Review of Scientific Instruments
   
 
 
 
Previous Article
Calibration of a Thomson scattering diagnostic for fluctuation measurements
Detailed calibrations of the Madison Symmetric Torus polychromator Thomson scattering system have been made suitable for electron temperature fluctuation measurements. All calibrations have taken plac...
Next Article
A pulse-burst laser system for a high-repetition-rate Thomson scattering diagnostic
A “pulse-burst” laser system is being constructed for addition to the Thomson scattering diagnostic on the Madison Symmetric Torus (MST) reversed-field pinch. This laser is designed to pro...

Optimizing a Thomson scattering diagnostic for fast dynamics and high background

Rev. Sci. Instrum. 79, 10E735 (2008); doi:10.1063/1.2957844

Published 31 October 2008

You are not logged in to this journal. Log in

R. O'Connell, D. J. Den Hartog, M. T. Borchardt, D. J. Holly, J. A. Reusch, and H. D. Stephens
University of Wisconsin-Madison, 1150 University Avenue, Madison, Wisconsin 53706, USA
The Madison Symmetric Torus (MST) presents challenging conditions for Thomson scattering (TS) measurements. The MST plasmas are reversed-field pinches (RFPs) with electron density ne<3×1013  cm−3, typically 1×1013  cm−3. The TS system was designed to measure from 10  eV  to  2  keV; however, six polychromators were upgraded from four to eight spectral channels to resolve to 10  keV. There is no diverter or vertical field, so wall interaction results in high background light both from ion and neutral bremsstrahlungs and from line radiation. Also during standard plasmas, the RFP exhibits regular reconnection sawteeth events during which the plasma current, density, and temperature profiles are flattened. These events are of interest both due to the reconnection physics and to their contribution to the MST equilibrium and confinement. These events occur over 100  µS and exhibit large changes in background light and fast changes in temperature. During improved confinement plasmas, there are no sawteeth; the background is low but the temperature can be over an order of magnitude higher. Data analysis of the system has been developed to accommodate both the large dynamic range of the temperature, the fast dynamics, and the fast changing, high amplitude background. Special attention has been paid to the sources of error, in particular, the contribution of the background. A response-function method reduces the measured uncertainty by a factor of 2. Numerical techniques have been developed which are extremely robust. Two methods are used, a conventional chi2 minimization using a Levenberg–Marquardt algorithm coupled with Monte Carlo modeling for the error bar and a Bayesian statistics method. The Bayesian method computes the probability distribution for the measured photons and electron temperature and this information can be used to ensemble data and will allow future integrated data analysis efforts. ©2008 American Institute of Physics
History: Presented 13 May 2008; received 12 May 2008; accepted 9 June 2008; published 31 October 2008
Permalink: http://link.aip.org/link/?RSINAK/79/10E735/1
BUY THIS ARTICLE   (US$28)
Download PDF (152 kB) View Cart

KEYWORDS and PACS

Keywords
PACS
  • 52.70.Kz
    Optical (ultraviolet, visible, infrared) plasma diagnostic measurements
  • 52.65.Pp
    Monte Carlo methods (plasma simulation)
  • 52.55.Lf
    Field-reversed configurations, rotamaks, Astrons, ion rings, magnetized target fusion, and cusps
  • 52.25.Fi
    Plasma transport properties
  • YEAR: 2008

PUBLICATION DATA

ISSN:
0034-6748 (print)   1089-7623 (online)
Publisher:
AIP is a member of CrossRef AIP

REFERENCES (13)

For access to fully linked references, you need to log in. For access to fully linked references, you need to Log in.
  1. J. A. Reusch, D. J. Den Hartog, D. Holly, R. O'Connell, and H. D. Stephens, Rev. Sci. Instrum. (unpublished).
  2. The Center for Magnetic Self-Organization in Astrophysical Laboratory Plasmas (http://www.cmso. info).
  3. M. Jakobi, B. Kurzan, H. Murmann, and J. t. Neuhauser, 29th EPS Conference on Plasma Physics and Controlled Fusion, Montreux, Switzerland, 2002 (unpublished), Vol. 26B.
  4. H. McLean, J. Moller, and D. Hill, Rev. Sci. Instrum. 75, 3887 (2004).
  5. D. Sivia and J. Skilling, Data Analysis, A Bayesian Tutorial (Oxford Science, Oxford, U.K., 2006).
  6. A. Selden, Phys. Lett. 79, 405 (1980).
  7. B. Kurzan, M. Jakobi, and H. Murmann, Plasma Phys. Controlled Fusion 46, 299 (2004).
  8. H. D. Stephens, M. Borchardt, D. J. Den Hartog, A. Falkowski, D. Holly, R. O'Connell, and J. A. Reusch, Rev. Sci. Instrum. 79, 10E734 (2008).
  9. W. Press, B. Flannery, S. Teukolsky, and W. Vetterling, Numerical Recipes in C: The Art of Scientific Computing (Cambridge University Press, Cambridge, 1992).
  10. R. Fischer, C. Wendland, A. Dinklage, S. Gori, V. Dose, and W.-A. Team, Plasma Phys. Controlled Fusion 44, 1501 (2002).
  11. M. Stoneking and D. J. Den Hartog, Rev. Sci. Instrum. 68, 914 (1997).
  12. Apple Inc. (URL http://www.apple. com/server/macosx/technology/xgrid. html).
  13. R. Rivest, MIT Laboratory for Computer Science, and RSA Data Security, Inc., RFC 1321.

CITING ARTICLES

For access to citing articles, you need to log in.
For access to citing articles, you need to Log in.