S.Co.P.E. is one of the four projects funded by the Italian Government in
order to provide Southern Italy with a distributed computing infrastructure for
fundamental science. Beside being aimed at building the infrastructure,
S.Co.P.E. is also actively pursuing research in several areas among which
astrophysics and observational cosmology. We shortly summarize the most
significant results obtained in the first two years of the project and related
to the development of middleware and Data Mining tools for the Virtual
Observatory

Brescia, M.

Cavuoti, S.

D'Abrusco, R.

D'Angelo, G.

Deniskina, N.

Donalek, C.

Laurino, O.

Longo, G.

English

arXiv

S.Co.P.E. is one of the four projects funded by the Italian Government in order to provide Southern Italy with a distributed computing infrastructure for fundamental science. Besided being aimed at building the infrastructure, S.Co.P.E. is also actively pursuing research in several areas among which astrophysics and observational cosmology. We shortly summarize the most significant results obtained in the first two years of the project and related to the development of middleware and Data Mining tools for the Virtual Observatory

d'Angelo, G.

Archivio della ricerca - Università degli studi di Napoli Federico II

Mem. S.A.It. Suppl. Vol. 13, 56c© SAIt 2009 Memorie dellaSupplementiAstrophysics in S.Co.P.E.M. Brescia1, S. Cavuoti2, G. d’Angelo2, R. D’Abrusco2, C. Donalek3, N. Deniskina2,O. Laurino2, and G. Longo2,1,41 INAF - Osservatorio Astronomico Capodimonte, via Moiariello 16, 80131, Napoli, Italy2 Department of Physical Sciences - University Federico II, Naples, Italye-mail: longo@na.infn.it3 Department of Astronomy, California Institute of Technology, Pasadena, CA, USA4 INFN - Napoli Unit, via Cintia 9, 80126, Napoli, ItalyAbstract. S.Co.P.E. is one of the four projects funded by the Italian Government in order toprovide Southern Italy with a distributed computing infrastructure for fundamental science.Besided being aimed at building the infrastructure, S.Co.P.E. is also actively pursuing re-search in several areas among which astrophysics and observational cosmology. We shortlysummarize the most significant results obtained in the first two years of the project and re-lated to the development of middleware and Data Mining tools for the Virtual Observatory.Key words. distributed computing, cosmology1. IntroductionS.Co.P.E. is a general purpose GRID infras-tructure of the University Federico II in Naplesfunded through the Italian National Plan(PON) by the Italian Government to supportboth fundamental research and small/mediumsize companies. The infrastructure has beenconceived as a metropolitan GRID, embeddingdifferent (and in some cases pre-existing) andheterogeneous computing centers each with itsspecific vocation: high energy physics, astro-physics, bioinformatics, chemistry and mate-rial sciences, electric engineering, social sci-ences. Its intrinsically multi-disciplinary na-ture renders the S.Co.P.E. an ideal test bed forinnovative middleware solutions and for inter-operable tools and applications finely tuned onthe needs of a distributed computing environ-Send offprint requests to: G. Longoment. In what follows we shall shortly out-line the main activities in the fields of astro-physics and observational cosmology and, inparticular, we shall focus on: i) the ongoing ef-forts aimed at integrating the S.Co.P.E. GRID(hereafter SG) with the international VirtualObservatory (Sect.2), and ii) the implementa-tion in the SG of the data mining (DM) VO-Neural package (Sect.3) which is developedin the framework of a collaboration with theDept. of Astronomy at Caltech. In Sect. 4 weshortly outline a template scientific applicationand, finally, in Sect. 5, we outline some futuredevelopments.2. The VOb and the GRIDThe Virtual Observatory (VOb) is an in-ternational effort coordinated through theInternational Virtual Observatory AllianceM. Brescia & et al.: Astrophysics in S.Co.P.E. 57IVOA; URL.1 (2000) aimed at: i) federat-ing and making interoperable all astronomicaldata archives produced by both ground basedand space borne instruments; ii) deploying anew generation of science applications or toolswhich use VOb protocols for exploratory dataanalysis and for the extraction of knowledgefrom massive data sets. The VOb is inherentlydistributed: data collections remain with theirproviders and are accessed through standardinterfaces. The access to the data takes placethrough a registry which contains informationabout data sets, archives, catalogs, surveys, andcomputational services that can be accessedthrough VOb interfaces Hanish & De Young(2008). While the federation and fusion of het-erogeneous data