Bulletin of the American Physical Society
APS April Meeting 2021
Volume 66, Number 5
Saturday–Tuesday, April 17–20, 2021; Virtual; Time Zone: Central Daylight Time, USA
Session E15: Lepton Machines and Machine LearningLive
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Sponsoring Units: DPB Chair: Georg Hoffstaetter, Cornell |
Saturday, April 17, 2021 3:45PM - 3:57PM Live |
E15.00001: Simulation of the Belle II beam-induced background at the SuperKEKB collider Andrii Natochii At high-energy colliders, backgrounds due to losses of beam particles can lead to a number of detrimental effects, including degradation of data quality, reduction in data-taking efficiency, superconducting magnet quenches, and damage to sensitive detectors. We review the recently improved simulation of beam backgrounds in the Belle II experiment at the SuperKEKB electron-positron collider. The unprecedented instantaneous design luminosity of $\rm 8x10^{35}~cm^{-2}s^{-1}$ implies a crucial need for background control and mitigation. We present updated results for SuperKEKB backgrounds using an improved multi-turn particle tracking code based on the Strategic Accelerator Design software framework. The simulation now includes more realistic collimator shapes, particle scattering off of collimators, and a more CPU-efficient collimator optimisation. These improvements have led to greatly improved agreement between simulated and experimental background rates for the aperture scans of collimators. For the first time since the start of collider commissioning, all simulated background components are within an order of magnitude of measurements. The new simulation framework is used extensively at KEK for further collider optimisation and background mitigation towards higher luminosities. [Preview Abstract] |
Saturday, April 17, 2021 3:57PM - 4:09PM Live |
E15.00002: ERL e+ e- Collider Nikhil Bachhawat, Vladimir Litvinenko, Maria Chamizo-Llatas, Yichao Jing, Francois Meot, Thomas Roser The study of Higgs boson and searches for Beyond Standard Model physics are major topics of research in particle physics today. $\text{e^+ e^-}$ colliders provide a cleaner channel to study particle interactions and would complement LHC and other particle colliders in operation. The Energy Recovering Linac (ERL) $\text{e^+ e^-}$ collider is a high energy design for an $\text{e^+ e^-}$ collider that can potentially achieve center of mass beam energies of up to 600 GeV. This allows access to double Higgs and $\text{t\bar{t}H}$ production channels and hence, provides a way to perform higher precision studies of Higgs self-coupling and top Yukawa coupling among other electroweak parameters. The ERL design also boasts a more energy efficient method of reaching higher energies and higher luminosities compared to other $\text{e^+ e^-}$ collider designs. Additionally, ERL $\text{e^+ e^-}$ collider also provides access to polarization of the beams as a variable to study its effects on particle interactions such as the yield of di-higgs events. [Preview Abstract] |
Saturday, April 17, 2021 4:09PM - 4:21PM Live |
E15.00003: The Cornell-BNL ERL Test Accelerator: Demonstration of the World's first Multipass Superconducting Linear Accelerator with Energy Recovery. Colwyn Gulliford, Adam Bartnik, Nilanjan Banerjee, James Crittenden, Kirsten Deitrick, John Dobbins, Georg Hoffstaetter, Peter Quigely, David Sagan, David Burke, William Lou, Karl Smolenski, J. Scott Berg, Stephen Brooks, Rob Hulsart, George Mahler, Francois Meet, Rob Michnoff, Stephen Peggs, Thomas Roser, Dejan Trobjevic, Nicholaos Tsoupas Energy recovery has been achieved in both single and multipass configurations of the Cornell-BNL ERL Test Accelerator (CBETA). In the multipass configuration, energy transferred to the electron beam during the first four passes through the accelerating structure was recovered from the beam during four subsequent decelerating passes. The combination of superconducting accelerating cavities for minimizing power loss in the accelerating structure and permanent magnets for the return loop resulted in high-energy efficiency operation. The use of a fixed-field alternating-gradient optical system for the return loop allowed transport of beams with 42, 78, 114, and 150 MeV in a common vacuum chamber. In the single turn configuration, energy recovery efficiencies of 99.8-100.5{\%} were measured for each of the accelerating cavities. The technology used in CBETA makes possible more compact particle accelerators featuring higher beam currents and reduced energy consumption. [Preview Abstract] |
