Angular correlations of galaxy distribution

We study the angular correlations of various galaxy catalogs (CfA1, SSRS1, Perseus-Pisces, APM Bright Galaxies and Zwicky). We find that the angular correlation exponent is $\gamma_a= 0.1 \pm 0.1$ rather than $\gamma_a=0.7$ as usually found by the standard correlation function $\omega(\theta)$. We identify the problem in the artificial decay of $\omega(\theta)$. Moreover we find that no characteristic angular scale is present in any of the analyzed catalogs. Finally we show that all the available data are consistent with each other and the angular distribution of galaxies is quite naturally compatible with a fractal structure with $D \approx 2$.Comment: 16 pages, latex, 3 postscript figures. Accepted for publication in Astrophysical Journal Letters. This paper is also available at http://www.phys.uniroma1.it/DOCS/PIL/pil.htm

Spectral Analysis of the Stromlo-APM Survey II. Galaxy luminosity function and clustering by spectral type

We study the luminosity function and clustering properties of subsamples of local galaxies selected from the Stromlo-APM survey by the rest-frame equivalent widths of their Halpha and Oii emission lines. The b_J luminosity function of star-forming galaxies has a significantly steeper faint-end slope than that for quiescent galaxies: the majority of sub-L* galaxies are currently undergoing significant star formation. Emission line galaxies are less strongly clustered, both amongst themselves, and with the general galaxy population, than quiescent galaxies. Thus as well as being less luminous, star-forming galaxies also inhabit lower-density regions of the Universe than quiescent galaxies.Comment: 8 pages, 7 figures, MNRAS, in pres

Luminosity and surface brightness distribution of K-band galaxies from the UKIDSS Large Area Survey

We present luminosity and surface brightness distributions of 40,111 galaxies with K-band photometry from the United Kingdom Infrared Telescope (UKIRT) Infrared Deep Sky Survey (UKIDSS) Large Area Survey (LAS), Data Release 3 and optical photometry from Data Release 5 of the Sloan Digital Sky Survey (SDSS). Various features and limitations of the new UKIDSS data are examined, such as a problem affecting Petrosian magnitudes of extended sources. Selection limits in K- and r-band magnitude, K-band surface brightness and K-band radius are included explicitly in the 1/Vmax estimate of the space density and luminosity function. The bivariate brightness distribution in K-band absolute magnitude and surface brightness is presented and found to display a clear luminosity--surface brightness correlation that flattens at high luminosity and broadens at low luminosity, consistent with similar analyses at optical wavelengths. Best fitting Schechter function parameters for the K-band luminosity function are found to be M*-5 log h=-23.19 +/- 0.04, alpha=-0.81 +/- 0.04 and phi*=(0.0166 +/- 0.0008)h^3 Mpc^{-3}, although the Schechter function provides a poor fit to the data at high and low luminosity, while the luminosity density in the K band is found to be j = (6.305 +/- 0.067) x 10^8 L_sun h Mpc^{-3}. However, we caution that there are various known sources of incompleteness and uncertainty in our results. Using mass-to-light ratios determined from the optical colours we estimate the stellar mass function, finding good agreement with previous results. Possible improvements are discussed that could be implemented when extending this analysis to the full LAS.Comment: 17 pages, 24 figures, matches MNRAS accepted versio

Using data mining for assessing students interaction with social media in higher education: the case of using learning analytics within the curriculum

The role of social media in higher education has shifted from providing a web 2.0 solution for supporting communication in computer supported learning to more advanced functionalities including virtual learning environment tools (e.g. content sharing, threaded discussions). This paper discusses how analysis of social media usage can equip tutors with visual probes to identify areas that may need attention. The paper also describes how data mining can be used to assess communication patterns in computer supported collaborative learning (e.g. issues associated with content, learning activities or student competencies). The data collected from Facebook, Twitter and LinkedIn have been analysed using various statistical techniques to identify group cohesion, communication pattern, student interactions with and use of the different types of social media. Current work presented in the paper includes the statistical analysis of data communication between small student groups or student pairs from a cohort of more than 200 final year students studying information systems, over a two-year period. The scope of the analysis was to assess how different learning tasks affected individual and group contributions as well as the impact of specific learning activities on tasks such as commenting, sharing, linking and liking. The investigation also considered how keywords were used, indicating how social media interaction was affected by the subjects covered during specific learning weeks. Building on previous work by the authors, this paper tests metrics identified previously for use in a learning analytics dashboard. The data includes attributes, which identify students’ usage of social media such as total tweets, Facebook posts and LinkedIn projects, submitted by students over a period of 24 learning weeks over 7-8 months, during the two academic years 2014-15 and 2015-16. Data mining techniques are used to investigate whether metric, identified before, are useful in predicting student’s results in terms of engagement, involvement, participation, contribution and communication. These are some of the factors that may affect the learning experience when integrating Web 2.0 technologies with traditional virtual learning environments. Current work also discussed the design of learning dashboards, to identify student’s results in real time (i.e. identifying those students who are likely to fail or need additional support), as ways to implement learning analytics in the curriculum

