Broadband high power rf window design for the BNL Electron Ion Collider
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Traveling-wave (TW) technology can push the accelerator field gradient of niobium SRF cavity to 70MV/m or higher beyond the limit of 50~60MV/m in Standing-wave (SW) technology. The early stages of TW SRF cavity developments had been funded by several SBIR grants to Euclid Techlabs and completed in collaboration with Fermilab through a 1-cell prototype and a proof-of-principle 3-cell TW cavity. The TW resonance excitation in the 3-cell TW cavity at 2K was demonstrated through the low power RF test in early 2024. A high-power test of the 3-cell in TW mode being prepared. To advance a design and technology to fabricate a novel high gradient TW SRF cavity, FNAL proposed a half-meter TW RF design and R&Ds to realize that are in progress. Here we will report the recent progress in the 3-cell TW cavity and the challenges towards a half-meter scale TW cavity.
Rapidly expanding, new telecommunications infrastructure based on 5G technologies will disrupt and transform how we design, build, operate, and optimize scientific infrastructure and the experiments and services enabled by that infrastructure, from continental-scale sensor networks to centralized scientific user facilities, from intelligent Internet of Things devices to supercomputers. Concurrently, 5G will introduce, or exacerbate, challenges related to protecting infrastructure and associated scientific data as well as to fully leveraging opportunities related to expanded infrastructure scale and complexity. The U.S. Department of Energy (DOE) Office of Science operates scientific infrastructure, supporting some of the nation’s most advanced intellectual discoveries, spanning the country and including 30 world-class user facilities from supercomputers to accelerators. Along with field experiments and remote observatories, every aspect of DOE’s scientific enterprise will be affected by 5G, which amounts to a complete renovation of the underpinnings of the nation’s information infrastructure. In this report we explore the scientific opportunities and new research challenges associated with 5G, ranging from scalability to heterogeneity to cybersecurity. The rapid commercial deployment of 5G opens the opportunity to rethink and reinvent DOE’s scientific infrastructure and experimentation, from intelligent sensor networks at unprecedented scales to a digital continuum of cyberinfrastructure spanning low-power sensors, high-performance computing embedded within and at the edge of the network, and DOE’s large-scale user instrument and computing facilities. New programming paradigms, workflow and data frameworks, and AI-based system design, operation, and autonomous adaptation and optimization will be necessary in order to exploit these new opportunities. Field deployments and centralized scientific instruments can also be revolutionized, moving (without traditional performance penalties) from wired to wireless connectivity for data and control systems, improving flexibility, and opening new sensing modalities, including the use of the 5G electromagnetic spectrum itself as an environmental probe. For DOE science, in contrast to commercial 5G applications and settings, devices will be deployed in extreme environments such as cryogenically cooled instrument control systems and in remote settings with harsh conditions, requiring the design of new materials for RF communication and edge processing to operate in these regimes. Concurrently, 5G infrastructure comprises both hardware and sophisticated software systems - currently closed and proprietary. The cybersecurity challenges to 5G-empowered reinvention mirror the complexity and variety of new 5G features, from virtualization to private network slices to ubiquitous access. Research is also needed in order to accelerate the development of secure and open 5G software infrastructure, reducing reliance on hardware and software produced outside the United States and providing the transparency and rigorous evaluation and testing afforded through open software. Twelve broad research thrusts are laid out in four chapters, with a companion fifth chapter (and three additional research thrusts) underscoring the needs and opportunities for an aggressive testbed program co-designed by networking experts and scientists involved in the 15 research thrusts. The urgency of undertaking this research is fueled by a global, accelerating deployment of new telecommunications infrastructure that is designed for entertainment and commercial applications - barely scratching the surface of what 5G can do to extend U.S. leadership in scientific discovery.
The upcoming Proton Improvement Plan-II (PIP-II), designated for enhancements to the Fermilab accelerator complex, features a new 800 MeV superconducting linac and a Beam Transfer Line (BTL) to transport the beam to the existing Booster synchrotron. To mitigate the space charge tune shift associated with a high intensity accumulated beam, the low emittance linac beam is used to paint the ring phase space both transversely and longitudinally. To prevent losses caused by particles injected outside the rf separatrix while painting longitudinal phase space, the momentum spread of the incoming beam should not exceed 2.1 x 10^-4. Detailed simulations showed that due to space charge, the rms momentum spread increases to 4 x 10^-4 while it is transported in the BTL --about twice the allowable limit. In this paper, we outline a mitigation strategy involving a debuncher cavity. We discuss location, operating frequency, and gap voltage under both nominal and perturbed beam conditions, specifically accounting for momentum jitter. The impact of cavity misalignments is also assessed. The paper concludes by recommending an optimized configuration.
