32. ADVANCED CONCEPTS AND TECHNOLOGY FOR PARTICLE ACCELERATORS Maximum Phase I Award Amount: $200,000 Maximum Phase II Award Amount: $1,100,000 Accepting SBIR Phase I Applications: YES Accepting STTR Phase I Applications: YES The DOE HEP program supports a broad research and development (R&D) effort in the science, engineering, and technology of charged particle accelerators, storage rings, and associated apparatus. The strategic plan for HEP includes initiatives on the energy and intensity frontiers, relying on accelerators capable of delivering beams of the required energy and intensity. As high energy physics facilities get bigger and more costly, the DOE HEP program seeks to develop advanced technologies that can be used to reduce the overall machine size and cost, and also to develop new concepts and capabilities that further scientific and commercial needs beyond HEP's discovery science mission. In many cases the technology sought is closely tied to a specific machine concept which sets the specifications (and tolerances) for the technology. Applicants are strongly encouraged to review the references provided. Applications to subtopics specifically associated with a machine concept that do not closely adhere to the specifications of the machine will be considered non- responsive. For subtopics that are not machine-specific, applicants are strongly advised to understand the state-of- the-art and to clearly describe in the application what quantitative advances in the technology will result. Grant applications are sought only in the following subtopics: a. Machine Learning, Diagnostics, Controls and Modeling Machine learning (ML) promises significant enhancements for particle accelerator operations, including applications in diagnostics, controls, and modeling. However, it is essential that as these promising ML methods are experimentally demonstrated before being deployed at user facilities. The ability to generalize the training and deployment of ML algorithms to different operating configurations for the same beamline, or between facilities, remains a challenge. Proposals are sought to develop novel algorithms and associated software to support the training and deployment of ML tools at accelerator facilities. Developments, which facilitate domain transfer between facilities and diagnostic types, are highly encouraged. Successful proposals must include plans for experimental demonstration and validation. Questions Contact: John Boger, john.boger@science.doe.gov b. Adaptive Online Machine Learning for Dynamic Beam Diagnostics Particle accelerators are large complex systems composed of hundreds to thousands of interconnected electro-magnetic components including radio frequency (RF) resonant accelerating structures for beam acceleration and longitudinal focusing and various magnets for beam steering and transverse focusing. Charged particle beams are themselves complex objects living in a six-dimensional phase space. They undergo complex collective effects such as coherent synchrotron radiation and vary with time in unpredictable ways. Sources of variation include accelerator RF phase and amplitude jitter, and magnet current jitter, and time-varying laser intensities and photoemission at the beam source. As bunches become shorter and more intense, the effects of intra-bunch collective interactions such as space charge forces and bunch-to-bunch influences such as wakefields also increase. Short, intense bunches are extremely difficult to accurately image because their dimensions are beyond the resolution of existing diagnostics and they may be destructive to intercepting diagnostics. Proposals are sought for the design and implementation of adaptive machine learning methods as applied to time-varying systems since they have the potential to aid in the diagnostics and control of high-intensity, ultrashort beams by interfacing online models with real time non-invasive beam data, and thereby provide a detailed virtual view of intense bunch dynamics. The goal is to enable beam prediction and control, and to develop new diagnostics in order to increase beam phase-space density by at least an order of magnitude than currently achievable. (See References 1 through 6 for further information.) Questions Contact: John Boger, john.boger@science.doe.gov c. Structure Wakefield Acceleration (SWFA) Structure Wakefield Acceleration (SWFA) provides a viable path towards a future HEP linear collider. In order to continue progress along the 2016 SWFA Roadmap [7] improvements of both structures and bunch shaping systems are needed to realize beam-driven SWFA that operate with high-gradient (>few 100s MeV/m) and high rf-to-beam efficiency (>40%). 