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Web application for resolution of DGS instruments: first release

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The new website should be pretty straightforward to navigate, but here are some quick explanations:

At the start, the instrument selection is shown on the top left of the page. Click on one instrument to start.

Click “Help” under the page banner will show some information about the data and modeling

For each instrument there are two tabs. One for inelastic energy resolution plots, one for elastic resolution plots.

In the inelastic page, there is a form to choose incident energy and some (chopper) settings. Click the “Calculate” button will calculate the resolution curve using those settings

Click “Summary” above the resolution-vs-energy plot will display some basic information such as FWHM at elastic line

Below summary, a polynomial fit result may be displayed (only CNCS for now)

Click the download button below the resolution-vs-energy plot will download a csv file

The elastic tab shows some plots about the elastic resolution/flux information. It is different from instrument to instrument, because different kinds of data exist for different instruments.

Super-resolution satellite image correlation helps study glacier erosion law

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Super-resolution satellite image correlation helps study glacier erosion law

published in Science on Oct 9, 2015. https://science.sciencemag.org/content/350/6257/193.

From Wikipedia: “A glacier is a persistent body of dense ice that is constantly moving under its own weight; it forms where the accumulation of snow exceeds its ablation (melting and sublimation) over many years, often centuries”

  • Glacier erosion has obvious effects on the landscape of the Earth
  • Glacial erosion rates span several orders of magnitude from polar and dry regions to temperate alpine glaciers, and from hill-slope landscapes to steep, tectonically active mountain ranges

Glacier erosion power law was proposed by Jonathan Harbor, Bernard Hallet, and Charles Raymond, Nature, 1988.

But what is value of b, the power factor?

Sub-pixel image correlation techniques for satellite imagery were developed and used in this study to obtain accurate 3D model of the glacier surface, and then its moving speed, u, and helped constrain the value of b.

  • ~50k lines of python/C++ code for Satellite Image Correlation
  • Include the full DEM workflow of ancillary data correction (bundle correction), tie-points generation, orthorectification, correlation, triangulation, and gridding

Energy dependence of the flux and elastic resolution for the ARCS neutron spectrometer

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Energy dependence of the flux and elastic resolution for the ARCS neutron spectrometer

Flux and elastic resolution for a wide range of instrument conditions were measured and modeled for the ARCS chopper spectrometer.

This work provides a key reference for users of the ARCS instrument, making it easier for users to plan for their experiments. The online interactive resolution/flux plots make it much easier for ARCS users to take educated tradeoff between resolution and flux, while taking their experiment requirements into account.

Research details:

  • A series of vanadium calibration measurements for a wide range of incident energies were performed for the ARCS direct geometry chopper spectrometer
  • Analytical models were able to reproduce most of the general trends observed in the measurements.
  • MCViNE simulations were used to confirm modeling results when experimental results are not reliable for direct measurement of resolution

This work was published at https://doi.org/10.1016/j.physb.2018.11.027.

iMars3D: Neutron tomography reconstruction

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iMars3D: Neutron tomography reconstruction

A snapshot of a neutron CT reconstruction.

Neutron has greater penetration depths and has different cross section dependency on elements than X-ray. Neutron tomography can be used to study a variety of larger samples and is finding applications in engineering, material science, biology, and archeology. This package is in use by ORNL neutron imaging beamlines for neutron CT reconstructions.

First automatic CT reconstruction for the CG-1D beamline at HFIR

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  • New metadata added to images acquired at CG-1D beamline to identify grouping of CT scans
  • Auto-discovery of the image files improved for a CT scan, including open beam and dark field images based on measurement time
  • Auto-cropping algorithm improved to work with more measurement scenarios, especially those with slits
  • New trigger from Cataloging service added to start the auto reduction service for CG1D
  • Auto-reconstruction script implemented
  • First successful auto-reconstruction happened Sept. 13, 2018