How much dark matter is there inside early-type galaxies?

We study the luminous mass as a function of the dynamical mass inside the effective radius (r_e) of early-type galaxies (ETGs) to search for differences between these masses. We assume Newtonian dynamics and that any difference between these masses is due to the presence of dark matter. We use several samples of ETGs -ranging from 19 000 to 98 000 objects- from the ninth data release of the Sloan Digital Sky Survey. We perform Monte Carlo (MC) simulations of galaxy samples and compare them with real samples. The main results are: i) MC simulations show that the distribution of the dynamical vs. luminous mass depends on the mass range where the ETGs are distributed (geometric effect). This dependence is caused by selection effects and intrinsic properties of the ETGs. ii) The amount of dark matter inside r_e is approximately 7% +- 22%. iii) This amount of dark matter is lower than the minimum estimate (10%) found in the literature and four times lower than the average (30%) of literature estimates. However, if we consider the associated error, our estimate is of the order of the literature average.Comment: 24 pages, 12 figures. MNRAS accepte

Mathematical properties of weighted impact factors based on measures of prestige of the citing journals

The final publication is available at Springer via http://dx.doi.org/10.1007/s11192-015-1741-0An abstract construction for general weighted impact factors is introduced. We show that the classical weighted impact factors are particular cases of our model, but it can also be used for defining new impact measuring tools for other sources of information as repositories of datasets providing the mathematical support for a new family of altmet- rics. Our aim is to show the main mathematical properties of this class of impact measuring tools, that hold as consequences of their mathematical structure and does not depend on the definition of any given index nowadays in use. In order to show the power of our approach in a well-known setting, we apply our construction to analyze the stability of the ordering induced in a list of journals by the 2-year impact factor (IF2). We study the change of this ordering when the criterium to define it is given by the numerical value of a new weighted impact factor, in which IF2 is used for defining the weights. We prove that, if we assume that the weight associated to a citing journal increases with its IF2, then the ordering given in the list by the new weighted impact factor coincides with the order defined by the IF2. We give a quantitative bound for the errors committed. We also show two examples of weighted impact factors defined by weights associated to the prestige of the citing journal for the fields of MATHEMATICS and MEDICINE, GENERAL AND INTERNAL, checking if they satisfy the increasing behavior mentioned above.Ferrer Sapena, A.; Sánchez Pérez, EA.; González, LM.; Peset Mancebo, MF.; Aleixandre Benavent, R. (2015). Mathematical properties of weighted impact factors based on measures of prestige of the citing journals. Scientometrics. 105(3):2089-2108. https://doi.org/10.1007/s11192-015-1741-0S208921081053Ahlgren, P., & Waltman, L. (2014). The correlation between citation-based and expert-based assessments of publication channels: SNIP and SJR vs. Norwegian quality assessments. Journal of Informetrics, 8, 985–996.Aleixandre Benavent, R., Valderrama Zurián, J. C., & González Alcaide, G. (2007). Scientific journals impact factor: Limitations and alternative indicators. El Profesional de la Información, 16(1), 4–11.Altmann, K. G., & Gorman, G. E. (1998). The usefulness of impact factor in serial selection: A rank and mean analysis using ecology journals. Library Acquisitions-Practise and Theory, 22, 147–159.Arnold, D. N., & Fowler, K. K. (2011). Nefarious numbers. Notices of the American Mathematical Society, 58(3), 434–437.Beliakov, G., & James, S. (2012). Using linear programming for weights identification of generalized bonferroni means in R. In: Proceedings of MDAI 2012 modeling decisions for artificial intelligence. Lecture Notes in Computer Science, Vol. 7647, pp. 35–44.Beliakov, G., & James, S. (2011). Citation-based journal ranks: The use of fuzzy