms. We exploit the tunability of magnetic Feshbach resonances to instigate an interplay between scattering resonances. By experimentally recording the scattering in a parameter space spanned by collision energy and magnetic field, we capture the imprint of the $S$-matrix pole flow in the complex energy plane. After revisiting experiments that place a Feshbach resonance in the proximity of a shape resonance and an anti-bound state, respectively, we discuss the possibility of using $S$-matrix pole interplay between two Feshbach resonances to create a bound-state-in-the-continuum., Comment: European Conference on Few-Body Problems in Physics (EFB25) 2023---Invited Plenary Talk

Low Level RF (LLRF) control systems of linear accelerators (LINACs) are typically implemented with heterodyne based architectures, which have complex analog RF mixers for up and down conversion. The Gen 3 Radio Frequency System-on-Chip (RFSoC) device from AMD Xilinx integrates data converters with maximum RF frequency of 6~GHz. This enables direct RF sampling of C-band LLRF signal typically operated at 5.712 GHz without any analogue mixers, which can significantly simplify the system architecture. The data converters sample RF signals in higher order Nyquist zones and then up or down convert digitally by the integrated data path in RFSoC. The closed-loop feedback control firmware implemented in FPGA integrated in RFSoC can process the base-band signal from the ADC data path and calculate the updated phase and amplitude to be up-mixed by the DAC data path. We have developed a C-band LLRF control RFSoC platform with direct RF sampling, which targets Cool Copper Collider (C3) and other C or S band LINAC research and development projects. In this paper, the architecture of the platform will be described. We have optimized the configuration of the data converter and characterized performance of them with RF pulses. The test results for some of the key performance parameters for the LLRF platform with our custom solid-state amplifier, such as phase and amplitude stability, will be discussed in this paper.

Plasma wakefields offer high acceleration gradients, orders of magnitude larger than conventional RF accelerators. However, the achievable luminosity remains relatively low, typically limited by the plasma recovery time and the charge which can be accelerated per shot. In this work, we show that a train of drive bunches can be harnessed to accelerate multiple witness bunches in a single shot. We demonstrate that periodically loading the wakefields removes the limit on the energy transfer from the drive beam to the plasma, which allows the luminosity to be increased. Proof-of-concept simulations for the AWAKE scheme are carried out to demonstrate the technique, achieving a doubling of the accelerated charge while exploiting only a fraction of the drive train., Comment: 5 pages, 4 figures

Compact laser-plasma acceleration of fast ions has made great strides since its discovery over two decades ago, resulting in the current generation of high-energy ($\geq 100\,\rm MeV$) ultracold beams over ultrashort ($\leq 1\,\rm ps$) durations. To unlock broader applications of these beams, we need the ability to tailor the ion energy spectrum. Here, we present a scheme that achieves precisely this by accelerating protons in a "lighthouse" fashion, whereby the highest-energy component of the beam is emitted in a narrow cone, well separated from the lower-energy components. This is made possible by a two-stage interaction in which the rear surface of the target is first set into rapid motion before the main acceleration phase. This approach offers the additional advantages of leveraging a robust sheath acceleration process in standard micron-thick targets and being optically controllable., Comment: 9 pages, 6 figures

Radio-frequency particle accelerators are engines of discovery, powering high-energy physics and photon science, but are also large and expensive due to their limited accelerating fields. Plasma-wakefield accelerators (PWFAs) provide orders-of-magnitude stronger fields in the charge-density wave behind a particle bunch travelling in a plasma, promising particle accelerators of greatly reduced size and cost. However, PWFAs can easily degrade the beam quality of the bunches they accelerate. Emittance, which determines how tightly beams can be focused, is a critical beam quality in for instance colliders and free-electron lasers, but is particularly prone to degradation. We demonstrate, for the first time, emittance preservation in a high-gradient and high-efficiency PWFA while simultaneously preserving charge and energy spread. This establishes that PWFAs can accelerate without degradation$\unicode{x2014}$essential for energy boosters in photon science and multistage facilities for compact high-energy particle colliders., Comment: 9 pages, 4 figures, 11 supplementary figures

The Antiproton Decelerator (AD) at CERN provides antiproton bunches with a kinetic energy of 5.3 MeV. The Extra-Low ENergy Antiproton ring at CERN, commissioned at the AD in 2018, now supplies a bunch of electron-cooled antiprotons at a fixed energy of 100 keV. The MUSASHI antiproton trap was upgraded by replacing the radio-frequency quadrupole decelerator with a pulsed drift tube to re-accelerate antiprotons and optimize the injection energy into the degrader foils. By increasing the beam energy to 119 keV, a cooled antiproton accumulation efficiency of (26 +- 6)% was achieved., Comment: 22 pages, 8 figures

