Number-counts slope estimation in the presence of Poisson noise

Astronomy and Astrophysics – Astrophysics

Scientific paper

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

29

Data Sampling, Distribution Functions, Poisson Density Functions, X Ray Sources, Background Noise, Bias, Error Analysis, Mathematical Models, Signal To Noise Ratios

Scientific paper

The slope determination of a power-law number flux relationship in the case of photon-limited sampling. This case is important for high-sensitivity X-ray surveys with imaging telescopes, where the error in an individual source measurement depends on integrated flux and is Poisson, rather than Gaussian, distributed. A bias-free method of slope estimation is developed that takes into account the exact error distribution, the influence of background noise, and the effects of varying limiting sensitivities. It is shown that the resulting bias corrections are quite insensitive to the bias correction procedures applied, as long as only sources with signal-to-noise ratio five or greater are considered. However, if sources with signal-to-noise ratio five or less are included, the derived bias corrections depend sensitively on the shape of the error distribution.

No associations

LandOfFree

Say what you really think

Search LandOfFree.com for scientists and scientific papers. Rate them and share your experience with other people.

Rating

Number-counts slope estimation in the presence of Poisson noise does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.

If you have personal experience with Number-counts slope estimation in the presence of Poisson noise, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Number-counts slope estimation in the presence of Poisson noise will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFWR-SCP-O-1148771

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.