2 F. PASIAN ET AL. ology, both for ordinary spectra (Gulati et al., 1994; Vieira & Ponz, 1995) and for high-resolution objective prism data (von Hippe

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1 AUTOMATED OBJECTIVE PRISM SPECTRAL CLASSIFICATION USING NEURAL NETWORKS F. PASIAN AND R. SMAREGLIA Osservatorio Astronomico di Trieste Via G.B.Tiepolo 11, Trieste, Italy AND P. HANZIOS, A. DAPERGOLAS AND I. BELLAS-VELIDIS National Observatory of Athens - Astronomical Institute P.O.Box 20048, Athens, Greece Abstract. An automatic spectral classication system has been developed for objective prism spectra (dispersion 830 and 2440 A/mm at H ) of LMC stars, taken with the 1.2m UK Schmidt telescope in Australia. A supervised method based on articial neural networks (ANNs) has been successfully used, and a comparison with visually-classied spectra is reported. Furthermore, an unsupervised neural network approach based on the Kohonen SOM method has been tested: spectra containing peculiarities can be evidenced, and the possibility of classifying automatically objective prism spectra using minimal a-priori information is demonstrated. 1. Introduction Objective prism spectroscopy is perhaps the oldest technology available to perform wide-eld spectroscopy, and it still has its importance and validity, due to the large number of spectra that can be acquired in a single exposure. The dispersion of this kind of instrumentation is usually insucient to allow detailed spectral analysis, but it can be certainly used for classication purposes. Given the large number of spectra available, data processing techniques to automatize spectral classication are highly desirable. Recently, quite a number of dierent eorts have been devoted to solve the problem of classifying automatically spectra. In particular, the use of articial neural networks (ANNs) appears to be the most successful method-

2 2 F. PASIAN ET AL. ology, both for ordinary spectra (Gulati et al., 1994; Vieira & Ponz, 1995) and for high-resolution objective prism data (von Hippel et al., 1994). In the framework of a collaboration between the Observatories of Trieste and Athens, ANNs have been also used on lower resolution objective prism material to detect, extract and pre-classify spectra (Smareglia et al., 1994), and to carry out a preliminary classication work on a set of 140 spectra (Bellas-Velidis & Kontizas, 1995). In this paper, a more rened automatic supervised classication system having an accuracy of about 2 spectral subclasses is described, and the feasibility of using unsupervised methods to evidence spectra containing peculiarities, and to perform classication, is demonstrated. 2. Observational Material and Processing For this study, high quality lm copies of IIIa-J plates taken with the 1.2m U.K. Schmidt Telescope in Australia have been used. This material includes both low and intermediate resolution spectra, with dispersions of 2440 A/mm and 830 A/mm at H. Such dispersions yield a magnitude limit of V 18 mag and V 16.5 mag, respectively. The spectral range goes from 3400 to 5400 A, which is the IIIa-J plate cuto. The photographic material has been digitized at the OAT by means of a PDS1010A microdensitometer. The data processing has been performed using a dedicated context mostly developed under MIDAS and SNNS (Pasian et al., 1996). The system allows correction of acquisition tilt, spectra detection (using standard data processing techniques or ANNs), spectra extraction, density-to-exposure calibration, and classication by means of ANNs. The techniques related to this nal step are described in the following. 3. Articial Neural Networks - Review An ANN consists of a net of autonomous cells u 1 ; :::; u i ; :::; u j ; :::; u n, distributed on dierent layers, joined by connections having numerical weights w i;j. Networks can be feed-forward if they contain no loops or cycles, recurrent otherwise. The weights can sometimes be chosen a priori, but in general we \teach" the network to perform as desired by iterative adjustments of the w i;j values. For this phase, called learning, two basic mechanisms are available:? Supervised learning: uses a priori knowledge to create the best performance for the ANN. In other words, the learning is done by directly comparing the output of the network with known correct answers.? Unsupervised learning: no a priori knowledge is used in this case, but there is no teaching mechanism to evaluate the performance. The result

3 OBJECTIVE PRISM SPECTRAL CLASSIFICATION WITH ANNS 3 of the processing is the construction of groups of similar input patterns, in a way which is similar to the clustering mechanism known from pattern recognition. 4. Supervised Classication 4.1. RULES FOR THE SELECTION OF THE TRAINING SET For the supervised classication, the training and validation sets have been built classifying visually the spectra extracted from the objective prism images. The rules are dened as in the following. For low dispersion spectra, the overall appearance of the spectrum is taken into account, together with strong spectral features such as the Balmer lines, the CaII H and K lines, and the G band. The global accuracy that can be reached is approximately one spectral class. For intermediate dispersion spectra, the rules to classify the spectra subsequently inserted in the training set have been dened by Hantzios et al. (1994). They use:? a sample of 259 spectra evenly distributed over the spectral type sequence and representing luminosity classes Ib, II, and early III;? 39 criteria both spectroscopic and photometric: various line ratios, the Balmer discontinuity, the H-K discontinuity, and the intensities at four selected wavelengths. Such set of criteria is actually a modication of the M-K classication system suitable for the resolution available. The comparison of 27 stars in the sample (classied by other authors by means of spectra of comparable or better resolution) yields a linear correlation with a standard deviation comparable to our accuracy (2 spectral subtypes) RESULTS A set of 582 spectra of intermediate dispersion has been classied using a supervised method based on a feed-forward ANN. A training set has been built with 50 well-exposed spectra, with classes from B0 to M8 (plus carbon stars) and resolution of 2 spectral subclasses, 2 spectra per subclass, taken from a list of visually-classied spectra. To demonstrate the overall independence of the results from the specic training set chosen, the training set itself has been modied several times by interchanging elements, yielding quite comparable results, well within our accuracy. The network has 3 layers: input, output, and 1 hidden layer. The input layer has 300 units (pixels of the extracted spectrum), the output layer has 7 units (one per spectral class, ranging from -1 to +1); an output 1,0.2,-1,-1,-1,-1,-1 denes the spectral subtype A2.

