Apr 10th, 2009| 03:16 pm | Posted by vlk

Probability density functions are another way of summarizing the consequences of assuming a Gaussian error distribution when the true distribution is Poisson. We can compute the posterior probability of the intensity of a source, when some number of counts are observed in a source region, and the background is estimated using counts observed in a different region. We can then compare it to the equivalent Gaussian.

The figure below (AAS 472.09) compares the pdfs for the Poisson intensity (red curves) and the Gaussian equivalent (black curves) for two cases: when the number of counts in the source region is 50 (top) and 8 (bottom) respectively. In both cases a background of 200 counts collected in an area 40x the source area is used. The hatched region represents the 68% equal-tailed interval for the Poisson case, and the solid horizontal line is the ±1σ width of the equivalent Gaussian.

Clearly, for small counts, the support of the Poisson distribution is bounded below at zero, but that of the Gaussian is not. This introduces a visibly large bias in the interval coverage as well as in the normalization properties. Even at high counts, the Poisson is skewed such that larger values are slightly more likely to occur by chance than in the Gaussian case. This skew can be quite critical for marginal results. Continue reading ‘Poisson vs Gaussian, Part 2’ »

Aug 6th, 2008| 01:00 pm | Posted by vlk

As mentioned before, background subtraction plays a big role in astrophysical analyses. For a variety of reasons, it is not a good idea to subtract out background counts from source counts, especially in the low-counts Poisson regime. What Bayesians recommend instead is to set up a model for the intensity of the source and the background and to infer these intensities given the data. Continue reading ‘Background Subtraction, the Sequel [Eqn]’ »

Tags:

background,

background marginalization,

background subtraction,

EotW,

Equation,

Equation of the Week Category:

Astro,

Bayesian,

Data Processing,

High-Energy,

Imaging,

Jargon,

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May 21st, 2008| 01:00 pm | Posted by vlk

There is a lesson that statisticians, especially of the Bayesian persuasion, have been hammering into our skulls for ages: __do not subtract__ background. Nevertheless, old habits die hard, and old codes die harder. Such is the case with X-ray aperture photometry. Continue reading ‘Background Subtraction [EotW]’ »

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aperture photometry,

background,

background marginalization,

background subtraction,

celldetect,

Chandra,

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EotW,

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error propagation,

ldetect,

local detect,

wavdetect,

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Apr 20th, 2008| 09:05 pm | Posted by hlee

The dichotomy of outliers; detecting outliers to be discarded or to be investigated; statistics that is robust enough not to be influenced by outliers or sensitive enough to alert the anomaly in the data distribution. Although not related, one paper about outliers made me to dwell on what outliers are. This week topics are diverse. Continue reading ‘[ArXiv] 3rd week, Apr. 2008’ »

Tags:

background,

bootstrap,

calibration errors,

Cash statistics,

clusters,

CMB,

corona,

edge detection,

FFT,

gravitational lens,

maximum likelihood,

multiscale,

neural network,

outlier,

SDSS,

sunspot,

systematic errors,

topology,

WMAP,

XMM-Newton Category:

arXiv,

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