Author: Vacaliuc, B.
Paper Title Page
MO2AO05 Deployment of ADTimePix3 areaDetector Driver at Neutron and X-ray User Facilities 90
 
  • K.J. Gofron, J. Wlodek
    BNL, Upton, New York, USA
  • S.C. Chong, F. Fumiaki, SG. Giles, G.S. Guyotte, SDL. Lyons
    ORNL, Oak Ridge, Tennessee, USA
  • B. Vacaliuc
    ORNL RAD, Oak Ridge, Tennessee, USA
 
  Funding: This work was supported by the U.S. Department of Energy, Office of Science, Scientific User Facilities Division under Contract No. DE-AC05-00OR22725.
TimePix3 is a 65k hy­brid pixel read­out chip with si­mul­ta­ne­ous Time-of-Ar­rival (ToA) and Time-over-Thresh­old (ToT) record­ing in each pixel*. The chip op­er­ates with­out a trig­ger sig­nal with a sparse read­out where only pix­els con­tain­ing events are read out. The flex­i­ble ar­chi­tec­ture al­lows 40 MHits/s/cm2 read­out through­put, using si­mul­ta­ne­ous read­out and ac­qui­si­tion by shar­ing read­out logic with trans­port logic of su­per­pixel ma­trix formed using 2x4 struc­ture. The chip ToA records 1.5625 ns time res­o­lu­tion. The X-ray and charged par­ti­cle events are counted di­rectly. How­ever, in­di­rect neu­tron counts use 6Li fis­sion in a scin­til­la­tor ma­trix, such as ZnS(Ag). The fis­sion space-charge re­gion is lim­ited to 5-9 um. A pho­ton from scin­til­la­tor ma­te­r­ial ex­cites a pho­to­cath­ode elec­tron, which is fur­ther mul­ti­plied in dual-stack MCP. The neu­tron count event is a clus­ter of elec­tron events at the chip. We re­port on the EPICS areaD­e­tec­tor** AD­TimePix3 dri­ver that con­trols Ser­val*** using json com­mands. The dri­ver di­rects data to stor­age and to a real-time pro­cess­ing pipeline and con­fig­ures the chip. The time-stamped data are stored in raw .tpx3 file for­mat and passed through a socket where the clus­ter­ing soft­ware iden­ti­fies in­di­vid­ual neu­tron events. The con­ven­tional 2D im­ages are avail­able as im­ages for each ex­po­sure frame, and a pre­view is use­ful for sam­ple align­ment. The areaD­e­tec­tor dri­ver al­lows in­te­gra­tion of time-en­hanced ca­pa­bil­i­ties of this de­tec­tor into SNS beam­lines con­trols and un­prece­dented time res­o­lu­tion.
*T Poikela et al 2014 JINST 9 C05013.
**https://github.com/areaDetector
***Software provided by the vendor (ASI) that interfaces detector (10GE) and EPICS data acquisition ioc ADTimePix3
 
slides icon Slides MO2AO05 [3.379 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-MO2AO05  
About • Received ※ 04 October 2023 — Revised ※ 08 October 2023 — Accepted ※ 13 October 2023 — Issued ※ 28 October 2023
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TUPDP113 A Flexible EPICS Framework for Sample Alignment at Neutron Beamlines 836
 