archives and the implementa-tion of flexible data reduction and data analysistools have been widely addressed and, at leastin their fundamental aspects, solved, the pos-sibility to access large distributed computingfacilities to perform computing intensive taskshas not yet been satisfactorily answered.One problem to be solved is the conflict ex-isting between the VOb and the GRID secu-rity procedures: most users of a specific VirtualOrganization (VO) do not possess the personalcertificates which are requested to access theGRID or, even when they do have a personalcertificate, the computing GRID which theyneed does not recognize their own certificationauthority.In the framework of the VONeural project(Sect. 3) and in order to make our Data Mining(DM) tools accessible to the wider commu-nity, we implemented and tested a general pur-pose interface between the UK-ASTROGRIDhereafter AG; URL.4 (2000) and the SG.2.1. GRID-Launcher v.1.0The UK based ASTROGRID is one of the mostrobust astronomical Virtual Organizations sofar implemented and represents a good groundfor testing innovative solutions. The mainproblem we had to face was the fact that mostusers which are recognized by the AG UserAuthentication Service do not possess a per-sonal GRID certificate and cannot thereforeaccess distributed computing resources. ThisFig. 1. Grid Launcher v.1.0 workflows for inputand output. UI: user interface; RB: resource bro-ker; SE: storage element; CE: computing element;WN: working node. Upper panel: imput flow; lowerpanel: output flow.problem can be at least in part circumvented byoffering the applications as web services to beconsumed on the Grid via a service certificate(or ”robot” certificates). At the time GRID-Launcher v.1.0 was developed, this option hadnot yet been formally accepted by the EGEE-2boards and we were obliged to implement a testversion which makes use of a personal GRIDcertificate (signed by the INFN-GRID CA)which is recognized by the S.Co.P.E. GRID.In a very schematic way, GRID-Launcherworks as it is summarized in Fig. 1:– It takes the user input from the UserInterface of the ASTROGRID Desktop,collects all files, tabs and programs neededand generates automatically three scripts:58 M. Brescia & et al.: Astrophysics in S.Co.P.E.task.sh, task. jdl and wn runner.sh to beexecuted on the GRID;– it wraps them in an archive and sends itto the GRID UI (authentication takes placewith public keys exchange);– the UI unpacks them, copies the datato the Storage Element (SE), copieswn runner.sh to the WN’s, starts task.shand task. jdl;– wn runner.sh starts on the WNs, takes thedata from SE, starts the application andputs the results on the SE. The GRIDgenerates automatically two output filestask.err and task.out and sends them to theUI using the Output SandBox.– GRID-launcher periodically checks thestatus of job and when it ends, it movesthe results from the UI to the ASTROGRIDmachine. GRID-launcher receives the dataarchive, unpacks them and puts the resultsinto the AG Myspace (VO-Space).So far, GRID-Launcher v.1.0 has been im-plemented and tested on an handful of appli-cations: VO-Neural MLP & VO-Neural SVM(cf. Sect. 3), Sextractor Bertin & Arnouts(1996) & SWarp URL.8 (2000).3. VO-NeuralAs it was mentioned above, in the last decademany national cf. URL.2 (2000) and interna-tional cf. URL.3 (2000) projects have solvedmany problems related to the federation of het-erogeneous data sets while much remains stillto be done for what tools and user interfacesare concerned. One of the main issues to besolved is the implementation of scalable anduser friendly data mining tools capable to dealwith the huge VOb data sets.VO-Neural is a data mining frameworkcapable to work on massive (> 1 TB) datasets (catalogues) in a distributed comput-ing environment matching the IVOA stan-dards and requirements. VO-Neural is theevolution of the AstroNeural Tagliaferri et al.(2003) project which was started in 1994,as a collaboration between the Departmentof Mathematics and Applications at theUniversity of Salerno and the AstronomicalObservatory of Capodimonte-INAF, and is cur-rently under continuous evolution. VO-Neuralallows to extract from large datasets informa-tion useful to determine patterns, relationships,similarities and regularities in the space of pa-rameters, and to identify outlayers. In its fi-nal version, it will be accessible both as aweb application and through the AG Desktopand will offer main elaborative features likeexploratory data analysis, data prediction andancillary functionality like fine tuning, visualexploration of the main characteristics of thedatasets, etc.. Besides offering the possibilityto use the individual routines to perform spe-cific tasks, VO-Neural