Stellar spectroscopic classification has been successfully automated by a
number of groups. Automated classification and parameterization work best when
applied to a homogeneous data set, and thus these techniques primarily have
been developed for and applied to large surveys. While most ongoing large
spectroscopic surveys target extragalactic objects, many stellar spectra have
been and will be obtained. We briefly summarize past work on automated
classification and parameterization, with emphasis on the work done in our
group. Accurate automated classification in the spectral type domain and
parameterization in the temperature domain have been relatively easy. Automated
parameterization in the metallicity domain, formally outside the MK system, has
also been effective. Due to the subtle effects on the spectrum, automated
classification in the luminosity domain has been somewhat more difficult, but
still successful. In order to extend the use of automated techniques beyond a
few surveys, we present our current efforts at building a web-based automated
stellar spectroscopic classification and parameterization machine. Our proposed
machinery would provide users with MK classifications as well as the
astrophysical parameters of effective temperature, surface gravity, mean
abundance, abundance anomalies, and microturbulence.Comment: 5 pages; to appear in The Garrison Festschrift conference proceeding

Prieto, Carlos Allende

Sneden, Chris

von Hippel, Ted

English

arXiv

Stellar spectroscopic classification has been successfully automated by a number of groups. Automated classification and parameterization work best when applied to a homogeneous data set, and thus these techniques primarily have been developed for and applied to large surveys. While most ongoing large spectroscopic surveys target extragalactic objects, many stellar spectra have been and will be obtained. We briefly summarize past work on automated classification and parameterization, with emphasis on the work done in our group. Accurate automated classification in the spectral type domain and parameterization in the temperature domain have been relatively easy. Automated parameterization in the metallicity domain, formally outside the MK system, has also been effective. Due to the subtle effects on the spectrum, automated classification in the luminosity domain has been somewhat more difficult, but still successful. In order to extend the use of automated techniques beyond a few surveys, we present our current efforts at building a web-based automated stellar spectroscopic classification and parameterization machine. Our proposed machinery would provide users with MK classifications as well as the astrophysical parameters of effective temperature, surface gravity, mean abundance, abundance anomalies, and microturbulence