Saturday, April 17, 2021 4:21PM - 4:33PM Live |
E15.00004: An Integrated Approach to the Design of Diffraction Limited Light Sources Ji Li Many laboratories around the world are aiming to build a next generation diffraction-limited light source. Diamond Light Source is planning an upgrade of its accelerator with a factor- 20 reduction in emittance. One of the key aspects in the design of the upgrade is the optimisation of the photon beam properties, including the flux, brightness, and spot size, as well as the divergence and/or coherence of the new sources. To find the best trade-offs between electron beam dynamics and photon dynamics, we developed a wrapper code package which integrated the accelerator physics tracking code (ELEGANT) with radiation codes (SRW, SHADOW). We also implemented the Non-Dominated Sorting Genetic Algorithm II (NGSA II)~in this approach so as to optimise a beamline using both wavefront and ray-tracing models. We evaluated the characteristics of the expected X-rays using the Diamond I13 coherence beamline as an example, and found extremely promising results arising from the optimisation. The beam size reduced significantly from ($\sigma_{\mathrm{x}}$,$\sigma_{\mathrm{y\thinspace }})=$(17.9,10.3)$\mu $m to ($\sigma_{\mathrm{x}}$,$\sigma _{\mathrm{y\thinspace }})=$(3.3,1.9)$\mu $m, as a result the peak intensity increased by 40 times. We believe this code package would be a valuable tool for the design of future advanced synchrotron facilities. [Preview Abstract] |
Saturday, April 17, 2021 4:33PM - 4:45PM Live |
E15.00005: A sub-micron resolution, bunch-by-bunch beam trajectory feedback system and its application to reducing wakefield effects in single-pass beamlines Philip Burrows A high-precision intra-bunch-train beam orbit feedback correction system has been developed and tested in the ATF2 beamline of the Accelerator Test Facility at the High Energy Accelerator Research Organization in Japan. The system uses the vertical position of the bunch measured at two beam position monitors (BPMs) to calculate a pair of kicks which are applied to the next bunch using two upstream kickers, thereby correcting both the vertical position and trajectory angle. Using trains of two electron bunches separated in time by 187.6~ns, the system was optimised so as to stabilize the beam offset at the feedback BPMs to better than 350~nm, yielding a local trajectory angle correction to within 250~nrad. The quality of the correction was verified using three downstream witness BPMs and the results were found to be in agreement with the predictions of a linear lattice model used to propagate the beam trajectory from the feedback region. This same model predicts a corrected beam jitter of c.~1~nm at the focal point of the accelerator. Measurements with a beam size monitor at this location demonstrate that reducing the trajectory jitter of the beam by a factor of 4 also reduces the increase in the measured beam size as a function of beam charge by a factor of c.~1.6. [Preview Abstract] |
Saturday, April 17, 2021 4:45PM - 4:57PM Live |
E15.00006: Observation of Undulator Radiation Generated by a Single Electron Circulating in a Storage Ring and Possible Applications Ihar Lobach, Sergei Nagaitsev, Giulio Stancari, Aleksandr Romanov, Alexander Valishev, Aliaksei Halavanau, Zhirong Huang Experimental study into the undulator radiation generated by a single electron was carried out at the Integrable Optics Test Accelerator (IOTA) storage ring at Fermilab. The individual photons were detected by a Single Photon Avalanche Diode (SPAD) at an average rate of 1 detection per 300 revolutions in the ring. The photodetection events were continuously recorded by a picosecond event timer for as long as 1 minute at a time. The collected data were used to test if there is any deviation from the classically predicted Poissonian photostatistics. It was motivated by the observation $^{\mathrm{\ast }}$ of sub-Poissonian statistics in a similar experiment. The observation * could be an instrumentation effect related to low detection efficiency and long detector dead time. In our experiment, the detector (SPAD) has a much higher efficiency (65{\%}) and a much lower dead time. In addition, we show that the collected data (recorded detection times) can be used to study the synchrotron motion of a single electron and infer some parameters of the ring. For example, by comparing the results of simulation and measurement for the synchrotron motion we were able to estimate the magnitude of the RF phase jitter in IOTA. * Teng Chen and John M. J. Madey, Phys. Rev. Lett. 86, 5906, June 2001 [Preview Abstract] |
