Beware of fake AGNs

In the BPT diagram, the distribution of the emission-line galaxies from the Sloan Digital Sky Survey (SDSS) evokes the wings of a seagull. Traditionally, galaxies in the right wing are considered to host AGNs. Our study of the stellar populations of SDSS galaxies showed that about1/4 of galaxies thought to host LINERS are in fact "retired galaxies", i.e. galaxies that stopped forming stars and are ionized by hot post-AGB stars and white dwarfs (Stasinska et al. 2008). When including the galaxies that lack some of the lines needed to place them in the BPT diagram the fraction of retired galaxies is even larger (Cid Fernandes et al., 2009, arXiv:0912.1376)Comment: to be published in "Co-evolution of central black holes and galaxies: feeding and feed-back" Proceedings IAU Symposium No. 267, Peterson, Rachel Somerville, & Thaisa Storchi-Bergmann ed

A comprehensive classification of galaxies in the SDSS: How to tell true from fake AGN?

We use the W_Ha versus [NII]/Ha (WHAN) diagram to provide a comprehensive emission-line classification of SDSS galaxies. This classification is able to cope with the large population of weak line galaxies that do not appear in traditional diagrams due to a lack of some of the diagnostic lines. A further advantage of the WHAN diagram is to allow the differentiation between two very distinct classes that overlap in the LINER region of traditional diagnostic diagrams. These are galaxies hosting a weakly active nucleus (wAGN) and "retired galaxies" (RGs), i.e. galaxies that have stopped forming stars and are ionized by their hot evolved low-mass stars. A useful criterion to distinguish true from fake AGN (i.e. the RGs) is the ratio (\xi) of the extinction-corrected L_Ha with respect to the Ha luminosity expected from photoionization by stellar populations older than 100 Myr. This ratio follows a markedly bimodal distribution, with a \xi >> 1 population composed by systems undergoing star-formation and/or nuclear activity, and a peak at \xi ~ 1 corresponding to the prediction of the RG model. We base our classification scheme on the equivalent width of Ha, an excellent observational proxy for \xi. Based on the bimodal distribution of W_Ha, we set the division between wAGN and RGs at W_Ha = 3 A. Five classes of galaxies are identified within the WHAN diagram: (a) Pure star forming galaxies: log [NII]/Ha 3 A. (b) Strong AGN (i.e., Seyferts): log [NII]/Ha > -0.4 and W_Ha > 6 A. (c) Weak AGN: log [NII]/Ha > -0.4 and W_Ha between 3 and 6 A. (d) RGs: W_Ha < 3 A. (e) Passive galaxies (actually, line-less galaxies): W_Ha and W_[NII] < 0.5 A. A comparative analysis of star formation histories and of other properties in these different classes of galaxies corroborates our proposed differentiation between RGs and weak AGN in the LINER-like family. (Abridged)Comment: Accepted for publication in MNRA

BOND: Bayesian Oxygen and Nitrogen abundance Determinations in giant H II regions using strong and semi-strong lines

We present BOND, a Bayesian code to simultaneously derive oxygen and nitrogen abundances in giant H II regions. It compares observed emission lines to a grid of photoionization models without assuming any relation between O/H and N/O. Our grid spans a wide range in O/H, N/O and ionization parameter U, and covers different starburst ages and nebular geometries. Varying starburst ages accounts for variations in the ionizing radiation field hardness, which arise due to the ageing of H II regions or the stochastic sampling of the initial mass function. All previous approaches assume a strict relation between the ionizing field and metallicity. The other novelty is extracting information on the nebular physics from semi-strong emission lines. While strong lines ratios alone ([O III]/Hbeta, [O II]/Hbeta and [N II]/Hbeta) lead to multiple O/H solutions, the simultaneous use of [Ar III]/[Ne III] allows one to decide whether an H II region is of high or low metallicity. Adding He I/Hbeta pins down the hardness of the radiation field. We apply our method to H II regions and blue compact dwarf galaxies, and find that the resulting N/O vs O/H relation is as scattered as the one obtained from the temperature-based method. As in previous strong-line methods calibrated on photoionization models, the BOND O/H values are generally higher than temperature-based ones, which might indicate the presence of temperature fluctuations or kappa distributions in real nebulae, or a too soft ionizing radiation field in the models.Comment: MNRAS in press; 21 pages, 22 figures, 2 tables; code, data and results available at http://bond.ufsc.b