Spectral Analysis of the Stromlo-APM Survey I. Spectral Properties of Galaxies

We analyze spectral properties of 1671 galaxies from the Stromlo-APM survey, selected to have 15 < b_J < 17.15 and having a mean redshift z = 0.05. This is a representative local sample of field galaxies, so the global properties of the galaxy population provide a comparative point for analysis of more distant surveys. We measure Halpha, Oii 3727, Sii 6716, 6731, Nii 6583 and Oi 6300 equivalent widths and the D_4000 break index. The 5A resolution spectra use an 8 arcsec slit, which typically covers 40-50% of the galaxy area. We find no evidence for systematic trends depending on the fraction of galaxy covered by the slit, and further analysis suggests that our spectra are representative of integrated galaxy spectra. We classify spectra according to their Halpha emission, which is closely related to massive star formation. Overall we find 61% of galaxies are Halpha emitters with rest-frame equivalent widths EW(Halpha) >= 2A. The emission-line galaxy (ELG) fraction is smaller than seen in the CFRS at z = 0.2 and is consistent with a rapid evolution of Halpha luminosity density. The ELG fraction, and EW(Halpha), increase at fainter absolute magnitudes, smaller projected area and smaller D_4000. In the local Universe, faint, small galaxies are dominated by star formation activity, while bright, large galaxies are more quiescent. This picture of the local Universe is quite different from the distant one, where bright galaxies appear to show rapidly-increasing activity back in time. (Abridged)Comment: 40 pages, 25 figures, MNRAS, in pres

Bivariate galaxy luminosity functions in the Sloan Digital Sky Survey

Bivariate luminosity functions (LFs) are computed for galaxies in the New York Value-Added Galaxy Catalogue, based on the Sloan Digital Sky Survey Data Release 4. The galaxy properties investigated are the morphological type, inverse concentration index, Sérsic index, absolute effective surface brightness (SB), reference frame colours, absolute radius, eClass spectral type, stellar mass and galaxy environment. The morphological sample is flux limited to galaxies with r < 15.9 and consists of 37 047 classifications to an rms accuracy of ± half a class in the sequence E, S0, Sa, Sb, Sc, Sd, Im. These were assigned by an artificial neural network, based on a training set of 645 eyeball classifications. The other samples use r < 17.77 with a median redshift of z∼ 0.08, and a limiting redshift of z < 0.15 to minimize the effects of evolution. Other cuts, for example in axis ratio, are made to minimize biases. A wealth of detail is seen, with clear variations between the LFs according to absolute magnitude and the second parameter. They are consistent with an early-type, bright, concentrated, red population and a late-type, faint, less concentrated, blue, star-forming population. This bimodality suggests two major underlying physical processes, which in agreement with previous authors we hypothesize to be merger and accretion, associated with the properties of bulges and discs, respectively. The bivariate luminosity–SB distribution is fit with the Chołoniewski function (a Schechter function in absolute magnitude and Gaussian in SB). The fit is found to be poor, as might be expected if there are two underlying processes

The local space density of dwarf galaxies

We estimate the luminosity function of field galaxies over a range of ten magnitudes (-22 < M_{B_J} < -12 for H_0 = 100 km/s/Mpc) by counting the number of faint APM galaxies around Stromlo-APM redshift survey galaxies at known distance. The faint end of the luminosity function rises steeply at M_{B_J} \approx -15, implying that the space density of dwarf galaxies is at least two times larger than predicted by a Schechter function with flat faint-end slope. Such a high abundance of dwarf galaxies at low redshift can help explain the observed number counts and redshift distributions of faint galaxies without invoking exotic models for galaxy evolution.Comment: 20 pages, 5 included postscript figures, uses AAS LaTex macros. Accepted for publication in the Astrophysical Journal. Two figures and associated discussion added; results and conclusions unchange

Galaxy types in the Sloan Digital Sky Survey using supervised artificial neural networks

Supervised artificial neural networks are used to predict useful properties of galaxies in the Sloan Digital Sky Survey, in this instance morphological classifications, spectral types and redshifts. By giving the trained networks unseen data, it is found that correlations between predicted and actual properties are around 0.9 with rms errors of order ten per cent. Thus, given a representative training set, these properties may be reliably estimated for galaxies in the survey for which there are no spectra and without human intervention