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TU2T1 | Collimation and Machine Protection for Low Emittance Rings | 196 |
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Funding: Work supported by Hard X-ray Sciences LDRD Project 2021-0119 and by the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, under Contract No. DE-AC02-06CH11357. The reduced emittance and concomitant increase in electron beam intensity in Fourth Generation Storage Ring (4GSR) light sources lead to the challenging machine protection problem of how to safely dispose of the circulating charge during unplanned whole-beam loss events. Two recent experiments conducted to study the effects of 4GSR whole-beam dumps showed that damage to candidate collimator materials can be severe. This is a paradigm shift for SR light source machine protection. Typically the biggest threat to the machine is from CW synchrotron radiation. The choice of collimator material is important. High-Z, high-density materials such as tungsten may appear effective for stopping the beam in static simulations; however, in reality, short radiation lengths will cause severe destructive hydrodynamic effects. In our experiments, significant damage was observed even in low-Z aluminum. Thus unplanned, whole-beam dumps cannot be stopped in a single collimator structure. In this tutorial, alternatives such as multiple collimators and fan-out abort kicker systems will be discussed. Collimator design strategy and foreseen diagnostics for their operation will also be presented. |
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DOI • | reference for this paper ※ doi:10.18429/JACoW-IBIC2022-TU2T1 | |
About • | Received ※ 08 September 2022 — Revised ※ 09 September 2022 — Accepted ※ 12 September 2022 — Issue date ※ 04 October 2022 | |
Cite • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | |
TUP39 | Neural Network Inverse Models for Implicit Optics Tuning in the AGS to RHIC Transfer Line | 327 |
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Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Nuclear Physics under Award Number DE-SC0019682 One of the fundamental challenges of using machine-learning-based inverse models for optics tuning in accelerators, particularly transfer lines, is the degenerate nature of the magnet settings and beam envelope functions. Moreover, it is challenging, if not impossible, to train a neural network to compute correct quadrupole settings from a given set of measurements due to the limited number of diagnostics available in operational beamlines. However, models that relate BPM readings to corrector settings are more forgiving, and have seen significant success as a benchmark for machine learning inverse models. We recently demonstrated that when comparing predicted corrector settings to actual corrector settings from a BPM inverse model, the model error can be related to errors in quadrupole settings. In this paper, we expand on that effort by incorporating inverse model errors as an optimization tool to correct for optics errors in a beamline. We present a toy model using a FODO lattice and then demonstrate the use of this technique for optics corrections in the AGS to RHIC transfer line at BNL. |
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DOI • | reference for this paper ※ doi:10.18429/JACoW-IBIC2022-TUP39 | |
About • | Received ※ 05 September 2022 — Revised ※ 10 September 2022 — Accepted ※ 11 September 2022 — Issue date ※ 12 November 2022 | |
Cite • | reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml) | |