No Arabic abstract
We present a new software tool to enable astronomers to easily compare observations of emission line ratios with those determined by photoionization and shock models, ITERA, the IDL Tool for Emission-line Ratio Analysis. This tool can plot ratios of emission lines predicted by models and allows for comparison of observed line ratios against grids of these models selected from model libraries associated with the tool. We provide details of the libraries of standard photoionization and shock models available with ITERA, and, in addition, present three example emission line ratio diagrams covering a range of wavelengths to demonstrate the capabilities of ITERA. ITERA, and associated libraries, is available from url{http://www.brentgroves.net/itera.html}
In this paper we investigate the performance of the likelihood ratio method as a tool for identifying optical and infrared counterparts to proposed radio continuum surveys with SKA precursor and pathfinder telescopes. We present a comparison of the infrared counterparts identified by the likelihood ratio in the VISTA Deep Extragalactic Observations (VIDEO) survey to radio observations with 6, 10 and 15 arcsec resolution. We cross-match a deep radio catalogue consisting of radio sources with peak flux density $>$ 60 $mu$Jy with deep near-infrared data limited to $K_{mathrm{s}}lesssim$ 22.6. Comparing the infrared counterparts from this procedure to those obtained when cross-matching a set of simulated lower resolution radio catalogues indicates that degrading the resolution from 6 arcsec to 10 and 15 arcsec decreases the completeness of the cross-matched catalogue by approximately 3 and 7 percent respectively. When matching against shallower infrared data, comparable to that achieved by the VISTA Hemisphere Survey, the fraction of radio sources with reliably identified counterparts drops from $sim$89%, at $K_{mathrm{s}}lesssim$22.6, to 47% with $K_{mathrm{s}}lesssim$20.0. Decreasing the resolution at this shallower infrared limit does not result in any further decrease in the completeness produced by the likelihood ratio matching procedure. However, we note that radio continuum surveys with the MeerKAT and eventually the SKA, will require long baselines in order to ensure that the resulting maps are not limited by instrumental confusion noise.
We present a suite of IDL routines to interactively run GALFIT whereby the various surface brightness profiles (and their associated parameters) are represented by regions, which the User is expected to place. The regions may be saved and/or loaded from the ASCII format used by ds9 or in the Hierarchical Data Format (version 5). The software has been tested to run stably on Mac OS X and Linux with IDL 7.0.4. In addition to its primary purpose of modeling galaxy images with GALFIT, this package has several ancillary uses, including a flexible image display routines, several basic photometry functions, and qualitatively assessing Source Extractor. We distribute the package freely and without any implicit or explicit warranties, guarantees, or assurance of any kind. We kindly ask users to report any bugs, errors, or suggestions to us directly (as opposed to fixing them themselves) to ensure version control and uniformity.
We describe the development of automated emission line detection software for the Fiber Multi-Object Spectrograph (FMOS), which is a near-infrared spectrograph fed by $400$ fibers from the $0.2$ deg$^2$ prime focus field of view of the Subaru Telescope. The software, FIELD (FMOS software for Image-based Emission Line Detection), is developed and tested mainly for the FastSound survey, which is targeting H$alpha$ emitting galaxies at $z sim 1.3$ to measure the redshift space distortion as a test of general relativity beyond $z sim 1$. The basic algorithm is to calculate the line signal-to-noise ratio ($S/N$) along the wavelength direction, given by a 2-D convolution of the spectral image and a detection kernel representing a typical emission line profile. A unique feature of FMOS is its use of OH airglow suppression masks, requiring the use of flat-field images to suppress noise around the mask regions. Bad pixels on the detectors and pixels affected by cosmic-rays are efficiently removed by using the information obtained from the FMOS analysis pipeline. We limit the range of acceptable line-shape parameters for the detected candidates to further improve the reliability of line detection. The final performance of line detection is tested using a subset of the FastSound data; the false detection rate of spurious objects is examined by using inverted frames obtained by exchanging object and sky frames. The false detection rate is $< 1$% at $S/N > 5$, allowing an efficient and objective emission line search for FMOS data at the line flux level of $gtrsim 1.0 times 10^{-16}$[erg/cm$^2$/s].
We have investigated the ensemble regularities of the equivalent widths (EWs) of MgII 2800 emission line of active galactic nuclei (AGNs), using a uniformly selected sample of 2092 Seyfert 1 galaxies and quasars at 0.45 <= z <= 0.8 in the spectroscopic data set of Sloan Digital Sky Survey Fourth Data Release. We find a strong correlation between the EW of MgII and the AGN Eddington ratio (L/L_Edd): EW(MgII) propto (L/L_Edd)^{-0.4}. Furthermore, for AGNs with the same L/L_Edd, their EWs of MgII show no correlation with luminosity, black hole mass or line width, and the MgII line luminosity is proportional to continuum luminosity, as expected by photoionization theory. Our result shows that MgII EW is not dependent on luminosity, but is solely governed by L/L_Edd.
We present a Bayesian approach to the redshift classification of emission-line galaxies when only a single emission line is detected spectroscopically. We consider the case of surveys for high-redshift Lyman-alpha-emitting galaxies (LAEs), which have traditionally been classified via an inferred rest-frame equivalent width (EW) greater than 20 angstrom. Our Bayesian method relies on known prior probabilities in measured emission-line luminosity functions and equivalent width distributions for the galaxy populations, and returns the probability that an object in question is an LAE given the characteristics observed. This approach will be directly relevant for the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX), which seeks to classify ~10^6 emission-line galaxies into LAEs and low-redshift [O II] emitters. For a simulated HETDEX catalog with realistic measurement noise, our Bayesian method recovers 86% of LAEs missed by the traditional EW > 20 angstrom cutoff over 2 < z < 3, outperforming the EW cut in both contamination and incompleteness. This is due to the methods ability to trade off between the two types of binary classification error by adjusting the stringency of the probability requirement for classifying an observed object as an LAE. In our simulations of HETDEX, this method reduces the uncertainty in cosmological distance measurements by 14% with respect to the EW cut, equivalent to recovering 29% more cosmological information. Rather than using binary object labels, this method enables the use of classification probabilities in large-scale structure analyses. It can be applied to narrowband emission-line surveys as well as upcoming large spectroscopic surveys including Euclid and WFIRST.