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

The design and optimization of a condition monitoring device using data reduction techniques to estimate the leakage of a load sensing axial piston pump

Hydraulic systems are commonly used as solutions to industry challenges. Their excellent power-to-weight ratio can achieve specific design criteria that other power methods may not. In many hydraulic components, precision machining is present. This is to provide hydrodynamic lubrication between contacting components. By design, component life is greatly increased due to limited physical part interaction. Subsequently, any changes to the machined surfaces can result in accelerated and even catastrophic damage. Pressure compensated load sensing (PCLS) axial piston pumps are common in heavy duty hydraulic applications and provide flow in hydraulic systems. Typically, when a pump is exposed to common environmental contamination, internal machined surfaces can become damaged in the form of scoring. Depending on the degree of damage, this can result in increased leakage across lubricating boundaries or catastrophic failure due to adhesion. Component failure can then manifest in several ways. On a pump, slight wear can result in increased case drain leakage and the operator may not notice any performance issues, however, catastrophic failure may result in immediate system changes. A current method of evaluating the condition of an axial piston pump is by measuring the case drain leakage flow. This procedure involves installing a test flowmeter between the case drain leakage line and the reservoir and recording the flow at certain pressures. This can be an involved procedure and any time a closed hydraulic circuit is disassembled, the risk of introducing contamination is high. Additionally, robust, heavily used flowmeters can be inaccurate and unreliable due to wear and calibration errors. There is an obvious need to further develop the method of evaluating the health of a load sensing axial piston pump. The research contained in this thesis provides a potential cost effective alternative to case drain flow monitoring of PCLS axial piston pumps through the analysis of dynamic pump data. A nonlinear dynamic model of a load sensing axial piston pump and circuit is developed and validated with experimental dynamic pressure and swash angle position signals. The dynamic response of the pump outlet pressure, control piston pressure, and swashplate angle of a load sensing pump is shown to change with case drain leakage, both with the model and experimentally. iii A statistical procedure, Principal Component Analysis, (PCA), is applied to a large training dataset developed by the dynamic model. PCA is a fundamental piece of the leakage prediction algorithm developed in this research. In a simulation study, the designed leakage prediction algorithm is able to predict leakage using clean training and test data with a root mean square (RMS) error of less than 1%. Further algorithm development includes determining the best dynamic measurements to obtain, the amount of training data, a filter design for the raw experimental data, and training data manipulation. A simulation study shows that the signal combination that gives the best prediction performance is a combination of the pump pressure, control piston pressure, and the swashplate angle. This was confirmed by evaluating the leakage prediction performance with experimental pump response data. Having determined the optimal sensor data, the amount of training data is investigated. This was shown to improve from 100 samples and peak at 1000 samples. An optimization using experimental data was performed to determine the best filter to apply to the experimental response data. It was determined that a low pass filter with a cutoff frequency 10% below the piston pumping frequency gave the best leakage prediction results. This research includes a thorough investigation into the manipulation of the training data. The detailed optimal noise addition parameters give a predictive error of less than 20% using a signal combination of pump pressure, control piston pressure, and swashplate angle for experimental pump response data. Using just the pump and control piston pressure transients results in approximately 40% prediction error. Swashplate response data give conflicting results as the predictive error for the minimally worn pump is much different than the high wear pump (20% for severely worn). This research is an investigation into the feasibility of a load sensing axial piston pump condition monitoring device that measures case drain leakage via dynamic measurements. A comprehensive analysis is performed to optimize a leakage predictive algorithm and the design is tested in simulation as well as with experimental data and shows good potential