Western School of Law. Thanks go to Bob Salinas and to the 1996-97 editorial boards of the California Law Review and La Raza Law Journal, who proposed and approved this first-ever symposium. Thanks also go to the 1997-98 boards of these two journals for their caring work on this project, and especially to Joseph Hahn for his tireless and crucial efforts. Special thanks go to David Oakland and Sherri Sokeland for crucial attention both to detail and to vision. Thanks additionally go to the symposium contributors, and to all LatCrit scholars, whose endeavors are helping to bring Latina/o concerns to the fore of legal and social discourse. Finally, personal thanks go to Tony
A new automated astronomical image classifier is described. The classifier is of the Bayesian type using maximumlikelihood template fitting with Poisson noise. The method's advantages are that there is no need for an explicit galaxy model, it provides a continuous spectrum between totally unresolved objects and obviously diffuse resolved galaxies, and it mn assign a probability to the classification. The continuous nature of the classifier allows identification of intermediate types such as stellar objects with faint nebulosity and galaxies with bright unresolved nuclei. The ability to as sign a probability to each classification allows a determination of when the noise, plate quality, and scale of the images no longer gives a sensible division of stars and galaxies. Also the probability allows the weighting of objects in statistical studies relying on this separation, The method is applied to the catalog of 4 -meter prime focus plates automatically reduced by the FOCAS system It is compared with the hyperserfaoe clustering classifier of Jarvis and Tyson. P(C, IN,) = P(N IC,) P(C5)/P(A),where P(NL IC5) is the probability that the measurements N; are obtained given an object of class Ci , P(Ci) is the a priori probability that the object is of dass Ci regardless of the measurements, and P(NN) is the probability of getting measurements N. For an unbiased classifier all the a priori probabilities are equal and the dass for which P(Ci INL) is maximum is also the one for which P(N; ICJ) is maximum The reason for this little manipulation is that the latter probability can be computed from the noise characteristics of Abstract A new automated astronomical image dassiner is described. The classifier is of the BayesLan type using rnaxirnumlikelihood template fitting with Poisson noise. The method's advantages are that there is no need for an explicit galaxy model, it provides a continuous spectrum between totally unresolved objects and obviously diffuse resolved galaxies, and it can assign a probability to the dassification. The continuous nature of the classifier allows identification of intermediate types such as stellar objects with faint nebulosity and galaxies with bright unresolved nuclei. The ability to assign a probability to each dassLfication allows a determination of when the noise, plate quality, and scale of the images no longer gives a sensible division of stars and galaxies. Also the probability allows the weighting of objects in statistical studies relying on this separation The method is applied to the catalog of 4-meter prime focus plates automatically reduced by the FOCAS system It is compared with the hypersurface clustering dasstfier of Jarvis and Tyson.
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