No Arabic abstract
One of the biggest problems faced by current and next-generation astronomical surveys is trying to produce large numbers of accurate cross identifications across a range of wavelength regimes with varying data quality and positional uncertainty. Until recently simple spatial nearest neighbour associations have been sufficient for most applications. However as advances in instrumentation allow more sensitive images to be made the rapid increase in the source density has meant that source confusion across multiple wavelengths is a serious problem. The field of far-IR and sub-mm astronomy has been particularly hampered by such problems. The poor angular resolution of current sub-mm and far-IR instruments is such that in a lot of cases there are multiple plausible counterparts for each source at other wavelengths. Here we present a new automated method of producing associations between sources at different wavelengths using a combination of spatial and SED information set in the Bayesian framework presented by Budavari & Szalay (2008). Testing of the technique is performed on both simulated catalogues of sources from GaLICS and real data from multi-wavelength observations of the SXDF. It is found that a single figure of merit, the Bayes factor, can be effectively used to describe the confidence in the match. Further applications of this technique to future Herschel datasets are discussed.
Context:Blazars are the rarest and most powerful active galactic nuclei, playing a crucial and growing role in today multi-frequency and multi-messenger astrophysics. Current blazar catalogs, however, are incomplete and particularly depleted at low Galactic latitudes. Aims: We aim at augmenting the current blazar census to build a catalog of blazar candidates with homogeneous sky coverage that can provide candidate counterparts to unassociated gamma-ray sources, sources of high-energy neutrino emission, and ultra-high energy cosmic rays. Methods: Starting from the ALMA Calibrator Catalog we built a catalog of 1580 blazar candidates (ALMA Blazar Candidates, ABC) for which we collect multi-wavelength information. We also compared ABC sources with existing blazar catalogs. Results: The ABC catalogue fills the lack of low Galactic latitude sources in current blazar catalogues. ABC sources are significantly dimmer than known blazars in Gaia g band, and they appear bluer in SDSS and WISE colors. The majority of ABC sources (~ 90%) have optical spectra that classify them as QSO, while the remaining sources resulted galactic objects. ABC sources are similar in X-rays to known blazar, while in gamma-rays they are on average dimmer and softer, indicating a significant contribution of FSRQ sources. Making use of WISE colours, we classified 715 ABC sources as candidate gamma-ray blazar of different classes. Conclusions: We built a new catalogue of 1580 candidate blazars with a rich multi-wavelength data-set, filling the lack of low Galactic latitude sources in current blazar catalogues. This will be particularly important to identify the source population of high energy neutrinos or ultra-high energy cosmic rays. The data collected by the upcoming LSST surveys will provide a key tool to investigate the possible blazar nature of these sources.
We present a novel population-based Bayesian inference approach to model the average and population variance of spatial distribution of a set of observables from ensemble analysis of low signal-to-noise ratio measurements. The method consists of (1) inferring the average profile using Gaussian Processes and (2) computing the covariance of the profile observables given a set of independent variables. Our model is computationally efficient and capable of inferring average profiles of a large population size from noisy measurements, without stacking and binning data nor parameterizing the shape of the mean profile. We demonstrate the performance of our method using dark matter, gas and stellar profiles extracted from hydrodynamical cosmological simulations of galaxy formation. Population Profile Estimator (PoPE) is publicly available in a GitHub repository. Our new method should be useful for measuring the spatial distribution and internal structure of a variety of astrophysical systems using large astronomical surveys.
We present a new method to classify the broad band optical-NIR spectral energy distributions (SEDs) of galaxies using three shape parameters (super-colours) based on a Principal Component Analysis of model SEDs. As well as providing a compact representation of the wide variety of SED shapes, the method allows for easy visualisation of information loss and biases caused by the incomplete sampling of the rest-frame SED as a function of redshift. We apply the method to galaxies in the UKIDSS Ultra Deep Survey with 0.9<z<1.2, and confirm our classifications by stacking rest-frame optical spectra for a fraction of objects in each class. As well as cleanly separating a tight red-sequence from star-forming galaxies, three unusual populations are identifiable by their unique colours: very dusty star-forming galaxies with high metallicity and old mean stellar age; post-starburst galaxies which have formed greater than around 10% of their mass in a recent unsustained starburst event; and metal-poor quiescent dwarf galaxies. We find that quiescent galaxies account for 45% of galaxies with log(M*/Msol)>11, declining steadily to 13% at log(M*/Msol)=10. The properties and mass-function of the post-starburst galaxies are consistent with a scenario in which gas-rich mergers contribute to the growth of the low and intermediate mass range of the red sequence.
A new approach is given for the implementation of boundary conditions used in solving the Mukhanov-Sasaki equation in the context of inflation. The familiar quantization procedure is reviewed, along with a discussion of where one might expect deviations from the standard approach to arise. The proposed method introduces a (model dependent) fitting function for the z/z and a/a terms in the Mukhanov-Sasaki equation for scalar and tensor modes, as well as imposes the boundary conditions at a finite conformal time. As an example, we employ a fitting function, and compute the spectral index, along with its running, for a specific inflationary model which possesses background equations that are analytically solvable. The observational upper bound on the tensor to scalar ratio is used to constrain the parameters of the boundary conditions in the tensor sector as well. An overview on the generalization of this method is also discussed.
Cosmological surveys in the far infrared are known to suffer from confusion. The Bayesian de-blending tool, XID+, currently provides one of the best ways to de-confuse deep Herschel SPIRE images, using a flat flux density prior. This work is to demonstrate that existing multi-wavelength data sets can be exploited to improve XID+ by providing an informed prior, resulting in more accurate and precise extracted flux densities. Photometric data for galaxies in the COSMOS field were used to constrain spectral energy distributions (SEDs) using the fitting tool CIGALE. These SEDs were used to create Gaussian prior estimates in the SPIRE bands for XID+. The multi-wavelength photometry and the extracted SPIRE flux densities were run through CIGALE again to allow us to compare the performance of the two priors. Inferred ALMA flux densities (F$^i$), at 870$mu$m and 1250$mu$m, from the best fitting SEDs from the second CIGALE run were compared with measured ALMA flux densities (F$^m$) as an independent performance validation. Similar validations were conducted with the SED modelling and fitting tool MAGPHYS and modified black body functions to test for model dependency. We demonstrate a clear improvement in agreement between the flux densities extracted with XID+ and existing data at other wavelengths when using the new informed Gaussian prior over the original uninformed prior. The residuals between F$^m$ and F$^i$ were calculated. For the Gaussian prior, these residuals, expressed as a multiple of the ALMA error ($sigma$), have a smaller standard deviation, 7.95$sigma$ for the Gaussian prior compared to 12.21$sigma$ for the flat prior, reduced mean, 1.83$sigma$ compared to 3.44$sigma$, and have reduced skew to positive values, 7.97 compared to 11.50. These results were determined to not be significantly model dependent. This results in statistically more reliable SPIRE flux densities.