Particle accelerator failures lead to unscheduled downtime and lower reliability. Although simple to mitigate while they are actually happening such failures are difficult to predict or identify beforehand. In this work we propose using machine learning approaches to predict machine failures via beam current measurements before they actual occur. To demonstrate this technique in this paper we examine beam pulses from the Oakridge Spallation Neutron Source (SNS). By evaluating a pulse against a set of common classification techniques we show that accelerator failure can be identified prior to actually failing with almost 80% accuracy. We also show that tuning classifier parameters and using pulse properties for refining datasets can further lead to almost 92% accuracy in classification of bad pulses. Most importantly, in the paper we establish there is information about the failure encoded in the pulses prior to it, so we also present a list of feasible next steps for increasing pulse classification accuracy.
Thin carbon foils are used as strippers for charge exchange injection into high intensity proton rings. However, the stripping foils become radioactive and produce uncontrolled beam loss, which is one of the main factors limiting beam power in high intensity proton rings. Recently, we presented a scheme for laser stripping an H ÿ beam for the Spallation Neutron Source (SNS) ring. First, H ÿ atoms are converted to H 0 by a magnetic field, then H 0 atoms are excited from the ground state to the upper levels by a laser, and the excited states are converted to protons by a magnetic field. In this paper we report on the proof-ofprinciple demonstration of this scheme to give high efficiency (around 90%) conversion of H ÿ beam into protons at SNS in Oak Ridge. The experimental setup is described, and comparison of the experimental data with simulations is presented.
A new in-situ plasma processing technique is being developed at the Spallation Neutron Source (SNS) to improve the performance of the cavities in operation. The technique utilizes a low-density reactive oxygen plasma at room-temperature to remove top-surface hydrocarbons. The plasma processing technique increases the work function of the cavity surface and reduces the overall amount of vacuum and electron activity during cavity operation; in particular it increases the field-emission onset, which enables cavity operation at higher accelerating gradients. Experimental evidence also suggests that the SEY of the Nb surface decreases after plasma processing which helps mitigating multipacting issues. In this article, the main developments and results from the plasma processing R&D are presented and experimental results for in-situ plasma processing of dressed cavities in the SNS horizontal test apparatus are discussed. 2. FIELD EMISSION AND END-GROUP THERMAL INSTABILITY LIMITING THE ACCELERATING GRADIENTS IN THE SNS LINAC Field emission in superconducting radio-frequency (SRF) cavities is a well-known limiting factor for operation at high accelerating gradients [1-3]. Beyond certain electric field thresholds, the electrons from the metal surface of the cavity have a non-negligible probability of tunneling out. The field emitted electrons are accelerated by the stored electromagnetic fields in the cavity and subsequently deposit their energy by collision with the cavity radio-frequency (RF) surface leading to vacuum activity, increase of the surface temperature and Bremsstrahlung radiation. If the deposited energy-density is larger than the cooling capacity it can also lead to thermal breakdown of the superconductivity.
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