measures. Fuzzy Sets and Systems, 167, 101–119.Buela-Casal, G. (2003). Evaluating quality of articles and scientific journals. Proposal of weighted impact factor and a quality index. Psicothema, 15(1), 23–25.Dorta-Gonzalez, P., & Dorta-Gonzalez, M. I. (2013). Comparing journals from different fields of science and social science through a JCR subject categories normalized impact factor. Scientometrics, 95(2), 645–672.Dorta-Gonzalez, P., Dorta-Gonzalez, M. I., Santos-Penate, D. R., & Suarez-Vega, R. (2014). Journal topic citation potential and between-field comparisons: The topic normalized impact factor. Journal of Informetrics, 8(2), 406–418.Egghe, L., & Rousseau, R. (2002). A general frame-work for relative impact indicators. Canadian Journal of Information and Library Science, 27(1), 29–48.Gagolewski, M., & Mesiar, R. (2014). Monotone measures and universal integrals in a uniform framework for the scientific impact assessment problem. Information Sciences, 263, 166–174.Garfield, E. (2006). The history and meaning of the journal impact factor. JAMA, 295(1), 90–93.Habibzadeh, F., & Yadollahie, M. (2008). Journal weighted impact factor: A proposal. Journal of Informetrics, 2(2), 164–172.Klement, E., Mesiar, R., & Pap, E. (2010). A universal integral as common frame for Choquet and Sugeno integral. IEEE Transaction on Fuzzy System, 18, 178–187.Leydesdorff, L., & Opthof, T. (2010). Scopus’s source normalized impact per paper (SNIP) versus a journal impact factor based on fractional counting of citations. Journal of the American Society for Information Science and Technology, 61, 2365–2369.Li, Y. R., Radicchi, F., Castellano, C., & Ruiz-Castillo, J. (2013). Quantitative evaluation of alternative field normalization procedures. Journal of Informetrics, 7(3), 746–755.Moed, H. F. (2010). Measuring contextual citation impact of scientific journals. Journal of Informetrics, 4, 265–277.NISO. (2014). Alternative metrics initiative phase 1. White paper. http://www.niso.org/apps/group-public/download.php/13809/Altmetrics-project-phase1-white-paperOwlia, P., Vasei, M., Goliaei, B., & Nassiri, I. (2011). Normalized impact factor (NIF): An adjusted method for calculating the citation rate of biomedical journals. Journal of Biomedical Informatics, 44(2), 216–220.Pinski, G., & Narin, F. (1976). Citation influence for journal aggregates of scientific publications: Theory, with application to the literature of physics. Information Processing and Management, 12, 297–312.Pinto, A. C., & Andrade, J. B. (1999). Impact factor of scientific journals: What is the meaning of this parameter? Quimica Nova, 22, 448–453.Raghunathan, M. S., & Srinivas, V. (2001). Significance of impact factor with regard to mathematics journals. Current Science, 80(5), 605.Ruiz Castillo, J., & Waltman, L. (2015). Field-normalized citation impact indicators using algorithmically constructed classification systems of science. Journal of Informetrics, 9, 102–117.Saha, S., Saint, S., & Christakis, D. A. (2003). Impact factor: A valid measure of journal quality? Journal of the Medical Library Association, 91, 42–46.Torra, V., & Narukawa, Y. (2008). The h-index and the number of citations: Two fuzzy integrals. IEEE Transaction on Fuzzy System, 16, 795–797.Torres-Salinas, D., & Jimenez-Contreras, E. (2010). Introduction and comparative study of the new scientific journals citation indicators in journal citation reports and scopus. El Profesional de la Información, 19, 201–207.Waltman, L., & van Eck, N. J. (2008). Some comments on the journal weighted impact factor proposed by Habibzadeh and Yadollahie. Journal of Informetrics, 2(4), 369–372.Waltman, L., van Eck, N. J., van Leeuwen, T. N., & Visser, M. S. (2013). Some modifications to the SNIP journal impact indicator. Journal of Informetrics, 7, 272–285.Zitt, M. (2011). Behind citing-side normalization of citations: some properties of the journal impact factor. Scientometrics, 89, 329–344.Zitt, M., & Small, H. (2008). Modifying the journal impact factor by fractional citation weighting: The audience factor. Journal of the American Society for Information Science and Technology, 59, 1856–1860.Zyczkowski, K. (2010). Citation graph, weighted impact factors and performance indices. Scientometrics, 85(1), 301–315

