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CHRNS: Event mode software


With the improvements in hardware, sample environment, and new data packets/storage of events we require new software for the production, distribution, caching, saving, histograming, reducing, fitting, monitoring, and feedback to users/NICE. 

  • CHRNS Computing resources available via ORCiD login within NIST
  • DAVE and Mslice for MACS
  • SASView for VSANS
  • REF1D for CANDOR
  • Developing necessary web-based tools to perform all other tasks

Web-based tools, fitting, and experimental optimizations

Refl1D/CANDOR data fitting

  • Enabled remote (web-based) access to a 'live' fitting server for interactive fitting sessions, or static fit results for batch fits for the Refl1D analysis software, which is used for analysis of data from the CHRNS CANDOR instrument.
  • The interface is detachable and re-attachable to the fitting session at any time
  • Parameter error bars are determined with MCMC, correlations, fit history etc. visually represented
  • Necessary for Autonomous Experiments
  • Low-code fitting development: 
    • Simple web app lets users build physical models
    • Compare model to data directly in-app
    • Code is generated from the visual model representation and piped to Refl1D
Low code Reflectivity model maker
A low code app to make models and code for advanced fitting

Experimental optimization of the neutron instrument, measurement, and sample to greatly reduce measurement times and increase data quality. 

  • Remote Execution of CANDOR experimental optimizations 
  • Jupyter Notebook data fitting using Refl1D
  • Feature-complete experimental optimization for bio materials
    • C++ classes wrapped in Python for speed and compatibility
    • Declarative model langue for use in Refl1D
  • See section on CANDOR Fluids handling robot for 1) ‘immediate’ feedback to the user on the information content in a measurement 2) a mechanism for feeding results from these routines back into the NICE control software to be used in intelligent, automated determinations of how to efficiently complete the measurements.
Experimental optimization on CANDOR and vSANS   using Bayesian statistics and information theory  

SASView/VSANS data fitting and modeling

  • decoupled GUI from backend computations 

  • GPU/CPU multiprocessor support for fitting

Intelligent experiment-design and experiment-planning tools using predictions of information content based on user-supplied models (bio/soft materials initially) 

  • Remote Execution of SANS experimental optimizations 
  • Jupyter Notebooks data fitting using SASVIEW
  • Feature-complete experimental optimization 
    • lipid vesicle molecular model
    • Any SASView model
    • Monte Carlo Markov Chain fitting of SANS data
    • Instrument resolution
      • also needed to classifying SANS patterns with machine learning (in collaboration with nSOFT)
    • While instrument optimization isn't too helpful on VSANS, the ‘immediate’ feedback to the user on the information content in a measurement and statistical error analysis for a given model(s) can be used to reduce the amount of overcounting on SANS. We will implement this first on CANDOR and use that understanding with the above infrastructure to provide feedback to users/the NICE control software to be used in intelligent, automated determinations of how to efficiently complete the measurements.
Information theory _SANS

Preliminary web-interface for data retrieval/histograming of VSANS events

  •  Scripts for all data files from an experiment 
  •  Graphical view of detector counts vs. time (1 sec default for now)
  •  Click-drag to histogram a range
  •  Automatic file naming
SANS timeresolved data reduction
A web tool to quickly histogram ranges of SANS data 

DAVE and Mslice

Used for reducing MACS data and has been adapted to visualize time-slice data, taking special care of the error bar due to low count rate of the event mode.

MACS time-dependent slices
Slices of data as a function of time for MACS

AI error monitoring

AI/ML routines providing real-time reporting of unexpected experimental conditions, such as significant statistical data fluctuations or sample environment deviations, will be developed. User alerts and automated instrument responses can be added

AI and statistical error monitoring scheme


Created February 5, 2023