arXiv subjects

Mike Irwin

Publications and source records attributed to Mike Irwin.

76 records · Page 5Linked to original sources

arXiv1998

Semi-automated Extraction of Digital Objective Prism Spectra

We describe a method for the extraction of spectra from high dispersion objective prism plates. Our method is a catalogue driven plate solution approach, making use of the Right Ascension and Declination coordinates for the target objects. In contrast to existing methods of photographic plate reduction, we digitize the entire plate and extract spectra off-line. This approach has the advantages that it can be applied to CCD objective prism images, and spectra can be re-extracted (or additional spectra extracted) without having to re-scan the plate. After a brief initial interactive period, the subsequent reduction procedure is completely automatic, resulting in fully-reduced, wavelength justified spectra. We also discuss a method of removing stellar continua using a combination of non-linear filtering algorithms. The method described is used to extract over 12,000 spectra from a set of 92 objective prism plates. These spectra are used in an associated project to develop automated spectral classifiers based on neural networks.

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arXiv1998

Angular correlations between LBQS and APM: Weak Lensing by the Large Scale Structure

We detect a positive angular correlation between bright, high-redshift QSOs and foreground galaxies. The QSOs are taken from the optically selected LBQS Catalogue, while the galaxies are from the APM Survey. The correlation amplitude is about a few percent on angular scales of over a degree. It is a function of QSO redshift and apparent magnitude, in a way expected from weak lensing, and inconsistent with QSO-galaxy correlations being caused by physical associations, or uneven obscuration by Galactic dust. The correlations are ascribed to the weak lensing effect of the foreground dark matter, which is traced by the APM galaxies. The amplitude of the effect found here is compared to the analytical predictions from the literature, and to the predictions of a phenomenological model, which is based on the observed counts-in-cells distribution of APM galaxies. While the latter agree reasonably well with the analytical predictions (namely those of Dolag & Bartelmann 1997, and Sanz et al. 1997), both under-predict the observed correlation amplitude on degree angular scales. We consider the possible ways to reconcile these observations with theory, and discuss the implications these observations have on some aspects of extragalactic astronomy.

astro-ph↗

arXiv1998

Automated Classification of Stellar Spectra. II: Two-Dimensional Classification with Neural Networks and Principal Components Analysis

We investigate the application of neural networks to the automation of MK spectral classification. The data set for this project consists of a set of over 5000 optical (3800-5200 AA) spectra obtained from objective prism plates from the Michigan Spectral Survey. These spectra, along with their two-dimensional MK classifications listed in the Michigan Henry Draper Catalogue, were used to develop supervised neural network classifiers. We show that neural networks can give accurate spectral type classifications (sig_68 = 0.82 subtypes, sig_rms = 1.09 subtypes) across the full range of spectral types present in the data set (B2-M7). We show also that the networks yield correct luminosity classes for over 95% of both dwarfs and giants with a high degree of confidence. Stellar spectra generally contain a large amount of redundant information. We investigate the application of Principal Components Analysis (PCA) to the optimal compression of spectra. We show that PCA can compress the spectra by a factor of over 30 while retaining essentially all of the useful information in the data set. Furthermore, it is shown that this compression optimally removes noise and can be used to identify unusual spectra.

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arXiv1997

Physical Parameterization of Stellar Spectra: The Neural Network Approach

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 & Corbally 1994, 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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