Embry-Riddle Aeronautical University

Publications Summer 8-8-2002 Automated Stellar Spectral Classification and Parameterization for the Masses Ted von Hippel The University of Texas, vonhippt@erau.edu Carlos Allende Prieto The University of Texas, callende@astro.as.utexas.edu Chris Sneden The University of Texas, chris@verdi.as.utexas.edu Follow this and additional works at: https://commons.erau.edu/publication  Part of the Stars, Interstellar Medium and the Galaxy Commons Scholarly Commons Citation von Hippel, T., Prieto, C. A., & Sneden, C. (2002). Automated Stellar Spectral Classification and Parameterization for the Masses. The Garrison Festschrift, (). Retrieved from https://commons.erau.edu/publication/1171 This Article is brought to you for free and open access by Scholarly Commons. It has been accepted for inclusion in Publications by an authorized administrator of Scholarly Commons. For more information, please contact commons@erau.edu. AUTOMATED STELLAR SPECTRAL CLASSIFICATIONANDPARAM-ETERIZATION FOR THE MASSESTed von Hippel, Carlos Allende Prieto, & Chris SnedenDepartment of Astronomy, University of Texas, Austin, TX 78712ABSTRACT: Stellar spectroscopic classification has been successfully automated by a number of groups.Automated classification and parameterization work best when applied to a homogeneous data set, and thusthese techniques primarily have been developed for and applied to large surveys. While most ongoing largespectroscopic surveys target extragalactic objects, many stellar spectra have been and will be obtained. Webriefly summarize past work on automated classification and parameterization, with emphasis on the workdone in our group. Accurate automated classification in the spectral type domain and parameterization inthe temperature domain have been relatively easy. Automated parameterization in the metallicity domain,formally outside the MK system, has also been effective. Due to the subtle effects on the spectrum, automatedclassification in the luminosity domain has been somewhat more difficult, but still successful. In order toextend the use of automated techniques beyond a few surveys, we present our current efforts at buildinga web-based automated stellar spectroscopic classification and parameterization machine. Our proposedmachinery would provide users with MK classifications as well as the astrophysical parameters of effectivetemperature, surface gravity, mean abundance, abundance anomalies, and microturbulence.1. A BRIEF HISTORY OF AUTOMATED CLASSIFICATIONCurrent or planned large-scale surveys, such as the Sloan Digital Sky Survey (York etal. 2000) or the GAIA mission (scheduled for launch around 2011), have led to increasedinterest in automated spectral classifiers (e.g., Bailer-Jones 2000). There are many otherreasons to develop automated classifiers, not the least of which are the homogeneity of theresults and the repeatability of the process. Automated spectral classification of stars goesback decades. For instance, in an early attempt Jones (1966) fit a few major stellar linesand correlated these indexes with MK type (Morgan, Keenan, & Kellman 1943). Malyuto& Shvelidze (1994) later developed this technique further. Unfortunately, the line fittingtechnique suffers from the disadvantage that one has first to know the approximate stellartype before determining which lines to fit, otherwise very different features will be foundat the same wavelengths. Kurtz (1983; see also LaSala 1994) developed a minimum vectordistance technique that matched spectra to a library of standards weighting the comparisonto different spectral regions for different types of stars. The minimum vector distancetechnique has had some success – classifying stellar spectral types to within σ=2.2 spectralsubtypes – but refining this technique is cumbersome since the weighting vectors need tobe carefully established, yet they vary as a function of spectral type and luminosity class.The Garrison FestschriftRichard Gray, Christopher Corbally, &A. G. Davis Philip, eds. p. 1–-10c© 2003 L. Davis Press 1Ted von HippelDept. of AstronomyUniversity of TexasAustin, TX 78712ted@astro.as.utexas.edu2 Ted von Hippel, Carlos Allende Prieto, & Chris SnedenIn the middle of the last decade four independent groups (Gulati et al. 1994; von Hippelet al. 1994; Vieira & Ponz 1995; Weaver & Torres-Dodgen 1995) began to successfully applyartificial neural networks (ANNs) to the spectral classification problem. Neural networks aretrained to yield classifications that are identical to those previously assigned, and thus havethe advantage that the ANN builder need not become a classification expert, but rather canrely on the true experts in the field, via their many previous classifications. For example,von Hippel et al. built an ANN using the objective prism spectra and classifications of Houk(1982, and references therein), who had at that time already classified more than 105 stars.This first generation of neural networks were applied to low resolution ultraviolet (Vieira &Ponz 1995) or optical (the other three studies) spectra and achieved σ=0.6 spectral subtypesand σ=0.35 luminosity classes for A stars and returned