  • J.P. Edelen, M.J. Henderson, M.C. Kilpatrick
    RadiaSoft LLC, Boulder, Colorado, USA
  • S. Calder, B. Vacaliuc
    ORNL RAD, Oak Ridge, Tennessee, USA
  • R.D. Gregory, G.S. Guyotte, C.M. Hoffmann, B.K. Krishna
    ORNL, Oak Ridge, Tennessee, 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-SC0021555.
Ra­di­a­Soft has been de­vel­op­ing a flex­i­ble front-end frame­work, writ­ten in Python, for rapidly de­vel­op­ing and test­ing au­to­mated sam­ple align­ment IOCs at Oak Ridge Na­tional Lab­o­ra­tory. We uti­lize YAML-for­mat­ted con­fig­u­ra­tion files to con­struct a thin ab­strac­tion layer of cus­tom classes which pro­vide an in­ter­nal rep­re­sen­ta­tion of the ex­ter­nal hard­ware within a con­trols sys­tem. The ab­strac­tion layer takes ad­van­tage of the PCASPy and PyEpics li­braries in order to serve EPICS process vari­ables & re­spond to read/write re­quests. Our frame­work al­lows users to build a new IOC that has ac­cess to in­for­ma­tion about the sam­ple en­vi­ron­ment in ad­di­tion to user-de­fined ma­chine learn­ing mod­els. The IOC then mon­i­tors for user in­puts, per­forms user-de­fined op­er­a­tions on the beam­line, and re­ports on its sta­tus back to the con­trol sys­tem. Our IOCs can be booted from the com­mand line, and we have de­vel­oped com­mand line tools for rapidly run­ning and test­ing align­ment processes. These tools can also be ac­cessed through an EPICS GUI or in sep­a­rate Python scripts. This pre­sen­ta­tion pro­vides an overview of our soft­ware struc­ture and show­cases its use at two beam­lines at ORNL.
 
DOI • reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-TUPDP113  
About • Received ※ 06 October 2023 — Revised ※ 22 October 2023 — Accepted ※ 04 December 2023 — Issued ※ 16 December 2023
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TUPDP114 Machine Learning Based Noise Reduction of Neutron Camera Images at ORNL 841
 
  • I.V. Pogorelov, J.P. Edelen, M.J. Henderson, M.C. Kilpatrick
    RadiaSoft LLC, Boulder, Colorado, USA
  • S. Calder, B. Vacaliuc
    ORNL RAD, Oak Ridge, Tennessee, USA
  • R.D. Gregory, G.S. Guyotte, C.M. Hoffmann, B.K. Krishna
    ORNL, Oak Ridge, Tennessee, 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-SC0021555.
Neu­tron cam­eras are uti­lized at the HB2A pow­der dif­frac­tome­ter to image the sam­ple for align­ment in the beam. Typ­i­cally, neu­tron cam­eras are quite noisy as they are con­stantly being ir­ra­di­ated. Re­moval of this noise is chal­leng­ing due to the ir­reg­u­lar na­ture of the pixel in­ten­sity fluc­tu­a­tions and the ten­dency for it to change over time. Ra­di­a­Soft has de­vel­oped a novel noise re­duc­tion method for neu­tron cam­eras that in­scribes a lower en­ve­lope of the image sig­nal. This process is then sped up using ma­chine learn­ing. Here we re­port on the re­sults of our noise re­duc­tion method and de­scribe our ma­chine learn­ing ap­proach for speed­ing up the al­go­rithm for use dur­ing op­er­a­tions.
 
DOI • reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-TUPDP114  
About • Received ※ 07 October 2023 — Revised ※ 22 October 2023 — Accepted ※ 11 December 2023 — Issued ※ 16 December 2023
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TUPDP116 Machine Learning Based Sample Alignment at TOPAZ 851
 
  • M.J. Henderson, J.P. Edelen, M.C. Kilpatrick, I.V. Pogorelov
    RadiaSoft LLC, Boulder, Colorado, USA
  • S. Calder, B. Vacaliuc
    ORNL RAD, Oak Ridge, Tennessee, USA
  • R.D. Gregory, G.S. Guyotte, C.M. Hoffmann, B.K. Krishna
    ORNL, Oak Ridge, Tennessee, 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-SC0021555.
Neu­tron scat­ter­ing ex­per­i­ments are a crit­i­cal tool for the ex­plo­ration of mol­e­c­u­lar struc­ture in com­pounds. The TOPAZ sin­gle crys­tal dif­frac­tome­ter at the Spal­la­tion Neu­tron Source stud­ies these sam­ples by il­lu­mi­nat­ing sam­ples with dif­fer­ent en­ergy neu­tron beams and record­ing the scat­tered neu­trons. Dur­ing the ex­per­i­ments the user will change tem­per­a­ture and sam­ple po­si­tion in order to il­lu­mi­nate dif­fer­ent crys­tal faces and to study the sam­ple in dif­fer­ent en­vi­ron­ments. Main­tain­ing align­ment of the sam­ple dur­ing this process is key to en­sur­ing high qual­ity data are col­lected. At pre­sent this process is per­formed man­u­ally by beam­line sci­en­tists. Ra­di­a­Soft in col­lab­o­ra­tion with the beam­line sci­en­tists and en­gi­neers at ORNL has de­vel­oped a new ma­chine learn­ing based align­ment soft­ware au­tomat­ing this process. We uti­lize a fully-con­nected con­vo­lu­tional neural net­work con­fig­ured in a U-net ar­chi­tec­ture to iden­tify the sam­ple cen­ter of mass. We then move the sam­ple using a cus­tom python-based EPICS IOC in­ter­faced with the mo­tors. In this talk we pro­vide an overview of our ma­chine learn­ing tools and show our ini­tial re­sults align­ing sam­ples at ORNL.
 