will provide the userwith a complete framework to write his owncustomized programs. In the next two para-graphs we shortly outline the main features oftwo supervised clustering models already in-cluded in the package which have already beenused on the GRID-S.Co.P.E. for specific sci-ence applications.3.0.1. VONeural MLPVONeural MLP is an implementation of astandard Multi Layer Perceptron based onthe FANN (Fast Artificial Neural Networks)Library URL.6 (2000), written in C++Skordovski (2008). The algorithm known asMulti Layer Perceptron (MLP) is based on theconcept of perceptron and the method of learn-ing is based on gradient-descent method thatallows to find a local minimum of a function ina space with N dimensions. The weights asso-ciated to the connections between the layers ofneurons are initialized at small and random val-ues, and then the MLP applies the learning ruleusing part of the template patterns. Once con-vergence has been achieved and a validationprocedure has been applied in order to avoidoverfitting, the performances of the networkare evaluated on a disjoint test set extractedfrom the template patterns. The resulting net-work is then applied to the original data.M. Brescia & et al.: Astrophysics in S.Co.P.E. 593.0.2. VONeural S V MVONeural S V M is an implementation ofthe Support Vector Machines Russo (2007);Cavuoti (2008) based on the LIBSVM libraryURL.7 (2000). Support Vector Machines per-form classification of records into classes byfirst mapping the data into an higher dimen-sionality and then using a set of templatevectors (targets) to find in this new space aseparation hyperplane with the largest mar-gin. Withouth entering into details Boser et al.(1992), we shall just remember that, in thecase of the C-SVC implemented with the RBF(Radial Basis Functions) kernel, the positionof this hyperplane depends on two parameters(C and γ) which cannot be estimated in ad-vance but need to be evaluated by finding themaximum in a grid of values which is usu-ally defined by letting C and γ vary as C =2−5, 2−3, ..., 215 and γ = 2−15, 2−13, ..., 23. Dueto their computational weight, and to the needto run many iterations for different pairs of thetwo parameters, SVM are ideally suited for be-ing used on the GRID.4. An application to the classificationof AGN in the SDSSThe astronomical community is used to per-form DM tasks in a sort of ”hidden” way(cf. the case of specific objects selection ina color-color diagram) but it has not yet be-come familiar with the potentialities of moreadvanced tools such as those described here.This is mainly due to the fact that these toolsare often everything but user friendly and re-quire an in depth understanding of the (of-ten complex) theory laying behind them; acomplexity which often discourages poten-tial users. Therefore, a crucial aspect of theproject is the application to challenging prob-lems capable to exemplify the new sciencewhich will emerge from the adoption of aless conservative approach to the analysis ofthe data. Two science cases, namely the eval-uation of photometric redshifts (a regressionand classification problem based on the use ofVONeural MLP) and the selection of candi-date quasars in the Sloan Digital Sky Surveycf. Stoughton et al. (2002) (based on the useof unsupervised clustering algorithms and ag-glomerative clustering) have already been pub-lished in the literature D’Abrusco et al. (2007,2008). We shall therefore focus on the applica-tion of VONeural S V M to the classification ofAGNs.The classification of AGN is usually per-formed on their overall spectral distributionusing some spectroscopic indicators (equiva-lent linewidths, FWHM of specific lines orlines flux ratios) and diagnostic diagrams (usu-ally called BPT) which are difficult and timeconsuming to derive. In this diagrams AGNand not-AGN are empirically separated bysome lines derived either from the theory orfrom empirical laws such as those derived byKewley et al. (2006); Kauffman et al. (2003);Heckman (1980). A reliable and accurateAGN classificator based on photometric fea-tures only, would allow to save precious tele-scope time and enable several studies basedon statistically significant samples of objects.We therefore used a supervised clustering ofthe photometric data exploiting the informa-tion contained in a spectroscopic Base ofKnowledge (BoK) derived from available cata-logues. We wish to stress that since neural net-works have no power of extrapolation all thebiases in the BoK are reproduced and thereforethe BoK needs to be as complete and bias-freeas possible. As classification tools, we usedboth the MLP and, due to the intrinsically bi-nary nature of the problem (AGN against non-AGN, Seyfert 1 against Seyfert 2, etc) also theSVM.The BoK was obtained from the fusion oftwo catalogues.