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Data Reduction and Analysis Techniques

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Chi-square of the fit. Parameter standard deviations. Template Fitting. Create a template: ... least-squares fit of the template to the data: Template Fitting ... – PowerPoint PPT presentation

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Title: Data Reduction and Analysis Techniques


1
Data Reduction and Analysis Techniques
  • Ronald J. Maddalena

2
Existing Analysis Systems
3
Continuum - Point SourcesOn-Off Observing
4
Continuum - Point SourcesOn-Off Observing
5
Continuum - Point SourcesOn-Off Observing
  • Known
  • Equivalent temperature of noise diode or
    calibrator (Tcal) 3 K
  • Bandwidth (BW) 10 MHz
  • Gain 2 K / Jy
  • Desired
  • Antenna temperature of the source (Tsrc)
  • Flux density (S) of the source.
  • System Temperature(Tsys)
  • Accuracy of antenna temperature (ssrc)

6
Continuum - Point SourcesOn-Off Observing
7
Continuum - Point SourcesAssumptions
  • Narrow bandwidths,
  • Linear power detector,
  • Source intensity ltlt Tsys,
  • Noise diode temperature ltlt Tsys,
  • treference tsignal
  • tcal_on tcal_off
  • Blanking time ltlt tsignal

8
Phases of a ObservationTotal Power
9
Phases of a ObservationTotal Power
10
Continuum - Point SourcesTotal Power
11
Phases of a ObservationSwitched Power
12
Phases of a ObservationSwitched Power
13
Continuum - Point SourcesBeam-Switched
Observation
14
Continuum - Point SourcesOn-The-Fly Observation
15
Continuum - Point SourcesOn-The-Fly Observation
If total power
If beam-switching (switched power)
16
Baseline Fitting Polynomials
  • Set order of polynomial
  • Define areas devoid of emission.
  • ---------------------------
  • Creates false features
  • Introduces a random error to an observation,

17
Continuum - Point Sources Gaussian Fitting
  • Restrict data to between the half power points,
  • Define initial guesses
  • Set flags to fit or hold constant each parameter
  • Set number of iterations
  • Set convergence criteria
  • ----------------------------
  • Fitted parameters
  • Chi-square of the fit
  • Parameter standard deviations.

18
Template Fitting
  • Create a template
  • Sufficient knowledge of the telescope beam, or
  • Average of a large number of observations.
  • ---------------
  • Convolve the template with the data gt x-offset.
  • Shift by the x-offset.
  • Perform a linear least-squares fit of the
    template to the data

19
Template Fitting
20
Averaging Data
  • Tsys changes due to atmosphere emission.
  • Tant changes due to atmosphere opacity.
  • --------------------
  • Use weighted average
  • Weights 1/s2.

21
Continuum - Extended Sources On-The-Fly Mapping
  • Telescope slews
  • A few samples /sec.
  • Highly oversampled.
  • Could be beam switching
  • ----------------------
  • Convert Power into Tant.
  • Fit baseline to each row?
  • Grid into a matrix

22
Continuum - Extended Sources On-The-Fly Mapping
- Common Problems
  • Striping (Emerson 1995 Klein and Mack 1995).
  • If beam-switched, Emerson, Klein, and Haslam
    (1979) to reconstruct the image.
  • Make multiple maps with the slew in diferent
    direction.

23
Spectral-Line - Point SourcesPosition-Switched
Observing
24
Spectral-Line - Point SourcesPosition-Switched
Observing
25
Spectral-Line - Point SourcesFrequency-Switched
Observing - In band
Tsys(REF)(SIG-REF)/REF - (SIG-REF)/REF
26
Spectral-Line - Point SourcesFrequency-Switched
Observing - In Band
Tsys(REF)(SIG-REF)/REF Tsys(SIG)(REF-SIG)/SI
G
27
Spectral-LineBaseline Fitting
  • Polynomial same as before
  • Sinusoid

28
Spectral-LineRipples
29
Spectral-LineOther Algorithms
  • Averaging Weighted by 1/s2
  • Velocity Calibration
  • Velocity/Frequency Shifting
  • Doppler tracking limitations
  • Gaussian fitting
  • Multi-component fits should be done
    simultaneosuly
  • Smoothing
  • Decimating vs. non-decimating routines
  • Moments for Integrated Intensities Velocity
    centroids, .

30
Spectral-LineRFI Excision
31
Spectral-Line MappingGrid and On-the-Fly
Velocity
DEC
RA
32
Spectral-Line MappingGrid and On-the-Fly
(If V1V2 gt Channel Map)
Velocity
For vvmin v ltvmax v if T(a,d,v) gt
Tmin then W(a,d)W(a,d) T(a,d,v)
endif endfor
DEC
RA
33
Spectral-Line MappingGrid and On-the-Fly
Velocity
(Position-velocity map)
DEC
RA
34
Spectral-Line MappingRudimentary Analysis
35
ConclusionThe Future of Single-Dish Data Analysis
  • Increase in the use of RDBMS.
  • Support the analysis of archived data.
  • Sophisticated visualization tools.
  • Sophisticated, robust algorithms (mapping).
  • Data pipelining for the general user.
  • Automatic data calibration using sophisticated
    models of the telescope.
  • Algorithms that deal with data sets.
  • Analysis systems supported by cross-observatory
    groups
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