Galaxies and the Universe - Galaxy Classification

Galaxies and the Universe - Galaxy Classification - 1/15/12...

Info iconThis preview shows page 1. Sign up to view the full content.

View Full Document Right Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: 1/15/12 Gala ie and he Uni e e - Gala Cla ifica ion Galaxy Classification Galaxies show a vast range of forms, and faced with any such situation we would like to seek any underlying patterns. This allows a compact description of individual objects, and if we are fortunate will lead to physical understanding (the prototype system of this kind is the MK stellar classification). Galaxy classification has developed with this aim, from rough description of an image through distinctions among components with different astrophysical properties. It is important to keep in mind that existing schemes are based on the appearance of a galaxy at optical wavelengths (usually in blue light), over the range of surface brightness conveniently recordable from the Earth's surface. As such, these classifications are dominated by certain components of galaxies. There are crude handwaving arguments implying that any outer faint structure might not be fundamental dynamically - based on lack of star formation there and the small number of crossing times at such distances - but we certainly need to check, and in fact very deep imaging has shown some surprising structures. The original definition of classification criteria in blue light also has implications for use on galaxies at substantial redshifts, where we typically view in the emitted ultraviolet. Recent surveys in the UV and near- IR have led to the notion of a "morphological Kcorrection", a shift in stage of the morphological classification due to changes in observed wavelength. This becomes especially important if we want to compare populations of galaxies at very different redshifts, so that different sets of galaxies are seen at different emitted wavelengths. As a specific example of how galaxies can change in appearance, here is a multiwavelength series of images of M81 = NGC 3031. Some of the same effects can be seen by comparing (observed) optical and near- infrared structures of faint galaxies, such as this example from WFPC2 and NICMOS imaging in the Hubble Deep Field. These are, left to right: a ROSAT image, from data archived at HEASARC; a color- mapped UV image from GALEX; an optical color image, from data provided by G. Bothun; a near- IR image from the 2MASS atlas [Atlas Image obtained as part of the Two Micron All Sky Survey (2MASS), a joint project of the University of Massachusetts and the Infrared Processing and Analysis Center/California Institute of Technology, funded by NASA and the NSF], and a 70- image from Spitzer. Comparing these images, starlight from the general population is important from the UV to the near- IR. Only recent star formation is important in the mid- UV, with the bulge vanishing. A more subtle shift occurs between optical and near- IR. Much of the bulge light can now be associated with a broad barlike oval distortion, and the spiral arms are now seen mostly by the light of red giants - not only are they smoother, but the ridge lines of the arms don't quite match those traced by young stars in the opti cal and UV. These young tracers tell where the density wave has driven cloud collapse, which may have a delay from the deepest potential perturbation and may not match the smoothed potential traced by the older stars. .a . a.ed /keel/gala ie /cla if .h ml 1/9 1/15/12 Gala ie and he Uni e e - Gala Cla Tho gh e ion: Wha k ind of gala cla if ica ion gala ie ia he UV, o in he nea -inf a ed? Ho abo Pl o? ifica ion o ld ha e a i en if e had f i enco n e ed ob e ing f om do n o n Rio o he f ace of The ea ie che e e e e de c i i e, da i g i e bef e ga a ie a d ga e eb ae c di i g i hed. A e a e i W f' ef 1908, h i Fig 1 f Sa dage i SSS9: A ead e e e ce f di e i h cha ge i i c i a i d he di ib i f ab bi g a e i he di e e ec g i ed; C i ed h g a h f i a e e bef e hei a e a The fi e a che e ( hich e ai i ide ead e) a ha f H bble . Thi e i ica , i a i h a d i h ba , a d i eg a . The c a ifica i c i e ia a e: A a e ha e f e i ica . B ge e g h a d a cha ac e i ic f i a (a cha ac e i ic h gh c i