E(B−V ) (Weaver & Torres-Dodgen1995), or σ < 2 subtypes over the broad range of O3 to M4 stars. These studies validatedthe ANN approach and indicated its tremendous potential.The second generation of ANN spectral classification studies (Weaver & Torres-Dodgen1997; Bailer-Jones, Irwin, & von Hippel 1998; Singh, Gulati, & Gupta 1998; Weaver 2000)focused on increasing sample size and moving on to two-dimensional classification. At thispoint spectral type classification became very good, with σ=0.5–0.7 subtypes. Luminosityclassification quality varied from σ ≈ 0.3 luminosity classes (Weaver & Torres-Dodgen1997) to a statistically reliable luminosity classification for dwarfs and giants, though notsub-giants (Bailer-Jones et al. 1998). Interestingly, Weaver (2000) also showed that he couldprovide two dimensional classification for both components of artificial binaries!A few ANN studies (Bailer-Jones et al. 1997; Snider et al. 2001) moved from MKstellar classification to parameterization of astrophysical parameters (Teff , log(g), [Fe/H]).Although the philosophy of classification and parameterization are different, they striveto serve the same community. Classification seeks to place any program star within theframework defined by a series of standards. Since the MK classification system is so widelyused and the connection to stellar parameters such as absolute magnitude, surface temper-ature, and mass, are in general well known, this has been a productive route for studyingindividual stars, local stellar populations, and Galactic structure. Stellar parameterizationseeks to skip the initial step of MK classification and directly determine atmospheric pa-rameters. The cost is that the results are model dependent, but in many parts of the HRdiagram model spectra look very much, though not exactly, like real stellar spectra. In ad-dition, model atmospheres can be easily constructed for subsolar metallicity, and thereforethese can be applied to spectra which would not be possible to classify on the MK sys-tem. Bailer-Jones et al. (1997) passed their objective prism spectra through ANNs trainedon stellar atmosphere models and derived a detailed mapping between MK classificationsand the Kurucz (1979, 1992) model atmosphere set they used (as implemented with theprogram SPECTRUM by Gray & Corbally 1994). They also reported that the mean metal-licity in the solar neighborhood, as represented by Houk’s (1982) objective prism spectra,is slightly subsolar, at [Fe/H] = −0.2. Snider et al. (2001) turned the problem around andtrained ANNs on real stellar spectra using atmospheric parameters previously derived fromfine abundance analysis work in the literature, now in three-dimensional parameter space.They achieved σ(Teff ) ≈ 150 K, σ(log(g)) ≈ 0.33 dex, and σ(Fe/H) ≈ 0.2 dex.2. A FEW LESSONS LEARNEDHere we offer a few comments on lessons we have learned in applying ANNs to theproblems of spectral classification and parameterization:Automated Stellar Spectral Classification and Parameterization for the Masses 3• Classification in spectral type and parameterization in Teff are easy, the ANNs havelittle trouble finding a good global solution, and the results are generally precise toless than a spectral subtype or 200 K.• Luminosity classification and log(g) parameterization are possible, but more difficult.Both require spectra with an adequate combination of resolution, wavelength coverage,and S/N. This spectral quality is just achieved with classical MK objective prismresolution and wavelength coverage.• ANNs are best suited to stellar classification/parameterization when a single wave-length range and resolution are used for a particular ANN.• If low S/N spectra are used, besides the higher random errors, ANNs may makesystematic errors unless they have been trained on low S/N spectra (Snider et al.2001).• Supervised ANNs treat spectra as patterns with a known correlation between thosepatterns and answers, and attempt to learn that relationship. The better one canhomogenize the training data so that the ANN does not find spurious correlationsbetween the input catalog and the answers the better results one achieves. Largercatalogs always help in this regard, as do uniform data sets. Spurious correlationscaused by real astrophysics can also be a problem. As an example of this phenomenon,the Houk catalog is magnitude-limited with Vlimit = 10–11. This magnitude limitcreates a correlation between spectral type and luminosity class, i.e., the catalogcontains mostly early type dwarfs and late type giants. Without proper scrutiny onemight believe a trained ANN has achieved true luminosity classification using such acatalog when the ANN could have learned to classify luminosity statistically based onspectral type.• Principle Component Analysis (PCA) can be used to compress stellar spectra or toremove spurious signals (e.g., Storrie-Lombardi et al. 1995). PCA also bears a strongresemblance to ANNs (Lahav 1995), and is a good pedagogical tool to gaining aheuristic understanding of ANNs. By creating a series of vectors, a linear combinationof which will recreate any star in the library, PCA recasts a stellar spectral library inmuch the same way as the hidden nodes in a single hidden layer ANN.