4 4 F. PASIAN ET AL. Figure 1. Comparison between visual and supervised ANN classications, for intermediate-dispersion spectra in test set. Values on axes are arbitrary numerals corresponding to spectral subclasses, from B0 to M8, and carbon stars. The network has been trained by cascade correlation, using the quickpropogation algorithm. Dierent iteration numbers have been tested to achieve convergence on the training set with appropriate errors. Once trained, the ANN has been used to classify the spectra belonging to the test set, with the results shown in Figure 1, where the classied objects are represented with a diamond, multiply-classied objects are represented with a cross, and unclassied objects appear as triangles at the bottom, laying on the abscissae axis. A very good correspondance between the visual and the automatic classication, within the given accuracy (2 subclasses) can be noticed. 5. Unsupervised Classication The same test set used for the supervised classication has been analyzed using Kohonen's Self-Organizing Map (SOM) algorithm (Kohonen et al., 1992). The implementation of the SOM in our case is made on a network having 300 input neurons (the number of pixels in each pattern) and just 1 output neuron. Every pattern (spectrum) has a specic network associated to it, for which the output value is minimal with respect to the other networks. A

5 OBJECTIVE PRISM SPECTRAL CLASSIFICATION WITH ANNS 5 Figure 2. SOM \distances" from spectrum with identier 31 (spectral type M2) versus spectral type (determined with supervised method) for intermediate-dispersion spectra. Spectra with identiers 6, 32 and 191 are overlapped. triangular matrix containing the dierence in the output values for any two networks and associated patterns is computed, considering such dierences as \distances" between any pair of spectra. The results on the test set of 582 intermediate-dispersion objective prism spectra are encouraging. Using a joining mechanism for clusterization with some clustering maximal radius, the barycentres follow the path of a rough stellar classication, forming classes corresponding to late, intermediate, and early-type stars (Smareglia et al., 1994). Figure 2, where the all \distances" from a specied spectrum are plotted versus the spectral subtypes determined by the supervised method, shows that there is a natural distribution of the extracted spectra along specied directions in the feature hyperspace implicitly de- ned by the ANN. To perform SOM-based unsupervised classication, it is necessary to dene a metric in the feature hyperspace once for all, and check the \distance" of an input spectrum from the position of known spectra. The SOM method is anyhow helpful in detecting \bad" or \peculiar" data. Figure 3 shows the SOM \distances" of the low-dispersion (830 A/mm at H ) spectra in the test set from two selected ones (classes B and M). Well exposed spectra appear in the lower left part of the plot, saturated spectra are located upper left, overlapped spectra upper right: these data should be rejected, or analyzed separately to obtain meaningful results.

6 6 F. PASIAN ET AL. Figure 3. SOM \distances" from M star (identier 71) versus \distances" from B star (identier 87) for low-dispersion spectra. Saturated spectra appear on the upper left, overlapped spectra on the upper right part of the plot. References Bellas-Velidis, I., Kontizas, E. (1995) Automated classication of stellar spectra in nearby galaxies - Articial neural network application, Proc. Greek Astr. Ass. Meeting Gulati, R.K., Gupta, R., Gothoskar, P., Khobragade, S. (1994), Stellar spectral classication using automated schemes, Astroph. Journal, Vol. No. 426, pp. 340{344 Hantzios, P., Kontizas, E., Pasian, F., Dapergolas, A., Kontizas, M., Smareglia, R. (1994) An extended scheme of spectral classication for objective prism spectra, in: Astronomy from Wide-Field Imaging, A.T.McGillivray et al. eds., pp. 255{257 Kohonen, T., Kangas, J., Laaksoonen, J., Torkkola, K. (1992), Lab. of Computer and Information Science Rakentajanaukio, Technical Report Pasian, F., Smareglia, R., Kontizas, E. (1996) A system for the analysis of objective prism data, these Proceedings Smareglia, R., Pasian, F., Kontizas, M., Kontizas, E., Dapergolas, A. (1994) Using neural networks for the detection, extraction and pre-classication of spectra in objective prism images, Vistas in Astronomy Vol. no. 38, pp. 309{315 Vieira, E.F., Ponz, J.D. (1995) Automated classication of IUE low-dispersion spectra - I. Normal stars, Astron. Astrophys. Suppl. Series, Vol. no. 111, pp. 393{398 von Hippel, T., Storrie-Lombardi, L.J., Storrie-Lombardi, M.C., Irwin, M.J. (1994) Automated classication of stellar spectra - I. Initial results with articial neural networks, Mon. Not. R. Astron. Soc., Vol. no. 269, pp. 97{104 Acknowledgements { We gratefully thank M.Kontizas, E.Kontizas and G.Sedmak for invaluable suggestions and support. This research was partially funded by the Greek and Italian Ministries of Foreign Aairs. SNNS (Stuttgart Neural Network Simulator) is distributed as `Free Software' by IPVR, University of Stuttgart.

Automatic detection of objective prism stellar spectra

ASTRONOMY & ASTROPHYSICS DECEMBER I 1998, PAGE 293 SUPPLEMENT SERIES Astron. Astrophys. Suppl. Ser. 133, 293 297 (1998) Automatic detection of objective prism stellar spectra E. Bratsolis 1,2, I. Bellas-Velidis