Automated galaxy surface photometry - I. Technique, calibration and validation

Cawson, M. G. M.; Kibblewhite, E. J.; Disney, M. J.; Phillipps, Steven

doi:10.1093/mnras/224.3.557

Cited by 14 publications

(10 citation statements)

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“…The output from the APM is an optical density. Cawson et al (1987) showed that the optical density calculated by the APM is, to a good approximation, linearly related to the incident intensity on the photographic plate for the typical range of optical density encountered for this type of extraction. Once scanned, the digitized plates were reduced and the spectra extracted off-line.…”

Section: Observational Datamentioning

confidence: 89%

Physical parametrization of stellar spectra: the neural network approach

Bailer‐Jones

Irwin

Gilmore

et al. 1997

Monthly Notices of the Royal Astronomical Society

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We present a technique which employs artificial neural networks to produce physical parameters for stellar spectra. A neural network is trained on a set of synthetic optical stellar spectra to give physical parameters (e.g. T eff , log g, [M/H]). The network is then used to produce physical parameters for real, observed spectra.Our neural networks are trained on a set of 155 synthetic spectra, generated using the spectrum program written by Gray , Gray & Arlt 1996. Once trained, the neural network is used to yield T eff for over 5000 B-K spectra extracted from a set of photographic objective prism plates (Bailer-Jones, Irwin & von Hippel 1997a). Using the MK classifications for these spectra assigned by Houk (1975, 1978, 1982, 1988), we have produced a temperature calibration of the MK system based on this set of 5000 spectra. It is demonstrated through the metallicity dependence of the derived temperature calibration that the neural networks are sensitive to the metallicity signature in the real spectra. With further work it is likely that neural networks will be able to yield reliable metallicity measurements for stellar spectra.

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Section: Observational Datamentioning

confidence: 89%

Physical parametrization of stellar spectra: the neural network approach

Bailer‐Jones

Irwin

Gilmore

et al. 1997

Monthly Notices of the Royal Astronomical Society

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“…Thus 5780 deg 2 of H i data, in the velocity range between −1280 and +12 700 km s −1 , were searched largely by eye to come up with 1107 sources (Garcia‐Appadoo 2005). The Equatorial Strip was chosen because (i) it was approximately perpendicular to the Galactic plane and so it is mostly dark enough to make LSBGs detectable; (ii) it is accessible for follow up with a large range of instruments including the Very Large Array (VLA), and for some of its area the SDSS‐DR2; (iii) it includes the area searched for LSBGs by Impey et al (1996) using the Automated Plate Measurement (APM) machine (Cawson et al 1987) to scan United Kingdom Schmidt plates. This should eventually lead to an estimate of the number of LSBGs still missing from wide‐scale optical surveys.…”

Section: Introductionmentioning

confidence: 99%

Correlations among the properties of galaxies found in a blind H i survey, which also have SDSS optical data

Garcia‐Appadoo¹,

West²,

Dalcanton³

et al. 2009

Monthly Notices of the Royal Astronomical Society

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We have used the Parkes Multibeam system and the Sloan Digital Sky Survey to assemble a sample of 195 galaxies selected originally from their H I signature to avoid biases against unevolved or low surface brightness objects. For each source nine intrinsic properties are measured homogeneously, as well as inclination and an optical spectrum. The sample, which should be almost entirely free of either misidentification or confusion, includes a wide diversity of galaxies ranging from inchoate, low surface brightness dwarfs to giant spirals. Despite this diversity there are five clear correlations among their properties. They include a common dynamical mass-to-light ratio within their optical radii, a correlation between surface brightness and luminosity and a common H I surface density. Such correlation should provide strong constrains on models of galaxy formation and evolution.

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“…The APM parameters are calculated using emulsion density D , rather than incident flux f measurements. Below the emulsion saturation D max , it is fairly well approximated by a linear relationship, where α and β are constants for any particular part of the plate (Cawson et al 1987; Maddox et al 1990a,b), and the value of D max is found to vary by 2 per cent across the field (Maddox et al 1990b). The incident flux f is the sum of the sky flux f sky and the object flux f obj .…”

Section: Measurement Of Surface Brightnessmentioning

confidence: 98%

“…They are directly related to true SB only if there is a linear relationship between the real flux and the APM density (). Clearly the emulsion saturation violates this assumption, and CCD observations show that it is not exactly linear below saturation (Cawson et al 1987). Our fitting technique approximates the saturation as a simple maximum APM density, and ignores any non‐linearity below the saturation density.…”

Section: Measurement Of Surface Brightnessmentioning

confidence: 99%

Surface brightness measurements for APM galaxies

Shao

Maddox

Jones

et al. 2003

Monthly Notices of the Royal Astronomical Society

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This paper considers some simple surface brightness (SB) estimates for galaxies in the Automated Plate Measuring Machine (APM) catalogue in order to derive homogeneous SB data for a very large sample of faint galaxies. The isophotal magnitude and area are used to estimate the central surface brightness and total magnitude based on the assumption of an exponential SB profile. The surface brightness measurements are corrected for field effects on each UK Schmidt plate and the zero-point of each plate is adjusted to give a uniform sample of SB and total magnitude estimates over the whole survey. Results are obtained for 2.4 million galaxies with blue photographic magnitudes brighter than b_J = 20.5 covering 4300 deg^2 in the region of the south galactic cap. Almost all galaxies in our sample have central surface brightness in the range 20 to 24 b_J mag per arcsec^2. The SB measurements we obtain are compared to previous SB measurements and we find an acceptable level of error of +/- 0.2 b_J mag per arcsec^2. The distribution of SB profiles is considered for different galaxy morphologies for the bright APM galaxies. We find that early-type galaxies have more centrally concentrated profiles.Comment: 14 pages, 17 figures, accepted for publication in MNRA

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Automated galaxy surface photometry - I. Technique, calibration and validation

Cited by 14 publications

References 0 publications

Physical parametrization of stellar spectra: the neural network approach

Physical parametrization of stellar spectra: the neural network approach

Correlations among the properties of galaxies found in a blind H i survey, which also have SDSS optical data

Surface brightness measurements for APM galaxies

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