Author: Abell, D.T.
Paper Title Page
MOPA50 Integrated Photonics Structure Cathodes for Longitudinally Shaped Bunch Trains 160
 
  • S.J. Coleman, D.T. Abell, C.C. Hall
    RadiaSoft LLC, Boulder, Colorado, USA
  • R. Kapadia
    University of Southern California, Los Angeles, California, USA
  • S.S. Karkare
    Arizona State University, Tempe, USA
  • S.Y. Kim, P. Piot, J.F. Power
    ANL, Lemont, Illinois, USA
  
  Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of High Energy Physics under Award Number DOE DE-SC0021681
Com­pact, high-gra­di­ent struc­ture wake­field ac­cel­er­a­tors can op­er­ate at im­proved ef­fi­ciency using shaped elec­tron beams, such as a high trans­former ratio beam shape, to drive the wakes. These shapes have gen­er­ally come from a pho­to­cath­ode gun fol­lowed by a trans­verse mask to im­print a de­sired shape on the trans­verse dis­tri­b­u­tion, and then an emit­tance ex­changer (EEX) to con­vert that trans­verse shape into a lon­gi­tu­di­nal dis­tri­b­u­tion. This process dis­cards some large frac­tion of the beam, lim­it­ing wall-plug ef­fi­ciency as well as leav­ing a solid ob­ject in the path of the beam. In this paper, we pre­sent a pro­posed method of using in­te­grated pho­ton­ics struc­tures to con­trol the emis­sion pat­tern on the cath­ode sur­face. This trans­verse pat­tern is then con­verted into a lon­gi­tu­di­nal pat­tern at the end of an EEX. This re­moves the need for the mask, pre­serv­ing the total charge pro­duced at the cath­ode sur­face. We pre­sent sim­u­la­tions of an ex­per­i­men­tal set-up to demon­strate this con­cept at the Ar­gonne Wake­field Ac­cel­er­a­tor. 		 
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA50  
About • Received ※ 03 August 2022 — Revised ※ 05 August 2022 — Accepted ※ 26 August 2022 — Issue date ※ 03 October 2022
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MOPA55 Facilitating Machine Learning Collaborations Between Labs, Universities, and Industry 164
 
  • J.P. Edelen, D.T. Abell, D.L. Bruhwiler, S.J. Coleman, N.M. Cook, A. Diaw, J.A. Einstein-Curtis, C.C. Hall, M.C. Kilpatrick, B. Nash, I.V. Pogorelov
    RadiaSoft LLC, Boulder, Colorado, USA
  • K.A. Brown
    BNL, Upton, New York, USA
  • S. Calder
    ORNL RAD, Oak Ridge, Tennessee, USA
  • A.L. Edelen, B.D. O’Shea, R.J. Roussel
    SLAC, Menlo Park, California, USA
  • C.M. Hoffmann
    ORNL, Oak Ridge, Tennessee, USA
  • E.-C. Huang
    LANL, Los Alamos, New Mexico, USA
  • P. Piot
    Northern Illinois University, DeKalb, Illinois, USA
  • C. Tennant
    JLab, Newport News, Virginia, USA
  
  It is clear from nu­mer­ous re­cent com­mu­nity re­ports, pa­pers, and pro­pos­als that ma­chine learn­ing is of tremen­dous in­ter­est for par­ti­cle ac­cel­er­a­tor ap­pli­ca­tions. The quickly evolv­ing land­scape con­tin­ues to grow in both the breadth and depth of ap­pli­ca­tions in­clud­ing physics mod­el­ing, anom­aly de­tec­tion, con­trols, di­ag­nos­tics, and analy­sis. Con­se­quently, lab­o­ra­to­ries, uni­ver­si­ties, and com­pa­nies across the globe have es­tab­lished ded­i­cated ma­chine learn­ing (ML) and data-sci­ence ef­forts aim­ing to make use of these new state-of-the-art tools. The cur­rent fund­ing en­vi­ron­ment in the U.S. is struc­tured in a way that sup­ports spe­cific ap­pli­ca­tion spaces rather than larger col­lab­o­ra­tion on com­mu­nity soft­ware. Here, we dis­cuss the ex­ist­ing col­lab­o­ra­tion bot­tle­necks and how a shift in the fund­ing en­vi­ron­ment, and how we de­velop col­lab­o­ra­tive tools, can help fuel the next wave of ML ad­vance­ments for par­ti­cle ac­cel­er­a­tors.  
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA55  
About • Received ※ 10 August 2022 — Revised ※ 11 August 2022 — Accepted ※ 22 August 2022 — Issue date ※ 01 September 2022
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MOPA57 Online Models for X-Ray Beamlines 170
 
  • B. Nash, D.T. Abell, M.V. Keilman, P. Moeller, I.V. Pogorelov
    RadiaSoft LLC, Boulder, Colorado, USA
  • Y. Du, A. Giles, J. Lynch, T. Morris, M.S. Rakitin, A. Walter
    BNL, Upton, New York, USA
  • N.B. Goldring
    STATE33 Inc., Portland, Oregon, USA
  
  Funding: This material is based upon work supported by the U.S. Department of Energy, Office of Science, Office of Basic Energy Science, under Award Number DE-SC0020593
X-ray beam­lines trans­port syn­chro­tron ra­di­a­tion from the mag­netic source to the sam­ple at a syn­chro­tron light source. Align­ment of el­e­ments such as mir­rors and grat­ings are often done man­u­ally and can be quite time con­sum­ing. The use of pho­ton beam mod­els dur­ing op­er­a­tions is not com­mon in the same way that they are used to great ben­e­fit for par­ti­cle beams in ac­cel­er­a­tors. Lin­ear and non-lin­ear op­tics in­clud­ing the ef­fects of co­her­ence may be com­puted from source prop­er­ties and aug­mented with mea­sure­ments. In col­lab­o­ra­tion with NSLS-II, we are de­vel­op­ing soft­ware tools and meth­ods to in­clude the model of the x-ray beam as it passes on its way to the sam­ple. We are in­te­grat­ing the Blue-Sky beam­line con­trol toolkit with the Sirepo in­ter­face to sev­eral x-ray op­tics codes. Fur­ther, we are de­vel­op­ing a sim­pli­fied lin­ear op­tics ap­proach based on a Gauss-Schell model and lin­ear canon­i­cal trans­forms as well as de­vel­op­ing Ma­chine Learn­ing mod­els for use di­rectly from di­ag­nos­tics data. We pre­sent progress on ap­ply­ing these ideas on NSLS-II beam­lines and give a fu­ture out­look on this rather large and open do­main for tech­no­log­i­cal de­vel­op­ment. 		 
DOI • reference for this paper ※ doi:10.18429/JACoW-NAPAC2022-MOPA57  
About • Received ※ 27 July 2022 — Revised ※ 02 August 2022 — Accepted ※ 07 August 2022 — Issue date ※ 11 August 2022
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)