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Register a new userThe systematic spectral analysis of radio surveys
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Current and future continuum surveys being undertaken by the new generation of radio telescopes are now poised to address many important science questions, ranging from the earliest galaxies, to the physics of nearby AGN, as well as potentially providing new and unexpected discoveries. However, how to efficiently analyse the large quantities of data collected by these studies in order to maximise their scientific output remains an open question. In these proceedings we present details of the surveys module for the Broadband Radio Astronomy Tools (BRATS) software package which will combine new observations with existing multi-frequency data in order to automatically analyse and select sources based on their spectrum. We show how these methods can been applied to investigate objects observed on a variety of spatial scales, and suggest a pathway for how this can be used in the wider context of surveys and large samples.
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We present a comprehensive analysis of the performance of noise-reduction (``denoising) algorithms to determine whether they provide advantages in source detection on extragalactic survey images. The methods under analysis are Perona-Malik filtering, Bilateral filter, Total Variation denoising, Structure-texture image decomposition, Non-local means, Wavelets, and Block-matching. We tested the algorithms on simulated images of extragalactic fields with resolution and depth typical of the Hubble, Spitzer, and Euclid Space Telescopes, and of ground-based instruments. After choosing their best internal parameters configuration, we assess their performance as a function of resolution, background level, and image type, also testing their ability to preserve the objects fluxes and shapes. We analyze in terms of completeness and purity the catalogs extracted after applying denoising algorithms on a simulated Euclid Wide Survey VIS image, on real H160 (HST) and K-band (HAWK-I) observations of the CANDELS GOODS-South field. Denoising algorithms often outperform the standard approach of filtering with the Point Spread Function (PSF) of the image. Applying Structure-Texture image decomposition, Perona-Malik filtering, the Total Variation method by Chambolle, and Bilateral filtering on the Euclid-VIS image, we obtain catalogs that are both more pure and complete by 0.2 magnitudes than those based on the standard approach. The same result is achieved with the Structure-Texture image decomposition algorithm applied on the H160 image. The advantage of denoising techniques with respect to PSF filtering increases at increasing depth. Moreover, these techniques better preserve the shape of the detected objects with respect to PSF smoothing. Denoising algorithms provide significant improvements in the detection of faint objects and enhance the scientific return of current and future extragalactic surveys.
We report the spectral index of diffuse radio emission between 50 and 100 MHz from data collected with two implementations of the Experiment to Detect the Global EoR Signature (EDGES) low-band system. EDGES employs a wide beam zenith-pointing dipole antenna centred on a declination of $-26.7^circ$. We measure the sky brightness temperature as a function of frequency averaged over the EDGES beam from 244 nights of data acquired between 14 September 2016 to 27 August 2017. We derive the spectral index, $beta$, as a function of local sidereal time (LST) using night-time data and a two-parameter fitting equation. We find $-2.59<beta<-2.54 pm 0.011$ between 0 and 12 h LST, ignoring ionospheric effects. When the Galactic Centre is in the sky, the spectral index flattens, reaching $beta = -2.46 pm 0.011$ at 18.2 h. The measurements are stable throughout the observations with night-to-night reproducibility of $sigma_{beta}<0.004$ except for the LST range of 7 to 12 h. We compare our measurements with predictions from various global sky models and find that the closest match is with the spectral index derived from the Guzm{a}n and Haslam sky maps, similar to the results found with the EDGES high-band instrument for 90-190 MHz. Three-parameter fitting was also evaluated with the result that the spectral index becomes more negative by $sim$0.02 and has a maximum total uncertainty of 0.016. We also find that the third parameter, the spectral index curvature, $gamma$, is constrained to $-0.11<gamma<-0.04$. Correcting for expected levels of night-time ionospheric absorption causes $beta$ to become more negative by $0.008$ - $0.016$ depending on LST.
The well-known age-metallicity-attenuation degeneracy does not permit unique and good estimates of basic parameters of stars and stellar populations. The effects of dust can be avoided using spectral line indices, but current methods have not been able to break the age-metallicity degeneracy. Here we show that using at least two new spectral line indices defined and measured on high-resolution (R= 6000) spectra of a signal-to-noise ratio (S/N) > 10 one gets unambiguous estimates of the age and metallicity of intermediate to old stellar populations. Spectroscopic data retrieved with new astronomical facilities, e.g., X-shooter, MEGARA, and MOSAIC, can be employed to infer the physical parameters of the emitting source by means of spectral line index and index--index diagram analysis.
High-resolution optical integral field units (IFUs) are rapidly expanding our knowledge of extragalactic emission nebulae in galaxies and galaxy clusters. By studying the spectra of these objects -- which include classic HII regions, supernova remnants, planetary nebulae, and cluster filaments -- we are able to constrain their kinematics (velocity and velocity dispersion). In conjunction with additional tools, such as the BPT diagram, we can further classify emission regions based on strong emission-line flux ratios. LUCI is a simple-to-use python module intended to facilitate the rapid analysis of IFU spectra. LUCI does this by integrating well-developed pre-existing python tools such as astropy and scipy with new machine learning tools for spectral analysis (Rhea et al. 2020). Furthermore, LUCI provides several easy-to-use tools to access and fit SITELLE data cubes.
The past decade has seen significant advances in cm-wave VLBI extragalactic observations due to a wide range of technical successes, including the increase in processed field-of-view and bandwidth. The future inclusion of MeerKAT into global VLBI networks would provide further enhancement, particularly the dramatic sensitivity boost to >7000 km baselines. This will not be without its limitations, however, considering incomplete MeerKAT band overlap with current VLBI arrays and the small (real-time) field-of-view afforded by the phased up MeerKAT array. We provide a brief overview of the significant contributions MeerKAT-VLBI could make, with an emphasis on the scientific output of several MeerKAT extragalactic Large Survey Projects.
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