Semi-empirical analysis of Sloan Digital Sky Survey galaxies III. How to distinguish AGN hosts

We consider the techniques to distinguish normal star forming (NSF) galaxies and active galactic nuclei (AGN) hosts using optical spectra. The observational data base is a set of 20000 galaxies extracted from the Sloan Digital Sky Survey, for which we have determined the emission line intensities after subtracting the stellar continuum obtained from spectral synthesis. Our analysis is based on photoionization models computed using the stellar ionizing radiation predicted by Starburst 99 and, for the AGNs, a broken power-law spectrum. We explain why, among the four classical emission line diagnostic diagrams, the [OIII]/Hb vs [NII]/Ha one works best. We show however, that none of these diagrams is efficient in detecting AGNs in metal poor galaxies, should such cases exist. We propose a new divisory line between ``pure'' NSF galaxies and AGN hosts. We also show that a classification into NSF and AGN galaxies using only [NII]/Ha is feasible and useful. Finally, we propose a new classification diagram, the DEW diagram, plotting D_n(4000) vs max(EW[OII],EW[NeIII]). This diagram can be used with optical spectra for galaxies with redshifts up to z = 1.3, meaning an important progress over classifications proposed up to now. Since the DEW diagram requires only a small range in wavelength, it can also be used at even larger redshifts in suitable atmospheric windows. It also has the advantage of not requiring stellar synthesis analysis to subtract the stars and of allowing one to see ALL the galaxies in the same diagram, including passive galaxies.Comment: 14 pages, 9 figures, accepted for publication in MNRAS (replaced on august 3, 2006, eqs 6 and 7 corrected

Towards a multi-dimensional methodology supporting a safeguarding decision on the future access to mineral resources

A multi-dimensional methodology is proposed to delimit areas hosting mineral resources of public importance (MRoPI). The assessment procedure considers the Level of Geological Knowledge (LGK) along with the Economic (Ec), Environmental (Ev) and Social Development and Acceptance (SDA) dimensions. Different sets of independent, but complementary and variably weighed, criteria support the appraisal of each dimension, and a final score (MRoPI r ) results from a reasonable balance between LGK and (Ec + Ev + SDA). A ranking based on MRoPI r will fall in the [1, 10] interval, as imposed by the scaling normalising factor, but only specific tracts having MRoPI r  ≥ 4 display LGK values confident enough to be covered by a safeguarding decision at a given time. Adequate MRoPI r mapping can also be done, interpolating within the kriging formalism and evaluating thoroughly the modelling results until achieving the final map. The methodology application shows in addition that the combined use of LGK, Ec, Ev and SDA allows to address suitably two overlapping and coexisting, although different, issues: (1) safeguarding the future access to mineral resources and (2) planning the mineral development in the short-medium term, recognising the need of assigning specific areas to mining activities.info:eu-repo/semantics/publishedVersio

Quantitative risk assessment of hepatitis E virus: modelling the occurrence of viraemic pigs and the presence of the virus in organs of food safety interest

Hepatitis E virus (HEV) is a zoonotic pathogen with consumption of pork and derived products identified in different countries as a risk factor for human exposure to HEV. Great efforts have been made to understand the dynamics of virus transmission within domestic swine populations through modelling. However, from a food safety prospective, it is critical to integrate the parameters involved in the transmission dynamics with those governing the actual presence of HEV in the bloodstream, the liver, gallbladder or faeces. To date, several aspects related to the pathogenesis of the disease are still unknown or characterized by significant levels of uncertainty, making this conjunction challenging. We used published serological data obtained from pigs in a farrow-to-finish farm to implement an Immune-Susceptible-Infected-Recovered (MSIR) model reproducing the on-farm dynamics that lead to the occurrence of viraemic pigs at slaughter. Expert opinion on the length of time infectious HEV can be detected in liver, gallbladder/bile and faeces after recovery from viraemic status were used to inform a stochastic model aimed at estimating the expected proportion of viraemic pigs, pigs with infectious HEV in liver, gallbladder/bile and faeces entering the slaughterhouse. To simulate the potential effect of on-farm mitigation strategies, we estimated the changes in outcomes of interest as a function of variations in the baseline transmission parameters. The model predicted a proportion of viraemic pigs entering the slaughterhouse of 13.8% while the proportions of, and ranged from 13.8% to 94.4%, 13.8% to 94.7% and from 25.3% to 30.8% respectively, due to the uncertainty surrounding the experts’ opinions. Variations in MSIR model’s parameters alert of the need to carefully consider the application of mitigation strategies aimed at delaying the decay of maternal immunity or the peak of the within herd transmission. When the rate of decay of maternal immunity and the transmission rate were decreased between 80% and 5% and 40% and 5% from the baseline values respectively, adverse effects on were observed. The model highlights the relevance of specific aspects in the pathogenesis of the disease from a food safety prospective and it was developed to be easily reproducible and updatable as soon as accurate data becomes available. As presented, the model can be directly connected to existing or future pig-related models to estimate the significance of the identified parameters on the risk of human exposure to HEV through consumption of pork products

Predicting spectral features in galaxy spectra from broad-band photometry

We explore the prospects of predicting emission line features present in galaxy spectra given broad-band photometry alone. There is a general consent that colours, and spectral features, most notably the 4000 A break, can predict many properties of galaxies, including star formation rates and hence they could infer some of the line properties. We argue that these techniques have great prospects in helping us understand line emission in extragalactic objects and might speed up future galaxy redshift surveys if they are to target emission line objects only. We use two independent methods, Artifical Neural Neworks (based on the ANNz code) and Locally Weighted Regression (LWR), to retrieve correlations present in the colour N-dimensional space and to predict the equivalent widths present in the corresponding spectra. We also investigate how well it is possible to separate galaxies with and without lines from broad band photometry only. We find, unsurprisingly, that recombination lines can be well predicted by galaxy colours. However, among collisional lines some can and some cannot be predicted well from galaxy colours alone, without any further redshift information. We also use our techniques to estimate how much information contained in spectral diagnostic diagrams can be recovered from broad-band photometry alone. We find that it is possible to classify AGN and star formation objects relatively well using colours only. We suggest that this technique could be used to considerably improve redshift surveys such as the upcoming FMOS survey and the planned WFMOS survey.Comment: 10 pages 7 figures summitted to MNRA