• Spectral classification errors are a function of spectral type, based largely on thenumber of examples and variance within a given type or range of types. For example,Bailer-Jones et al. (1998) found σ(SpT)=0.5 for B3 to A0 stars and σ(SpT)=0.8 forF3 to G1 stars. The lower errors for the B and A stars are probably the result ofreduced sensitivity of their spectra to abundance differences.• It is easiest to build multiple ANNs, each specializing in a specific dimension, whensolving multi-dimensional spectral classification or parameterization problems. Notonly are the ANNs more likely to converge on a good global solution, but less data arerequired for this approach. For example, Snider et al. (2001) built ANNs specializingin each of Teff , log(g), and [Fe/H]. Weaver (2000) has also found it helpful to use oneANN for initial rough classifications, followed by specialist ANNs, trained on a limitedspectral type ranges, to refine the classifications.4 Ted von Hippel, Carlos Allende Prieto, & Chris Sneden3. PROPOSED CLASSIFICATION AND PARAMETERIZATION FOR THE MASSESCan we build automated spectral classifiers for general use? For some time Bob Gar-rison has pointed out that we could build stellar classifiers for particular spectrographs.Users of such spectrographs might have a near real-time reduction pipeline, immediatelyfollowing which they would receive a stellar classification. This is certainly possible. In fact,if the data were obtained in a standard manner, the only reduction steps required prior toANN classification would be spectral extraction and wavelength calibration.In practice, neither we nor, to the best of our knowledge, any other group has takenthis approach. The difficulties are not technical, but rather the time-consuming nature ofbuilding multiple such classification machines for the many possible spectrographs in useby stellar spectroscopists. Certainly, if a stellar spectroscopic survey of sufficient size wereto be undertaken which would create a uniform data set of sufficient quality, we and otherswould be motivated to build a tailor-made stellar classification or parameterization machinefor that instrument/survey.We propose instead a thematically related approach. Instead of building ANN classi-fication/parameterization machines for particular spectrographs, we propose to build suchmachines for particular combinations of wavelength coverage, resolution, and S/N, and makethese available for use via the web. It would be up to the user to process their data onto alinear flux and wavelength scale at one of the resolutions and wavelength ranges supportedby our web site algorithms. Users would upload their spectra, run the classification orparameterization ANNs, and receive a spectral type and luminosity class and/or the stellarastrophysical parameters, along with the associated uncertainties.We hope to develop such a tool first by beginning with stellar parameterization basedon model atmospheres. Our entire approach would be modular and would initially supporta single resolution and wavelength range, while covering the parameter range 4500 ≤ Teff ≤8000, 2 ≤ log(g) ≤ 5, −4.5 ≤ [Fe/H] ≤ +0.5, and 0 ≤ microturbulence ≤ 1/2 the stellarrotation break-up velocity. Our initial resolution and wavelength range have not beenfinalized, but would probably be R ≈ 2000 and 150 A˚ around Hβ, respectively. This wouldallow anyone with higher resolution spectra or spectra with a broader range of wavelengthsto take advantage of our first automated parameterization algorithms. As we move tosupport a wider range of spectral resolutions and wavelength coverages we also intend to addmodules to increase our effective temperature range to Teff = 50,000, decrease our surfacegravity range to log(g) = 1, include different relative O, Mg, and Ca abundances, and beginspectral classification on the MK system for stars of near solar metallicity. Eventually wehope to push the stellar parameters into the M, L, and T dwarf regimes. We recognize thatmodel atmospheres are never perfectly accurate and that they improve with time. Fromtime to time, where meaningful advances have been made for a particular range of stellaratmospheres, we will upgrade our parameterization modules.Our anticipated users are spectroscopists doing fine-abundance analysis who want astarting point or a sanity check on their result, those conducting surveys who need clas-sifications or parameterizations for statistical or pre-selection purposes, and those wantingindependent determinations of the classifications/parameters for their program stars for awide variety of studies.Automated Stellar Spectral Classification and Parameterization for the Masses 5ACKNOWLEDGMENTSWe wishes to thank Bob Garrison, Richard Gray, and Coryn Bailer-Jones for thought-provoking discussions. We gratefully acknowledge major financial support for this workfrom the State of Texas Advanced Research Program.REFERENCESBailer-Jones, C. A. L. 2000 A&A 357, 197Bailer-Jones, C. A. L., Irwin, M., Gilmore, G. & von Hippel, T. 1997 MNRAS 292, 157Bailer-Jones, C. A. 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