Deep neural networks (DNNs) face significant challenges when deployed on resource-constrained extreme edge devices due to their computational and data-intensive nature. While standalone accelerators tailored for specific application scenarios suffer from inflexible control and limited programmability, generic hardware acceleration platforms coupled with RISC-V CPUs can enable high reusability and flexibility, yet typically at the expense of system level efficiency and low utilization. To fill this gap, we propose OpenGeMM, an open-source acceleration platform, jointly demonstrating high efficiency and utilization, as well as ease of configurability and programmability. OpenGeMM encompasses a parameterized Chisel-coded GeMM accelerator, a lightweight RISC-V processor, and a tightly coupled multi-banked scratchpad memory. The GeMM core utilization and system efficiency are boosted through three mechanisms: configuration pre-loading, input pre-fetching with output buffering, and programmable strided memory access. Experimental results show that OpenGeMM can consistently achieve hardware utilization ranging from 81.89% to 99.34% across diverse CNN and Transformer workloads. Compared to the SotA open-source Gemmini accelerator, OpenGeMM demonstrates a 3.58x to 16.40x speedup on normalized throughput across a wide variety ofGeMM workloads, while achieving 4.68 TOPS/W system efficiency.

The emerging microservice/serverless-based cloud programming paradigm and the rising networking speeds leave the RPC stack as the predominant data center tax. Domain-specific hardware acceleration holds the potential to disentangle the overhead and save host CPU cycles. However, state-of-the-art RPC accelerators integrate RPC logic into the CPU or use specialized low-latency interconnects, hardly adopted in commodity servers. To this end, we design and implement RPCAcc, a software-hardware co-designed RPC on-NIC accelerator that enables reconfigurable RPC kernel offloading. RPCAcc connects to the server through the most widely used PCIe interconnect. To grapple with the ramifications of PCIe-induced challenges, RPCAcc introduces three techniques:(a) a target-aware deserializer that effectively batches cross-PCIe writes on the accelerator's on-chip memory using compacted hardware data structures; (b) a memory-affinity CPU-accelerator collaborative serializer, which trades additional host memory copies for slow cross-PCIe transfers; (c) an automatic field update technique that transparently codifies the schema based on dynamic reconfigure RPC kernels to minimize superfluous PCIe traversals. We prototype RPCAcc using the Xilinx U280 FPGA card. On HyperProtoBench, RPCAcc achieves 3.2X lower serialization time than a comparable RPC accelerator baseline and demonstrates up to 2.6X throughput improvement in the end-to-end cloud workload.

The Accelerator Mass Spectrometry technique makes it possible to measure rare long-lived isotopes such as $^{10}$Be, $^{14}$C, $^{26}$Al, $^{36}$Cl, $^{41}$Ca and $^{129}$I. The content of these isotopes can be at the level of 10$^{-15}$ of the total element content. The Accelerator Mass Spectrometer developed by Budker Institute of Nuclear Physics (BINP AMS) successfully measures the concentration of $^{14}$C relative $^{12}$C. However, there is a problem of separating the $^{10}$B isobaric background from $^{10}$Be. Beryllium-10 is used to date geological objects on a time scale from 1 thousand years to 10 million years. To solve this problem we have proposed a new technique for ion identification based on measuring both ion track ranges and ion energies in a low-pressure Time-Projection Chamber (TPC) with Gas Electron Multiplier (GEM) readout. We have developed the TPC with a dedicated thin silicon nitride window for an efficient passage of ions. To begin with, the characteristic of the low-pressure TPC were studied in isobutane at a pressure of 50 torr using alpha particle sources. In this work, we set up the low-pressure TPC on BINP AMS facility and successfully measured track ranges and energies of ions from samples containing $^{14}$C. At the next stage, we are going to carry out measurements with samples containing $^{10}$Be. However, using the obtained results and SRIM simulation we have already shown that the isobaric boron and beryllium ions can be separated by more than 5 sigma. This technique is proposed to be applied in AMS for dating geological objects, namely for geochronology of Cenozoic era.