– Sorrentino et al (2006) separated objectsinto Seyfert 1, Seyfert 2 and ”Not AGN”using the Kewley’s lines Kewley et al.(2006);– a catalogue derived by us from the SDSSspectroscopic archive using the criteriaintroduced by Kauffman et al. (2003) inwhich objects are classified as AGN,not AGN, and ”mixed”. The Mix andPure AGN zone were further divided into60 M. Brescia & et al.: Astrophysics in S.Co.P.E.Table 1. Summary of the results of supervised classification experiments performed using bothVONeural MLP and VONeural S V M.experiment BoK algorithm efficiency completenessAGN vs Mix BPT plot + Kewley line MLP 76% 54%BPT plot + Kewley line SVM 74% 55%Type 1 vs 2 BPT plot + Kewley line MLP 95% ∼ 100%BPT plot + Kewley line SVM 82% 98%Seyfert vs LINER BPT plot + Hecman & Kewley lines MLP 80% 92%BPT plot + Kewley line SVM 78% 89%Seyfert and LINERs by using the HeckmanlineHeckman (1980).We made three experiments using both theMLP and SVM, and for all of them we usedthe same set of features (for a definition referto the SDSS specifications) extracted from theSDSS database: petroR50 u, petroR50 g,petroR50 r, petroR50 i, petroR50 z,concentration index r, f ibermag r,(u − g)dered, (g − r)dered, (r − i)dered,(i − z)dered, dered r, together with the photo-metric redshift in D’Abrusco et al. (2008). Weperformed three types of classification experi-ments: AGN vs Mix, Type1 vs Type2, Seyfertvs LINER. The experiments with SVM wereperformed on the SG using 110 worker nodes.In order to test the interoperability of thefour PON projects, the 110 nodes were takenfrom the Napoli, Catania and Cagliari PONlocations. Results are summarized in Table4 and, as it can be seen, the use of machinelearning tools allows to reach performanceswhich in some cases (e.g. Type 1 vs 2 withMLP’s) cannot by any means be achievedwith more traditional tools. A more detaileddiscussion of the results will be presentedin (Cavuoti, d’Abrusco & Longo, 2008, inpreparation).5. Future developmentsWe plan to continue the development of VO-Neural and to offer it as a web application.More in detail, we plan to deploy a generalpurpose GRID-Launcher interface capable tolaunch any ”command line” program througha ”robot certificate” (GRID-Launcher 2.0).At the moment we are engineering thepackage in order to increase its flexibility andcapability to adapt to a distributed computingenvironment. We are also implementing par-allel versions of some tools which are par-ticularly demanding in terms of computingtime. We also plan to integrate, within theVO-Neural interface existing statistical soft-ware (such as, for instance, the VO-STAT webapplication URL.9 (2000)), in order to en-sure proper statistical tools for exploratory dataanalysis and for the evaluation of the results.The status of the project can be monitored atthe URL: http://voneural.na.infn.it/ .Acknowledgements. The authors wish to thank M.Paolillo and E. de Filippis for many useful discus-sions. The work was funded through the Euro VO-Tech project and the MUR funded PON-S.Co.P.E.ReferencesBertin E. & Arnouts S. 1996, A& AS, 117,393.Boser B. E., Guyon I. & Vapnik V. 1992,inProc. of the Fifth Annual Workshop onComputational Learning Theory, 144, ACMPress.Cavuoti S. 2008, M.Sc. Thesis, University ofNapoli Federico II.D’Abrusco R., Staiano A., Longo G., BresciaM., De Filippis E., Paolillo M., TagliaferriR. 2007, ApJ, 663, 752.D’Abrusco R., Longo G. & Walton N.A. 2008,astro-ph/0805.0156v1.Hanish R. & de Young D. 2008, in TheNational Virtual Observatory Book ASPM. Brescia & et al.: Astrophysics in S.Co.P.E. 61Conference Series, Vol. 382, M. J. GrahamM. J. Fitzpatrick, & T. A. McGlynn eds., 1.Heckman T.M. 1980, A& A, 87, 182.Kauffman G. et al. 2003, MNRAS, 346, 1055.Kewley L.J. et al. 2006, MNRAS, 372, 961.Skordovski B. 2008, M.Sc. Thesis, Universityof Napoli Federico II.Russo V. 2007, M.Sc. Thesis, University ofNapoli Federico II.Stoughton C., Lupton R. H., Bernardi M. et al.2002, AJ, 123, 485.Tagliaferri R., Longo G., Milano L., AcerneseF., et al. 2003, Neural Networks, 16, 297.Sorrentino G., Radovich M. & Rifatto A. 2006,A & A 451, 809.URL.1: IVOA: http://www.ivoa.org/URL.2: NVO: http://www.nvo.org/URL.3: Euro-VO: http://www.eurovo.org/URL.4: http://www.astrogrid.uk/URL.5: http://voneural.na.infn.it/URL.6: http://leenissen.dk/fann/URL.7: http://www.csie.ntu.edu.tw/ cjlin/libsvm/URL.8: SWarp User manual , E. Bertin at:http://terapix.iap.fr/rubrique.php?id rubrique=49URL.9: VO-STAT, http://astrostatistics.psu.edu/

Astrophysics in S.Co.P.E.

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Astrophysics in S.Co.P.E

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