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Title: Linear feature detection algorithm for astronomical surveys – II. Defocusing effects on meteor tracks

Journal Article · · Monthly Notices of the Royal Astronomical Society
 [1];  [2];  [3];  [4]
  1. Science and Society Synergy Inst., Cakovec (Croatia)
  2. Science and Society Synergy Inst., Cakovec (Croatia); Hipersfera d.o.o., Zagreb (Croatia)
  3. SLAC National Accelerator Lab., Menlo Park, CA (United States)
  4. Univ. of Washington, Seattle, WA (United States). Dept. of Astronomy

Given the current limited knowledge of meteor plasma micro-physics and its interaction with the surrounding atmosphere and ionosphere, meteors are a highly interesting observational target for high-resolution wide-field astronomical surveys. Such surveys are capable of resolving the physical size of meteor plasma heads, but they produce large volumes of images that need to be automatically inspected for possible existence of long linear features produced by meteors. Here in this paper, we show how big aperture sky survey telescopes detect meteors as defocused tracks with a central brightness depression. We derive an analytic expression for a defocused point source meteor track and use it to calculate brightness profiles of meteors modelled as uniform brightness discs. We apply our modelling to meteor images as seen by the Sloan Digital Sky Survey and Large Synoptic Survey Telescope telescopes. The expression is validated by Monte Carlo ray-tracing simulations of photons travelling through the atmosphere and the Large Synoptic Survey Telescope telescope optics. We show that estimates of the meteor distance and size can be extracted from the measured full width at half-maximum and the strength of the central dip in the observed brightness profile. However, this extraction becomes difficult when the defocused meteor track is distorted by the atmospheric seeing or contaminated by a long-lasting glowing meteor trail. The full width at half-maximum of satellite tracks is distinctly narrower than meteor values, which enables removal of a possible confusion between satellites and meteors.

Research Organization:
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Sponsoring Organization:
USDOE Office of Science (SC); National Science Foundation (NSF); Alfred P. Sloan Foundation
Grant/Contract Number:
AC02-76SF00515
OSTI ID:
1423517
Journal Information:
Monthly Notices of the Royal Astronomical Society, Vol. 474, Issue 4; ISSN 0035-8711
Publisher:
Royal Astronomical SocietyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 5 works
Citation information provided by
Web of Science

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Cited By (2)

MAXIMASK and MAXITRACK: Two new tools for identifying contaminants in astronomical images using convolutional neural networks journal February 2020
Optimal and Efficient Streak Detection in Astronomical Images journal October 2018

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