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IBEXMagnetospheric ENA Foreground:

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Finding ENA from the Heliosphere, R. ... Search for a heliospheric signal in the HENA data that is not due to ... from start and stop accumulator data ... – PowerPoint PPT presentation

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Title: IBEXMagnetospheric ENA Foreground:


1
IBEX--Magnetospheric ENA Foreground Lessons
Learned from IMAGE/HENA Edmond C. Roelof Johns
Hopkins University/Applied Physics
Laboratory Laurel, MD 20723-6099 IBEX Science
Team Meeting 8 November 2006, Oxnard CA Finding
ENA from the Heliosphere, R. DeMajistre, E.
Roelof, P. Brandt, and D. Mitchell,
IMAGE/HENA,September, 2002.
2
Basic Task
  • Search for a heliospheric signal in the HENA data
    that is not due to
  • Magnetospheric ENA
  • Ion contamination
  • Sun/Background UV contamination
  • This will be our cleanest estimate of an upper
    bound on heliospheric ENA
  • VERY low count rate (a few/hour)

3
Approach
Select sky pixels for averaging
Select data at apogee
Preliminary geometric restrictions
Estimate and subtract accidentals
Solar particle/ Geomagnetic activity restrictions
Bin filtered data in sky pixels
Final geometric restrictions
Final spectrum/images
4
Initial data selection
  • All low energy MCP data extracted from UDF where
    IMAGE position gt 97 of apoapsis (3 hour
    segments).
  • Save all pixels looking gt 90 degrees from the
    sun, gt 90 degrees from earth and with a minimum L
    along the LOS gt 20

5
Data selection
  • Restrict data to
  • AP lt 10
  • ACE/EPAM P2 channel reading lt 8x104
  • Number of valid points in orbit gt 1000
  • Accidental estimation lt actual count rate

6
Proton activity selection
7
Final geometric restrictions
  • Pixels additionally restricted
  • Sun angle gt 120 deg
  • Earth angle gt 120 deg
  • Minimum L gt 40
  • These restrictions were based on viewing data
    binned in GSE and ECI coordinates with the
    preliminary restrictions

8
Sky binning
  • Regular grid on sky set out 10 degree bins
  • Count rate shared with nearest 4 points
  • Both exposure and count grids accumulated

X
X
X
X
9
Accidental rate estimation
  • Accidentals estimated from start and stop
    accumulator data
  • Accounts for dead time, sequential windows and
    software selection
  • Estimated rate exceeds actual rate only in the
    lowest energy bin (longest TOF)

10
Binning in GSE
Raw Data Relative scale
11
Binning in GSE
Accidental Estimation Relative scale
12
Binning in GSE
Accidental subtracted Relative scale
13
Binning in GECI
Accidental subtracted Relative scale
14
Binning in Apex Coordinates
Accidental subtracted Relative scale
15
  • Summary
  • Lessons to be learned from IMAGE/HENA search for
  • Heliospheric ENAs
  • Quantify instrumental backgrounds, e.g.,
    accidentals
  • 2. Excise data contaminated by SEPs, large ap
    storms
  • 3. Exclude regions of magnetospheric ENA
    emission
  • ring current, plasma sheet, cusp, etc.
  • 4. Bin data (and quantified backgrounds)
    properly in sky
  • nearest-point accumulations of counts
  • nearest-point accumulation occupation time
  • corrections for scanning duty cycle
  • Bin data in multiple coordinate systems in order
    to
  • identify possible contamination sources
  • e.g., GSE,GECI,Upwind
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