Reviewing, indicating, and counting books for modern research evaluation systems

In this chapter, we focus on the specialists who have helped to improve the conditions for book assessments in research evaluation exercises, with empirically based data and insights supporting their greater integration. Our review highlights the research carried out by four types of expert communities, referred to as the monitors, the subject classifiers, the indexers and the indicator constructionists. Many challenges lie ahead for scholars affiliated with these communities, particularly the latter three. By acknowledging their unique, yet interrelated roles, we show where the greatest potential is for both quantitative and qualitative indicator advancements in book-inclusive evaluation systems.Comment: Forthcoming in Glanzel, W., Moed, H.F., Schmoch U., Thelwall, M. (2018). Springer Handbook of Science and Technology Indicators. Springer Some corrections made in subsection 'Publisher prestige or quality

Metric-based vs peer-reviewed evaluation of a research output: Lesson learnt from UK’s national research assessment exercise

Purpose There is a general inquisition regarding the monetary value of a research output, as a substantial amount of funding in modern academia is essentially awarded to good research presented in the form of journal articles, conferences papers, performances, compositions, exhibitions, books and book chapters etc., which, eventually leads to another question if the value varies across different disciplines. Answers to these questions will not only assist academics and researchers, but will also help higher education institutions (HEIs) make informed decisions in their administrative and research policies. Design and methodology To examine both the questions, we applied the United Kingdom’s recently concluded national research assessment exercise known as the Research Excellence Framework (REF) 2014 as a case study. All the data for this study is sourced from the openly available publications which arose from the digital repositories of REF’s results and HEFCE’s funding allocations. Findings A world leading output earns between £7504 and £14,639 per year within the REF cycle, whereas an internationally excellent output earns between £1876 and £3659, varying according to their area of research. Secondly, an investigation into the impact rating of 25315 journal articles submitted in five areas of research by UK HEIs and their awarded funding revealed a linear relationship between the percentage of quartile-one journal publications and percentage of 4* outputs in Clinical Medicine, Physics and Psychology/Psychiatry/Neuroscience UoAs, and no relationship was found in the Classics and Anthropology/Development Studies UoAs, due to the fact that most publications in the latter two disciplines are not journal articles. Practical implications The findings provide an indication of the monetary value of a research output, from the perspectives of government funding for research, and also what makes a good output, i.e. whether a relationship exists between good quality output and the source of its publication. The findings may also influence future REF submission strategies in HEIs and ascertain that the impact rating of the journals is not necessarily a reflection of the quality of research in every discipline, and this may have a significant influence on the future of scholarly communications in general. Originality According to the author’s knowledge, this is the first time an investigation has estimated the monetary value of a good research output

The ALHAMBRA survey : B−B-band luminosity function of quiescent and star-forming galaxies at 0.2≤z<10.2 \leq z < 1 by PDF analysis