Saturday, April 17, 2021 4:57PM - 5:09PM Live |
E15.00007: Multi-Objective Bayesian Optimization for Online Accelerator Tuning Ryan Roussel, Adi Hanuka, Auralee Edelen Maximizing the performance of an accelerator facility often necessitates multi-objective optimization, where operators must balance trade-offs between multiple objectives simultaneously, often using limited, temporally expensive beam observations. Usually, accelerator optimization problems are solved offline, prior to actual operation, with advanced beamline simulations and parallelized evolutionary algorithms. Unfortunately, it is not feasible to use these methods for online multi-objective optimization, since beam measurements can only be done in a serial fashion, and these optimization methods require a large number of measurements to converge to a useful solution.Here, we introduce a multi-objective Bayesian optimization scheme, which finds the full Pareto front of an accelerator optimization problem efficiently in a serialized manner.This method uses a set of Gaussian process surrogate models, along with a multi-objective acquisition function, which reduces the number of observations needed to converge by at least an order of magnitude over current methods.We also demonstrate how this method can be modified to specifically solve optimization challenges posed by the tuning of accelerators. [Preview Abstract] |
Saturday, April 17, 2021 5:09PM - 5:21PM Live |
E15.00008: Novel Accelerator Diagnostic Development for Multi-Objective Bayesian Optimization at the Argonne Wakefield Accelerator Facility Juan Pablo Gonzalez Aguilera, Ryan Roussel, Young-Kee Kim Particle accelerators must achieve certain beam quality objectives for use in different experiments. Usually, optimizing certain beam objectives comes at the expense of others. Additionally, there are many input parameters and a limited number of measured outputs. Therefore, accelerator tuning becomes a multi-objective optimization problem with a high-dimensional decision space and a limited number of observations. Recently, multi-objective Bayesian optimization has been proposed as an efficient method to find the Pareto front for an accelerator tuning problem, and this method reduces the number of observations needed to converge. In order to experimentally test the multi-objective Bayesian optimization method, a novel accelerator diagnostic is being designed to simultaneously measure multiple beam quality metrics of an electron beam at the Argonne Wakefield Accelerator Facility. Here, we present a design consisting in a pepper-pot mask, a dipole and a scintillation screen, which allows a simultaneous measurement of the electron beam energy spread and vertical emittance. [Preview Abstract] |
Saturday, April 17, 2021 5:21PM - 5:33PM Live |
E15.00009: Deep Learning Methods for Uncertainty Quantification at the SLAC Linac Coherent Light Source Lipi Gupta, Aashwin Mishra, Auralee Edelen Particle accelerators are essential instruments in modern science, and must often provide charged particle beams with different beam parameters (e.g. different beam energies and durations) for each requested experiment. This is accomplished by tuning a wide variety of continuously-variable controllable settings on the accelerator. Tuning is a challenging task, as many particle accelerators are complex machines with thousands of components, each of which may contribute sources of uncertainty, or produce nonlinear responses to setting changes. Fast, accurate models of these systems could potentially aid rapid customization of beams. In order to accomplish this reliably, estimates of predictive uncertainty are essential, as many accelerators are high-regret and safety-critical systems. Here, we obtain quantified uncertainties from learned models of a noisy, high-dimensional, nonlinear accelerator system. We examine quantile regression neural networks (QRNNs) and Bayesian Neural Networks (BNNs) as candidate modeling paradigms. We assess model performance on noisy, high-dimensional data that covers a broad range of operating configurations, with the aim of obtaining an accurate model of the FEL pulse energy and associated uncertainty estimates for use in operation. [Preview Abstract] |
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