[FFA_CEBAF_Collab] [EXTERNAL] Re: Identification of Magnetic Field Errors in Synchrotrons based on Deep Lie Map Networks

[Brooks, Stephen]

It's up to you, but I wouldn't be so negative about the FOA for a couple of reasons:

1.  Last year's version of this FOA apparently had an unusually low amount of funding, so our odds could be improved this time.  And it's an easy resubmit with minor updates.

2.  Without a FOA or other funding channel, BNL (or Cornell) staff are not "officially" funded to work on the CEBAF upgrade.  So while I'm helping out of general interest in FFAs (and permanent magnets), larger blocks of work would might not be possible.

I'm also not sure that being on the long-range plan is make-or-break for this funding call (although obviously it would help), they'd probably take into account the overall quality.

     -Stephen

________________________________________
From: FFA_CEBAF_Collab <ffa_cebaf_collab-bounces at jlab.org> on behalf of Jay Benesch via FFA_CEBAF_Collab <ffa_cebaf_collab at jlab.org>
Sent: 13 January 2023 09:52
To: ffa at cebaf
Subject: [FFA_CEBAF_Collab] Identification of Magnetic Field Errors in Synchrotrons based on Deep Lie Map Networks

Since this is based on MAD-X/SixTrackLib it might be useful for the FFA error analysis and later to set corrector dipoles and quads when built.   Synchrotrons have a lot more passes, of course.  The size of the errors chosen, page 6, is interesting.   Different from the approach taken so far as I understand it, using absolute values of errors rather than fractions of tune and chromaticity.  YMMV

I trust everyone is aware of the ARDAP FOA https://urldefense.com/v3/__https://science.osti.gov/ardap/Funding-Opportunities__;!!P4SdNyxKAPE!EwnG0lNXBnvvkk4_zTeVViPzX1RwH0PIUy3crOiSMoPxPiBmSEc7NXVzqvPshFJ2rjVCts90hMr4-E5YPOyc861CKpg$
https://urldefense.com/v3/__https://science.osti.gov/ardap/-/media/grants/pdf/foas/2023/SC_FOA_0002951.pdf__;!!P4SdNyxKAPE!EwnG0lNXBnvvkk4_zTeVViPzX1RwH0PIUy3crOiSMoPxPiBmSEc7NXVzqvPshFJ2rjVCts90hMr4-E5YPOycmfiRLbA$
and that this group will NOT apply until the energy upgrade effort appears in the LRP, given response last year.

https://urldefense.com/v3/__https://arxiv.org/abs/2301.04914__;!!P4SdNyxKAPE!EwnG0lNXBnvvkk4_zTeVViPzX1RwH0PIUy3crOiSMoPxPiBmSEc7NXVzqvPshFJ2rjVCts90hMr4-E5YPOycKUcJxa0$

Identification of Magnetic Field Errors in Synchrotrons based on Deep Lie Map Networks
Conrad Caliari, Adrian Oeftiger, Oliver Boine-Frankenheim

    Magnetic field errors pose a limitation in the performance of synchrotrons, as they excite non-systematic resonances, reduce dynamic aperture and may result in beam loss. Their effect can be compensated assuming knowledge of their location and strength. Established identification procedures are based on orbit response matrices or resonance driving terms. While they sequentially build a field error model for subsequent accelerator sections, a method detecting field errors in parallel could save valuable beam time. We introduce deep Lie map networks, which enable construction of an accelerator model including multipole components for the magnetic field errors by linking charged particle dynamics with machine learning methodology in a data-driven approach. Based on simulated beam-position-monitor readings for the example case of SIS18 at GSI, we demonstrate inference of location and strengths of gradient and sextupole errors for all accelerator sections in parallel. The obt!
 ained refined accelerator model may support setup of corrector magnets in operation to allow more precise control over tunes, chromaticities and resonance compensation.

Subjects:       Accelerator Physics (physics.acc-ph)
Cite as:        arXiv:2301.04914 [physics.acc-ph]
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