Our goal is to study the evolution of the $B-$band luminosity function (LF) since $z=1$ using ALHAMBRA data. We used the photometric redshift and the $I-$band selection magnitude probability distribution functions (PDFs) of those ALHAMBRA galaxies with $I\leq24$ mag to compute the posterior LF. We statistically studied quiescent and star-forming galaxies using the template information encoded in the PDFs. The LF covariance matrix in redshift-magnitude-galaxy type space was computed, including the cosmic variance. That was estimated from the intrinsic dispersion of the LF measurements in the 48 ALHAMBRA sub-fields. The uncertainty due to the photometric redshift prior is also included in our analysis. We modelled the LF with a redshift-dependent Schechter function affected by the same selection effects than the data. The measured ALHAMBRA LF at $0.2\leq z<1$ and the evolving Schechter parameters both for quiescent and star-forming galaxies agree with previous results in the literature. The estimated redshift evolution of $M_B^* \propto Qz$ is $Q_{\rm SF}=-1.03\pm0.08$ and $Q_{\rm Q}=-0.80\pm0.08$, and of $\log \phi^* \propto Pz$ is $P_{\rm SF}=-0.01\pm0.03$ and $P_{\rm Q}=-0.41\pm0.05$. The measured faint-end slopes are $\alpha_{\rm SF}=-1.29\pm0.02$ and $\alpha_{\rm Q}=-0.53\pm0.04$. We find a significant population of faint quiescent galaxies, modelled by a second Schechter function with slope $\beta=-1.31\pm0.11$. We find a factor $2.55\pm0.14$ decrease in the luminosity density $j_B$ of star-forming galaxies, and a factor $1.25\pm0.16$ increase in the $j_B$ of quiescent ones since $z=1$, confirming the continuous build-up of the quiescent population with cosmic time. The contribution of the faint quiescent population to $j_B$ increases from 3% at $z=1$ to 6% at $z=0$. The developed methodology will be applied to future multi-filter surveys such as J-PAS.Comment: Accepted for publication in Astronomy and Astrophysics. 25 pages, 20 figures, 7 table

Extragalactic Background Light Inferred from AEGIS Galaxy SED-type Fractions

The extragalactic background light (EBL) is of fundamental importance both for understanding the entire process of galaxy evolution and for gamma-ray astronomy, but the overall spectrum of the EBL between 0.1-1000 microns has never been determined directly from galaxy spectral energy distribution (SED) observations over a wide redshift range. The evolving, overall spectrum of the EBL is derived here utilizing a novel method based on observations only. This is achieved from the observed evolution of the rest-frame K-band galaxy luminosity function up to redshift 4 (Cirasuolo et al. 2010), combined with a determination of galaxy SED-type fractions. These are based on fitting SWIRE templates to a multiwavelength sample of about 6000 galaxies in the redshift range from 0.2 to 1 from the All-wavelength Extended Groth Strip International Survey (AEGIS). The changing fractions of quiescent galaxies, star-forming galaxies, starburst galaxies and AGN galaxies in that redshift range are estimated, and two alternative extrapolations of SED-types to higher redshifts are considered. This allows calculation of the evolution of the luminosity densities from the UV to the IR, the evolving star formation rate density of the universe, the evolving contribution to the bolometric EBL from the different galaxy populations including AGN galaxies and the buildup of the EBL. Our EBL calculations are compared with those from a semi-analytic model, from another observationally-based model and observational data. The EBL uncertainties in our modeling based directly on the data are quantified, and their consequences for attenuation of very high energy gamma-rays due to pair production on the EBL are discussed. It is concluded that the EBL is well constrained from the UV to the mid-IR, but independent efforts from infrared and gamma-ray astronomy are needed in order to reduce the uncertainties in the far-IR.Comment: 25 pages, 18 figures, 4 tables; accepted for publication in MNRAS on September 3, 2010. Online material available at http://side.iaa.es/EB

Open-access mega-journals: A bibliometric profile

In this paper we present the first comprehensive bibliometric analysis of eleven open-access mega-journals (OAMJs). OAMJs are a relatively recent phenomenon, and have been characterised as having four key characteristics: large size; broad disciplinary scope; a GoldOA business model; and a peer-review policy that seeks to determine only the scientific soundness of the research rather than evaluate the novelty or significance of the work. Our investigation focuses on four key modes of analysis: journal outputs (the number of articles published and changes in output over time); OAMJ author characteristics (nationalities and institutional affiliations); subject areas (the disciplinary scope of OAMJs, and variations in sub-disciplinary output); and citation profiles (the citation distributions of each OAMJ, and the impact of citing journals). We found that while the total output of the eleven megajournals grew by 14.9% between 2014 and 2015, this growth is largely attributable to the increased output of Scientific Reports and Medicine. We also found substantial variation in the geographical distribution of authors. Several journals have a relatively high proportion of Chinese authors, and we suggest this may be linked to these journals’ high Journal Impact Factors (JIFs). The mega-journals were also found to vary in subject scope, with several journals publishing disproportionately high numbers of articles in certain sub-disciplines. Our citation analsysis offers support for Björk & Catani’s suggestion that OAMJs’s citation distributions can be similar to those of traditional journals, while noting considerable variation in citation rates across the eleven titles. We conclude that while the OAMJ term is useful as a means of grouping journals which share a set of key characteristics, there is no such thing as a “typical” mega-journal, and we suggest several areas for additional research that might help us better understand the current and future role of OAMJs in scholarly communication