a ). S i a a ca e i e be defi ed a ch b d a b b e a . H bb e ae f .a e ca be c e ie a a ged i he fa "T i g F Realm of he Neb lae, a e i ed b Sa dage 1975. . a.ed /keel/gala ie /cla if .h ml d be f ch . e di i g i he ie e " diag a . The che a ic e a i ei 2/9 1/15/12 R Gala ie and he Uni e e - Gala W CCD DSS I ifica ion .H : T" " Univ erse. C H H .H (E S ' , S0) .U , C E0- E7 ' Gala ies and t he ) .G S0 ( , H (S ,S , I ) , - , Y . , .T , ( M87), E7, .S 10(1-b/a). T b/a .M E0 , . S (S) (SB) ) S(B) (" ). A H per se. NGC 253 , . a.ed /keel/gala ie /cla , .N - .a Cla if .h ml , S(B) (" " , , " , , , .F ,I 3/9 1/15/12 Gala ie and he Uni e e - Gala Cla ifica ion NGC 3314 . T (1) (2) , - , / , , ISM, .T ' !S .S S0-, , S0,S0+ S0 ( H A ) . S0 -, . de Va co le rs : (1) (2) ( S -S -S -S -S -I ( SA- SAB- SB) M , LMC ). (3) , .T ' .H , T S) T -( H -1 .F S 0, 0 , , - R S 0/ , : ( ),( ),( ),( ). , M C 5 S 9 .T I .M V Handbuch der Ph sik . (1959, 53, 275) (4) " , C R T . a.ed /keel/gala ie /cla B if .h ml (1996, F P . 17, 95) : ' C .a B B .T .C SA/SB (1996) / R B S( ). A F .1 RC1, 4/9 1/15/12 Gala ie and he Uni e e - Gala V SAB( ) (5) R ( ( Cla ifica ion , ). R) - an de n Be rgh - (R) (" S0,A,S. T ( T , : ), " " H ") S0 , F . 5- 8 ' .O , .E , ( F . a.ed /keel/gala ie /cla if .h ml ). .T , 5 - M81 2885, NGC 3312, NGC 6872 B ( I- V, ,S V .S I .a M H . luminosit class, SI . H ,S A ). P D V ( B , NGC 309, UGC 250 5/9 1/15/12 Gala ie and he Uni e e - Gala f H0=50). a de Be gh ha de c ibed a c a ifica i Morpholog and Classif icat ion (Ca b idge, 1998). Cla ifica ion i e, h gica a d a i a i e, i Gala The Yerk es ( M ga ) che e e ic he e a i e i e ce f di a d b ge ( he H bb e c a ifica i i c de a c e) - he e a e ge e a c e a ed, b i e . Thi c a ifica i e af fac E,S,B, D (f e ic b -E S e ) a d i c i a i c a 1- 7 (7 e ga ed) a ec c ic e c e di g he ea e e a e i a e he ec c ic a ea a ce f a ica ga a f i ia h gica c e (c f ed e ?). Thi a ge f a- , Ye e e i e Eg 1, Iaf3, Sfg7, Bg4. The ec c ic c a i ba ed v isual inspect ion of t he image e e h gh i i ea ec a e. The e a defi ed a d i a ed i PASP 70,364 (1958), A J 135,1 (1962), A J 142, 1364 (1965), a d AJ 76,1000. The e ed f hi e ae e f he ie : N- ga a ie h e igh i d i a ed b a e ed c e , a d cD ga a ie f e gia ( ec c ic e c a e c) ga a ie i h e e ded e e e . A ed i a de Be gh' b , he c ce a i cha ge i e e he H bb e e a ge E- S0- Sa, a d cha ge a g ea dea i hi he Sc c a , g a hica de a i g ha h ica e ie f a id f e h gica c a ifica i a ha e a high i ea a i g ha c a ifica i ' c i e ia, e e he e a e f a e e gh f he e be a ef a i g a a . C a ifica i f hi i d, ba ed igh c ce a i , ha e ecei ed e e ed i e e i he c e f high- ed hif ga a ie , a d i h he ec g i i ha he ca be ade b e e f e ed e ba ia e de i g. Voronts ov-Ve l aminov d ced a e de c i i a ed i a a d ga a- f . Thi acc da e de ea ac i e e ce. I a i a i de a d ga a ie ha d ' ea fi i e e f he bi ? H ec ia i ec ia ? A a ie dig ified i h a ace i he che e e a ? (De Va c de a e f he dic f a a i ec ii e che e ha i c a e ec ia i ie ch a f he ddba ha he H bb e che e d e a ed i . 1 f he MCG. Thi i ai i e he H bb e c a ifica i - i i igh f ce he e a, h d i e ac i - i d ced di i e e e e a i e ha e d ' ha e a . B he ca a e ' e f- g a i a i g e ). S ia c e ha bee di ided i g a d- de ig a d f cc e The E eg ee (1982 MNRAS 201, 1021; 1987, A J 314, 3) e i a e e . The a c a a ge f 1 ("cha ic, f ag e g e ic a d i a i g he ica di ") S ch di i c i H bb e A a a d i a i a de Be gh' i i c a e ), b ed a e a a e e ica e i a e f a ga i a i . e , de e di g e a i de ha di ed, e ic ha e bee g i e e i di d h ee',b he i be ecia he e e f ga i a i . i g i hed he e, i a i a a ") 12 (" ed (f e a e i he a ic e e a S e ga a ie fa ide he c ec g i ed e e ce . Ma f he e a e " ai ec ", a ie f d ced b he i e ac i e ge f ga a ie . The e f i c de i g ( i ged) ga a ie , a i g , he , a d e i h ai , a e a d b e c ei a d high a e ic ga a ie . P ib e a ed a e ha H bb e ca ed I II a d de Va c e ca I0 e . The e e a e i M82; a ga a i h ea e e a ec ,b a ic a i a c ea da h a ea a ce. The e a e a aa f d i de e e i e a d a ea ef i e ac i - i d ced b f a f ai . The e ha e bee e ae i g gi e a `` e" h ica e a a i f he H bb e e e ce. Thei a h a e, i d, i i g he i . C a ifica i begi a a de c i i e