Revisiting the Hubble Sequence : Comparative Studies with Sloan Digital Sky Survey and the Hubble Ultra Deep Field

Mathematics – Logic

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Scientific paper

Recent large surveys of galaxies have advanced morphological studies tremendously, moving this field from the subjective art of morphological classification to a quantitative science of physical morphology. The Hubble Space Telescope (HST ), and in particular, the Hubble Deep Fields, have provided a vast resource of photometric, spectroscopic, and morphological data on high redshift galaxies, and the challenge now is to link these high redshift observations with their low redshift counterparts. Systematic studies have been initiated to quantify galaxy morphology and extend the structural parameters that form the basis of the Hubble sequence to higher redshifts (Abraham et al. 1996a; Brinchmann et al. 1998;Bouwens et al. 1998; Conselice 2003). The comparative study of high and low redshift morphology, however, is not straightforward. Complications arise in interpreting observations due to redshift-dependent selection effects, biases, and incompleteness. The most fundamental difficulty has been the lack of a good, complete, digital sample of nearby galaxies. This has limited the morphological type and luminosity range in most digital surveys of local galaxies conducted so far (Frei et al. 1996) which in turn has limited our knowledge of the basic properties that characterize distant galaxies.
We are using publicly available data from the Sloan Digital Sky Survey (SDSS) and high redshift data from the HST-ACS Ultra Deep Field (UDF) as well as the Gemini Deep Deep Survey (GDDS) to conduct a quantitative comparison of low and high redshift galaxy morphologies. We have determined a quantitative morphological parameter space (M-space) which adequately represents the diverse morphologies observed in the universe. Using M-space we show that local analogs to even very peculiar high redshift galaxies do exist and attempt to constrain their number density as a function of redshift.

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