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Addressing baseline effects in accelerator environments is crucial for accurate data acquisition and analysis, since baseline effects can obscure signal clarity and impact the reliability of beam current monitoring systems. There are many potential contributors to baseline noise, such as variations in beam dynamics, electromagnetic interference from nearby equipment, or RF interference. Previous applications of noise reduction systems don t sufficiently filter sources of asynchronous noise, so a new algorithm was implemented. A simulation dataset was created to replicate beam conditions and a Red Pitaya FPGA was used to collect data through the streaming application. A Python script was developed to implement noise reduction algorithms and efforts were made to integrate real-time data streaming with the Redis platform and Acnet Front End infrastructure.
High gradient and high quality factor superconducting radiofrequency (SRF) cavities are essential for efficient particle acceleration in next-generation accelerators. This work demonstrates that the superconducting performance of bulk niobium can be enhanced through a controlled introduction of interstitial oxygen and nitrogen. Thermal and chemical surface treatments modify the concentration and distribution of these impurities, enabling systematic tuning of SRF cavity performance. By combining cavity RF measurements with quantitative materials characterizations, we isolate the impact of impurity concentration on BCS surface resistance and identify the mechanisms by which interstitial impurities alter the superconducting properties of niobium. We find that nitrogen is an order of magnitude more effective than oxygen in reducing BCS resistance at 16 MV/m and that the reduction arises from a combination of mean free path optimization and superconducting gap enhancement. Using these insights, we develop a predictive model that estimates cavity performance directly from room temperature sample material studies, reducing the reliance on full-scale cryogenic tests for surface treatment development. We then apply this model to explore co-doping strategies, demonstrating that combining interstitial oxygen and nitrogen achieves a synergistic reduction in BCS resistance. These results establish co-doping as a viable path for the development of surface-engineered niobium for future high quality factor SRF applications.
The HWR (half-wave-resonator) cryomodule is the first one in the superconducting section of the PIP-II LINAC project at Fermilab. PIP-II IT is a test facility for the project where the injector, warm front-end, and the first two superconducting cryomodules are being tested. The HWR cryomodule comprises 8 cavities operating at a frequency of 162.5 MHz and accelerating beam up to 10 MeV. Resonance control of the cavities is performed with a pneumatically operated slow tuner which compresses the cavity at the beam ports. Helium gas pressure in a bellows mounted to an end wall of the cavity is controlled by two solenoid valves, one on the pressure side and one on the vacuum side. The resonant frequency of the cavity can be controlled in one of two modes. A pressure feedback control loop can hold the cavity tuner pressure at a fixed value for the desired resonant frequency. Alternately, the feedback loop can regulate the cavity tuner pressure to bring the RF detuning error to zero. The resonance controller is integrated into the LLRF control system for the cryomodule. The control system design and performance of the resonance control system are described in this paper.
The Low-Level Radio Frequency (LLRF) control system is one of the most critical superconducting linac infrastructures responsible for the parameters of the beam acceleration. The LLRF system mainly focuses on the electromagnetic field parameters inside the cavity. While it incorporates fast feedback algorithms to optimize energy transfer to the passing particle beam it does not follow other cryomodule or cavity parameter changes. The Radio Frequency Protection Interlock (RFPI) system closely monitors various factors (like cryomodule vacuum, beamline vacuum, field emission probe current level, temperature, RF signal leakage, etc). Its simple but reliable logic has to provide an instant decision about the LLRF system or high-power amplifier output signal blocking in case of safety region excitation. This contribution presents a new version of the RFPI system which logic is implemented in the FPGA chip. The initial work on the prototype of the new system design resulted in the PoC (Proof of Concept) device. The PoC offers the possibility of various protection logic configurations, input signals parameters evaluation, and modularity aspects verification. The structure and test results from the device evaluation are summarized and discussed in this contribution.
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We report evidence for the formation of niobium hydride phase within niobium films on silicon substrates in superconducting qubits fabricated at Rigetti Computing. For this study, we combined complementary techniques—including room-temperature and cryogenic atomic force microscopy (AFM), synchrotron x-ray diffraction, and time-of-flight secondary ion mass spectroscopy (ToF-SIMS)—to directly reveal the existence of niobium hydride precipitates on the surface of superconducting qubits. Upon cryogenic cooling, we observed variation in the size and morphology of the hydrides, ranging from small (∼5 nm) irregular shapes to large (∼10–100 nm) domains within the Nb grains, which were fully converted to niobium hydrides. Since niobium hydrides are nonsuperconducting and can easily change in size and location upon different cooldowns to cryogenic temperature, our finding highlights a previously unknown source of decoherence in superconducting qubits. This contributes to quasiparticle losses, offering a potential explanation for changes in qubit performance upon cooldowns. Finally, by leveraging the RF performance of a 3D bulk Nb resonator, we quantify RF dissipation in a superconducting qubit caused by hydrogen concentration variation, and propose a practical engineering pathway to mitigate the formation of Nb hydrides for superconducting qubit applications.