1. Proposals are sought for high-frequency structures (10 GHz - 1 THz) capable of high-gradient, low-loss, higher order mode damping and mutlipactor/field-emission mitigation. Structures for two-beam accelerators further require high-efficiency coupling schemes for short-pulse operation. Structures considered appropriate for this topic include, but are not limited to, metallic or dielectric, novel geometry (metamaterial, photonic bandgap, etc.), room temperature or cryogenic. 2. Proposals are also sought for bunch shaping systems for driving structures operating in a range from 10 GHz - 1 THz and capable of (i) shaping of high-charge bunches (>10 nC) with (<1 ps) resolution, (ii) generating multiple bunches with accurate time separation (<100 fs) with (iii) minimal charge losses (<10%). Such drive bunch shaping systems should target increasing the transformer ratio (>10) in collinear wakefield accelerator or increasing the utilization of the drive bunch energy (>80%) by reducing the beam-breakup instability. Witness bunch shaping systems should optimize beam-loading to achieve high rf-to-beam efficiency while being capable of operating in an environment with high-gradient, high net acceleration per structure (10s MeV energy gain) and beam quality preservation. Examples of bunch shaping systems include, but are not limited to, direct beam manipulation beamlines such as emittance exchange, round-to-flat transformers, deflecting cavities as well as 3D laser bunch shaping for photocathode guns. Questions Contact: John Boger, john.boger@science.doe.gov d. Targetry: Radiation Resistant Strain/Vibration Instrumentation Development Monitoring the health of a target component in situ requires strain instrumentation that can withstand the high radiation environment of a beam target. The accumulated dose of a future target component during its lifetime may exceed 10 Giga-Gray. Instrumentation that can function near to this limit is needed that can resolve strains as low as 100 nano-strain and as high as 10 milli-strain (non-concurrently). Sampling frequency is potentially as high as 4 MHz to capture the dynamic response of a high intensity beam pulse (1 to 10 microsec duration). Proposal are sought for the development of radiation-hardened transducers for measuring transient strains in the range of 10-7 to 10-2 in the frequency range of 1 kHz to 4 MHz. Questions Contact: John Boger, john.boger@science.doe.gov e. Other In addition to the specific subtopics listed above, the Department invites grant applications in other areas that fall within the scope of the topic description above. Questions Contact: John Boger, john.boger@science.doe.gov References: 1. Litos, M., Adli, E., An, W., Clarke, C., Clayton, C., Corde, S., Delahaye, J., England, R., Fisher, A., Frederico, J., et al., High-efficiency acceleration of an electron beam in a plasma wakefield accelerator. Nature 515, 92, 2014, https://www.nature.com/articles/nature13882. 2. Corde, S., Adli, E., Allen, J., An, W., Clarke, C., Clayton, C., Delahaye, J., Frederico, J., Gessner, S., Green, S., et al. Multi-gigaelectronvolt acceleration of positrons in a self-loaded plasma wakefield, Nature 524, 442 (2015), https://www.nature.com/articles/nature14890. 3. Adli, E., Ahuja, A., Apsimon, O., Apsimon, R., Bachmann, A. M., Barrientos, D., Batsch, F., Bauche, J., Olsen, V.B., Bernardini, M., et al. Acceleration of electrons in the plasma wakefield of a proton bunch. Nature 561, 363 367, 2018, https://www.nature.com/articles/s41586-018-0485-4 4. Joshi, C., Adli, E., An, W., Clayton, C.E., Corde, S., Gessner, S., Hogan, M.J., Litos, M., Lu, W., Marsh, K.A., et al. Plasma wakefield acceleration experiments at FACET II. Plasma Physics and Controlled Fusion 60, 034001, 2018, https://iopscience.iop.org/article/10.1088/1361-6587/aaa2e3/meta 5. Scheinker, A., and Gessner, S. Adaptive method for electron bunch profile prediction. Physical Review Special, Topics-Accelerators and Beams 18, 102801, 2015, https://doi.org/10.1103/PhysRevSTAB.18.102801 6. Scheinker, A., Edelen, A., Bohler, D., Emma, C., and Lutman, A. Demonstration of model-independent control of the longitudinal phase space of electron beams in the linac-coherent light source with femtosecond resolution. Physical review letters 121, 044801, 2018, https://doi.org/10.1103/PhysRevLett.121.044801 7. Advanced Accelerator Development Strategy Report: DOE Advanced Accelerator Concepts Research Roadmap Workshop. U.S. DOE Office of Science, Feb 03, 2016, https://www.osti.gov/biblio/1358081-advanced-accelerator-development-strategy-report-doe-advanced-accelerator-concepts-research-roadmap-workshop