DOI • reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-TUPDP116  
About • Received ※ 06 October 2023 — Accepted ※ 05 December 2023 — Issued ※ 11 December 2023  
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WE3AO03 Noise Mitigation for Neutron Detector Data Transport 1066
 
  • K.J. Gofron
    BNL, Upton, New York, USA
  • R. Knudson, C. Ndo
    ORNL, Oak Ridge, Tennessee, USA
  • B. Vacaliuc
    ORNL RAD, Oak Ridge, Tennessee, USA
 
  Funding: This work was supported by the U.S. Department of Energy, Office of Science, Scientific User Facilities Division under Contract No. DE-AC05-00OR22725.
De­tec­tor events at User Fa­cil­i­ties re­quire real-time fast trans­port of large data sets. Since con­struc­tion, the SNS user fa­cil­ity suc­cess­fully trans­ported data using an in-house so­lu­tion based on Chan­nel Link LVDS point-to-point data pro­to­col. Data trans­port so­lu­tions de­vel­oped more re­cently have higher speed and more ro­bust­ness; how­ever, the sig­nif­i­cant hard­ware in­fra­struc­ture in­vest­ment lim­its mi­gra­tion to them. Com­pared to newer so­lu­tions the ex­ist­ing SNS LVDS data trans­port uses only par­ity error de­tec­tion and LVDS frame error de­tec­tion. The used chan­nel link is DC cou­pled, and thus sen­si­tive to noise from the elec­tri­cal en­vi­ron­ment since it is dif­fi­cult to main­tain the same LVDS com­mon ref­er­ence po­ten­tial over an ex­ten­sive sys­tem of elec­tronic boards in de­tec­tor array net­works. The SNS ex­ist­ing Chan­nel Link* uses LVDS for data trans­port with clock of about 40 MHz and a mix­ture of par­al­lel and se­r­ial data trans­port. The 7 bits per twisted pair in each clock cycle are trans­ported over three pairs of Cat7 cable. The max­i­mum data rate is about 840 Mbps per cat7 cable. The DS90CR217 or DS90CR218 and SN65LVD­S32BD com­po­nents are used with shielded Cat7 ca­bling in trans­port­ing LVDS data. Here we dis­cuss noise mit­i­ga­tion meth­ods to im­prove data trans­port within the ex­ist­ing as build in­fra­struc­ture. We con­sider the role of shield­ing, ground loops, as well as specif­i­cally the use of toric fer­rite in­so­la­tion trans­former for rf noise fil­ter­ing.
* K. Vodopivec et al., "High Throughput Data Acquisition with EPICS", 16th ICALEPCS, 2017, Barcelona Spain, doi: 10.18429/JACoW-ICALEPCS2017-TUBPA05
 
slides icon Slides WE3AO03 [3.420 MB]  
DOI • reference for this paper ※ doi:10.18429/JACoW-ICALEPCS2023-WE3AO03  
About • Received ※ 04 October 2023 — Revised ※ 11 October 2023 — Accepted ※ 18 December 2023 — Issued ※ 22 December 2023
Cite • reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)