ce ; he de i ed h ica i igh i a a he diffe e hi g. .a . a.ed /keel/gala ie /cla if .h ml 6/9 1/15/12 Gala ie and he Uni e e - Gala Cla ifica ion Basic references on classification: for the Hubble system, see the Hubble Atlas, Revised Shapley Ames Catalog, Sandage- Bedke NASA atlas, and Carnegie Atlas of Galaxies. Luminosity classification of spirals was described by van den Bergh in ApJ 131, 215 (1960) and ApJ 131, 558 (1960); the NASA atlas includes many examples. de Vaucouleurs (1977, Yale conference p. 43) discusses comparison of classification with quantitative parameters. Dwarf galaxies were not included in these original schemes; see Sandage and Binggeli 1984 (AJ 89, 919). Many useful references are given in van den Bergh's review (JRAS Canada 69, 57, 1975) and in Sandage, SSS vol 9. Dwarfs can be classified based on whether a distinct nucleus is present and on whether the structure is spheroidal or more irregular. Some fall in the low- surface- brightness (LSB) category, though this also includes very large and massive systems; our understanding of LSB galaxies and how they relate to other kinds is still in its infancy. Classification on these systems is fairly robust; experienced observers generally agree on a galaxy's Hubble type to a level T = 0.7 on the scale where the range from, say, Sa to Sab is one unit (and automated systems can be trained to a similar level). So what is this system "telling" us? The Hubble type (or T) can be shown to correlate with: bulge/disk luminosity ratio relative H I content M(H I)/L(B) mass concentration stellar population nuclear properties chemical abundances in the ISM star formation history and integrated stellar spectrum We may usefully consider many galaxies as composed of some "building blocks" whose scale and importance varies from galaxy to galaxy. These include a nucleus, bulge, lens, bar, spheroidal component, disk (with arms, rings,...), and an unseen halo which is significant for the mass distribution and dynamics. The order revealed by existing classification systems tells us that these are not really independent; disk/bulge correlates overall with arm morphology, for example. These relations hold clues to both the formation and evolution of galaxies (unfortunately not easy to tell which is which). Many simulations suggest that bar and ring structure may evolve; we would like to distinguish properties which are primordial, or at least permanent once acquired, from those that are temporary. There has been very interesting recent work on automated classification of galaxy images, and on classification from minimal information (that is, from poorly resolved images as we get at high redshift). A few brave souls have tackled the automated recognition of "peculiar" galaxies, which would be a prerequisite for thinking we understand how their abundance might change with redshift and environment. Most people who have worked with deep HST images have been impressed by how peculiar faint galaxies are as a class, and it's a key question how much of this is a real difference from the local Universe and how much arises from color and surfacebrightness selection effects which favor detection not only of galaxies which are forming stars at the relevant epoch, but of those pieces of galaxies that are doing so. One approach to automated classification is to ask what set of analytic or empirical components (bulge, disk) best represent a galaxy's detected image, and what the expected errors (say in the 2 sense) are. The limitation here is that even in perfectly ordinary galaxies, the fitted forms for these components vary, and many galaxies have images that overlap with neighbors or are dotted with brilliant star- forming regions. A quite different .a . a.ed /keel/gala ie /cla if .h ml 7/9 1/15/12 Gala ie and he Uni e e - Gala Cla ifica ion approach is taken by neural- network schemes. Here, one defines a set of input values based on the galaxy image, and trains the code using a large set of galaxies classified by eyeball (usually by several sets of eyeballs for a consistency check). The code then finds the set of hidden connections needed to give these outputs, and can apply this mapping to any further data desired. This is thought to be an analog of what the human brain does