Processing Using Memory (PUM) accelerators have the potential to perform Deep Neural Network (DNN) inference by using arrays of memory cells as computation engines. Among various memory technologies, ReRAM crossbars show promising performance in computing dot-product operations in the analog domain. Nevertheless, the expensive writing procedure of ReRAM cells has led researchers to design accelerators whose crossbars have enough capacity to store the full DNN. Given the tremendous and continuous increase in DNN model sizes, this approach is unfeasible for some networks, or inefficient due to the huge hardware requirements. Those accelerators lack the flexibility to adapt to any given DNN model, facing an challenge. To address this issue we introduce ARAS, a cost-effective ReRAM-based accelerator that employs a smart scheduler to adapt different DNNs to the resource-limited hardware. ARAS also overlaps the computation of a layer with the weight writing of several layers to mitigate the high writing latency of ReRAM. Furthermore, ARAS introduces three optimizations aimed at reducing the energy overheads of writing in ReRAM. Our key optimization capitalizes on the observation that DNN weights can be re-encoded to augment their similarity between layers, increasing the amount of bitwise values that are equal or similar when overwriting ReRAM cells and, hence, reducing the amount of energy required to update the cells. Overall, ARAS greatly reduces the ReRAM writing activity. We evaluate ARAS on a popular set of DNNs. ARAS provides up to 2.2x speedup and 45% energy savings over a baseline PUM accelerator without any optimization. Compared to a TPU-like accelerator, ARAS provides up to 1.5x speedup and 61% energy savings., Comment: 13 pages, 17 figures

Accelerating end-to-end inference of transformer-based large language models (LLMs) is a critical component of AI services in datacenters. However, diverse compute characteristics of end-to-end LLM inference present challenges as previously proposed accelerators only address certain operations or stages (e.g., self-attention, generation stage, etc.). To address the unique challenges of accelerating end-to-end inference, we propose IANUS -- Integrated Accelerator based on NPU-PIM Unified Memory System. IANUS is a domain-specific system architecture that combines a Neural Processing Unit (NPU) with a Processing-in-Memory (PIM) to leverage both the NPU's high computation throughput and the PIM's high effective memory bandwidth. In particular, IANUS employs a unified main memory system where the PIM memory is used both for PIM operations and for NPU's main memory. The unified main memory system ensures that memory capacity is efficiently utilized and the movement of shared data between NPU and PIM is minimized. However, it introduces new challenges since normal memory accesses and PIM computations cannot be performed simultaneously. Thus, we propose novel PIM Access Scheduling that manages normal memory accesses and PIM computations through workload mapping and scheduling across the PIM and the NPU. Our detailed simulation evaluations show that IANUS improves the performance of GPT-2 by 6.2$\times$ and 3.2$\times$, on average, compared to the NVIDIA A100 GPU and the state-of-the-art accelerator. As a proof-of-concept, we develop a prototype of IANUS with a commercial PIM, NPU, and an FPGA-based PIM controller to demonstrate the feasibility of IANUS., Comment: Updated version of the paper accepted to ASPLOS 2024

In this paper, we present the first multi-modal FHE accelerator based on a unified architecture, which efficiently supports CKKS, TFHE, and their conversion scheme within a single accelerator. To achieve this goal, we first analyze the theoretical foundations of the aforementioned schemes and highlight their composition from a finite number of arithmetic kernels. Then, we investigate the challenges for efficiently supporting these kernels within a unified architecture, which include 1) concurrent support for NTT and FFT, 2) maintaining high hardware utilization across various polynomial lengths, and 3) ensuring consistent performance across diverse arithmetic kernels. To tackle these challenges, we propose a novel FHE accelerator named Trinity, which incorporates algorithm optimizations, hardware component reuse, and dynamic workload scheduling to enhance the acceleration of CKKS, TFHE, and their conversion scheme. By adaptive select the proper allocation of components for NTT and MAC, Trinity maintains high utilization across NTTs with various polynomial lengths and imbalanced arithmetic workloads. The experiment results show that, for the pure CKKS and TFHE workloads, the performance of our Trinity outperforms the state-of-the-art accelerator for CKKS (SHARP) and TFHE (Morphling) by 1.49x and 4.23x, respectively. Moreover, Trinity achieves 919.3x performance improvement for the FHE-conversion scheme over the CPU-based implementation. Notably, despite the performance improvement, the hardware overhead of Trinity is only 85% of the summed circuit areas of SHARP and Morphling., Comment: To be appeared in MICRO 2024. The first ASIC-based FHE accelerator which supports both CKKS, TFHE and their conversions. Provide new SOTA performance record for CKKS, TFHE and conversion