The ALHAMBRA survey: evolution of galaxy clustering since z ~ 1

We study the clustering of galaxies as function of luminosity and redshift in the range 0.35 < z < 1.25 using data from the Advanced Large Homogeneous Area Medium-Band Redshift Astronomical (ALHAMBRA) survey. The ALHAMBRA data used in this work cover 2.38 deg2 in seven independent fields, after applying a detailed angular selection mask, with accurate photometric redshifts, σz ≲ 0.014(1 + z), down to IAB < 24. Given the depth of the survey, we select samples in B-band luminosity down to Lth ≃ 0.16L* at z = 0.9. We measure the real-space clustering using the projected correlation function, accounting for photometric redshifts uncertainties. We infer the galaxy bias, and study its evolution with luminosity. We study the effect of sample variance, and confirm earlier results that the Cosmic Evolution Survey (COSMOS) and European Large Area ISO Survey North 1 (ELAIS-N1) fields are dominated by the presence of large structures. For the intermediate and bright samples, Lmed ≳ 0.6L*, we obtain a strong dependence of bias on luminosity, in agreement with previous results at similar redshift. We are able to extend this study to fainter luminosities, where we obtain an almost flat relation, similar to that observed at low redshift. Regarding the evolution of bias with redshift, our results suggest that the different galaxy populations studied reside in haloes covering a range in mass between log10[Mh/( h−1 M⊙)] ≳ 11.5 for samples with Lmed ≃ 0.3L* and log10[Mh/( h−1 M⊙)] ≳ 13.0 for samples with Lmed ≃ 2L*, with typical occupation numbers in the range of ∼1–3 galaxies per halo

The Ninth Data Release of the Sloan Digital Sky Survey: First Spectroscopic Data from the SDSS-III Baryon Oscillation Spectroscopic Survey

The Sloan Digital Sky Survey III (SDSS-III) presents the first spectroscopic data from the Baryon Oscillation Spectroscopic Survey (BOSS). This ninth data release (DR9) of the SDSS project includes 535,995 new galaxy spectra (median z=0.52), 102,100 new quasar spectra (median z=2.32), and 90,897 new stellar spectra, along with the data presented in previous data releases. These spectra were obtained with the new BOSS spectrograph and were taken between 2009 December and 2011 July. In addition, the stellar parameters pipeline, which determines radial velocities, surface temperatures, surface gravities, and metallicities of stars, has been updated and refined with improvements in temperature estimates for stars with T_eff<5000 K and in metallicity estimates for stars with [Fe/H]>-0.5. DR9 includes new stellar parameters for all stars presented in DR8, including stars from SDSS-I and II, as well as those observed as part of the SDSS-III Sloan Extension for Galactic Understanding and Exploration-2 (SEGUE-2). The astrometry error introduced in the DR8 imaging catalogs has been corrected in the DR9 data products. The next data release for SDSS-III will be in Summer 2013, which will present the first data from the Apache Point Observatory Galactic Evolution Experiment (APOGEE) along with another year of data from BOSS, followed by the final SDSS-III data release in December 2014.Comment: 9 figures; 2 tables. Submitted to ApJS. DR9 is available at http://www.sdss3.org/dr