in learning to recognize patterns, though working backwards, it is not particularly clear just hat the code is responding to in the image, except that it looks most like the typical image that it was taught to classify in this way. Neural net classifiers seem to be statistically about as good as human ones, which is especially impressive if one considers that people may fold in all sorts of outside knowledge as to redshifts and passbands in their estimates. A detailed comparison of human and neural classifiers was presented by Naim et al. 1995 (MNRAS 274, 1107). The drive to derive as much information as possible from the expensive images of distant galaxies has led to the use of very simple image moments to classify galaxies. The idea is much like that of the Yerkes classification - the light is more concentrated in earlier- type galaxies. One implementation, by Fukugita et al. (1995 ApJ 439, 584) used HST imagery as "ground truth", and ground- based images in mediocre seeing to classify. Knowledge of the PSF is crucial, since this enters into the mapping between true and observed concentration indices in their (concentration, mean surface brightness) diagram. Results which are valid even in a statistical sense are useful in mapping the content of galaxy clusters, or using survey data to cover large areas of the sky at low resolution. A similar scheme, but designed to augment the Hubble system, was described by Abraham et al. (1994, ApJ, 432, 75). These automated classifications are by and large outgrowths of a long- standing broader classification problem - separating star and galaxy images at the faintest limits. Conselice and colleagures have made extensive use of a simple system involving concentration, asymmetry, and smoothness (CAS) parameters. Concentration C is measured as (for example) the logarithmic ratio of the radii containing 90% and 50% of the light. Asymmetry uses the fact that an arbitrary function can be decomposed into even and odd parts, and the figure derived is the absolute value of the departures from symmetry. The S parameter is more complex and data- dependent to derive; it describes the fraction of light in a galaxy image contained in bright structures smaller than a set threshold scale. Very crudely, C maps to morphological stage, A to interaction histroy, and S to star- formation rate, but these mappings are broad enough that the most powerful use of CAS is in comparing galaxy samples against each other. Another frontier in classification problems is how we should classify galaxies in the UV or infrared. Only now are large enough samples (such as the IR galaxy atlas derived from 2MASS data by Jarrett) being observed to tackle such problems systematically, and preferably without direct reference to the optical properties. The UV issue long relied largely on UIT imagery, with some HST snapshots being done for more distant galaxies to augment the very limited data on nearby galaxies. This situation is being systematically improved with the nearly all- sky GALEX survey. It has already turned up not only the expected sensitive tracers of star formation in earlytype galaxies, but a category of spirals with very blue, low- surface- brightness outer disks which must have an interesting history of star formation. Going against the grain of this trend to simplify classification in the interest of automation, the Galaxy Zoo project has employed the contributions of hundreds of thousands of volunteers to derive increasingly sophisticated visual classifications for nearly a million galaxies in the SDSS, turning up some surprises (blue ellipticals, red spirals, different growth histories for black holes in galaxies with and without disks...) I think this has been an excellent idea! « Data and catalogs | Photometric components of galaxies .a . a.ed /keel/gala ie /cla if .h ml 8/9 1/15/12 Gala ie and he Uni e e - Gala Cla ifica ion Course Home Bill Keel's Home Page Image Usage and Copyright Info UA Astronomy k [email protected] . a.ed Ls cags 820 at hne: /09 .a . a.ed /keel/gala ie /cla if .h ml 20009 9/9 ...
View Full Document

This note was uploaded on 01/15/2012 for the course AY 620 taught by Professor Williamkeel during the Fall '09 term at Alabama.

Ask a homework question - tutors are online