The Proton Power Upgrade initiative at Oak Ridge National Lab’s Spallation Neutron Source aims to greatly boost the capability of proton beam power. This upgrade involves the integration of seven additional cryomodules, each housing four six-cell high-beta ( β = 0.81 ) superconducting radio frequency cavities. These cavities, manufactured and processed by Research Instruments in Germany, underwent meticulous treatment, including electropolishing as the bulk and final vital step. Upon delivery to Jefferson Lab, a total of 28 cavities for seven cryomodules, along with an additional four cavities for a spare cryomodule, underwent thorough vertical qualification tests, meeting the required specifications. Following cavity tanking and subsequent rf testing, the assembly of eight cryomodules was successfully executed, with all 32 cavities demonstrating compliance with acceptance criteria. The transportation of the cryomodule to SNS for high-power testing in the tunnel yielded exceptional results. Notably, the performance surpassed specifications, in terms of quality factor and accelerating gradient. Published by the American Physical Society 2024
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The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab operates hundreds of super-conducting radio frequency (SRF) cavities in its two linear accelerators (linacs). Field emission (FE) is an ongoing operational challenge in higher gradient SRF cavities. FE generates high levels of neutron and gamma radiation leading to damaged accelerator hardware and a radiation hazard environment. During machine development periods, we performed gradient scans to record data capturing the relationship between cavity gradients and radiation levels measured throughout the linacs. However, the field emission environment at CEBAF varies considerably over time as the configuration of the radio frequency (RF) gradients changes and due to the changing behaviour of field emitters. An artificial intelligence/machine learning (AI/ML) approach with transfer learning could be a valuable tool to mitigate FE and lower the radiation levels. In this work, we mainly focus on leveraging the RF trip data gathered during CEBAF operations. We develop a transfer learning-based surrogate model for radiation detector readings given RF cavity gradients to track the CEBAF?s changing configuration and environment. Then, we could use the developed model as an optimization process for redistributing the RF gradients within a linac to minimize radiation levels.
The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab operates hundreds of super-conducting radio frequency (SRF) cavities in its two linear accelerators (linacs). Field emission (FE) is an ongoing operational challenge in higher gradient SRF cavities. FE generates high levels of neutron and gamma radiation leading to damaged accelerator hardware and a radiation hazard environment. During machine development periods, we performed gradient scans to record data capturing the relationship between cavity gradients and radiation levels measured throughout the linacs. However, the field emission environment at CEBAF varies considerably over time as the configuration of the radio frequency (RF) gradients changes and due to the changing behaviour of field emitters. An artificial intelligence/machine learning (AI/ML) approach with transfer learning could be a valuable tool to mitigate FE and lower the radiation levels. In this work, we mainly focus on leveraging the RF trip data gathered during CEBAF operations. We develop a transfer learning-based surrogate model for radiation detector readings given RF cavity gradients to track the CEBAF?s changing configuration and environment. Then, we could use the developed model as an optimization process for redistributing the RF gradients within a linac to minimize radiation levels.
SRF cavities for particle acceleration are conventionally operated immersed in a bath of liquid helium at 4.2 K and below. Although this cooling configuration is practically and economically viable for large scientific accelerator installations, it may not be so for smaller accelerators intended for industrial applications such as the treatment of wastewater, sludge, flue gases, etc. In this paper, we describe a procedure to operate SRF cavities without liquid helium that can be used to construct electron-beam sources for industrial applications of electron irradiation (1-10 MeV electron energy). In this procedure, an elliptical single-cell 650 MHz niobium-tin coated niobium cavity is coupled to a closed-cycle 4.2 K cryocooler using high purity aluminum thermal links. The cryocooler conductively extracts heat (RF dissipation) from the cavity without requiring liquid helium around the cavity. We present construction details of this cryocooler conduction-cooling technique and systematic experiments that have demonstrated ~10 MV/m cw gradient on the cavity. By straightforward scaling up the cavity length and number of cryocoolers, the technique will provide the complete range of 1-10 MeV electron energy for industrial applications.
A significant contribution to the temporal resolution of an ultrafast electron diffraction (UED) instrument is arrival time jitter caused by amplitude and phase variation of radio-frequency (rf) cavities. In this paper, we present a semianalytical approach for calculating rf-induced temporal jitter from klystron and rf cavity parameters. Our approach allows fast estimation of temporal jitter for MeV-UED beamlines and can serve as a virtual timing tool when shot-to-shot measurements of rf amplitude and phase jitters are available. A simulation study for the SLAC MeV-UED instrument is presented, and the temporal resolution of several beamline configurations is compared.