Structure of neutron stars with unified equations of state

We present a set of three unified equations of states (EoSs) based on the nuclear energy-density functional (EDF) theory.These EoSs are based on generalized Skyrme forces fitted to essentially all experimental atomic mass data and constrained to reproduce various properties of infinite nuclear matter as obtained from many-body calculations using realistic two- and three-body interactions. The structure of cold isolated neutron stars is discussed in connection with some astrophysical observations.Comment: 4 pages, to appear in the proceedings of the ERPM conference, Zielona Gora, Poland, April 201

Symmetry energy: nuclear masses and neutron stars

We describe the main features of our most recent Hartree-Fock-Bogoliubov nuclear mass models, based on 16-parameter generalized Skyrme forces. They have been fitted to the data of the 2012 Atomic Mass Evaluation, and favour a value of 30 MeV for the symmetry coefficient J, the corresponding root-mean square deviation being 0.549 MeV. We find that this conclusion is compatible with measurements of neutron-skin thickness. By constraining the underlying interactions to fit various equations of state of neutron matter calculated {\it ab initio} our models are well adapted to a realistic and unified treatment of all regions of neutron stars. We use our models to calculate the composition, the equation of state, the mass-radius relation and the maximum mass. Comparison with observations of neutron stars again favours a value of J = 30 MeV.Comment: 10 pages, 9 figures, to appear in EPJA special volume on symmetry energ

De-blending Deep Herschel Surveys: A Multi-wavelength Approach

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.Comment: 8 pages, 7 figures, 3 tables. Accepted for publication in A&

Identifying Galaxy Mergers in Observations and Simulations with Deep Learning

Mergers are an important aspect of galaxy formation and evolution. We aim to test whether deep learning techniques can be used to reproduce visual classification of observations, physical classification of simulations and highlight any differences between these two classifications. With one of the main difficulties of merger studies being the lack of a truth sample, we can use our method to test biases in visually identified merger catalogues. A convolutional neural network architecture was developed and trained in two ways: one with observations from SDSS and one with simulated galaxies from EAGLE, processed to mimic the SDSS observations. The SDSS images were also classified by the simulation trained network and the EAGLE images classified by the observation trained network. The observationally trained network achieves an accuracy of 91.5% while the simulation trained network achieves 65.2% on the visually classified SDSS and physically classified EAGLE images respectively. Classifying the SDSS images with the simulation trained network was less successful, only achieving an accuracy of 64.6%, while classifying the EAGLE images with the observation network was very poor, achieving an accuracy of only 53.0% with preferential assignment to the non-merger classification. This suggests that most of the simulated mergers do not have conspicuous merger features and visually identified merger catalogues from observations are incomplete and biased towards certain merger types. The networks trained and tested with the same data perform the best, with observations performing better than simulations, a result of the observational sample being biased towards conspicuous mergers. Classifying SDSS observations with the simulation trained network has proven to work, providing tantalizing prospects for using simulation trained networks for galaxy identification in large surveys.Comment: Submitted to A&A, revised after first referee report. 20 pages, 22 figures, 14 tables, 1 appendi

Deep Learning for Galaxy Mergers in the Galaxy Main Sequence

Starburst galaxies are often found to be the result of galaxy mergers. As a result, galaxy mergers are often believed to lie above the galaxy main sequence: the tight correlation between stellar mass and star formation rate. Here, we aim to test this claim. Deep learning techniques are applied to images from the Sloan Digital Sky Survey to provide visual-like classifications for over 340 000 objects between redshifts of 0.005 and 0.1. The aim of this classification is to split the galaxy population into merger and non-merger systems and we are currently achieving an accuracy of 91.5%. Stellar masses and star formation rates are also estimated using panchromatic data for the entire galaxy population. With these preliminary data, the mergers are placed onto the full galaxy main sequence, where we find that merging systems lie across the entire star formation rate - stellar mass plane.Comment: 4 pages, 1 figure. For Proceedings IAU Symposium No. 34

A shrinking Compact Symmetric Object: J11584+2450?

We present multi-frequency multi-epoch Very Long Baseline Array (VLBA) observations of J11584+2450. These observations clearly show this source, previously classified as a core-jet, to be a compact symmetric object (CSO). Comparisons between these new data and data taken over the last 9 years shows the edge brightened hot spots retreating towards the core (and slightly to the west) at approximately 0.3c. Whether this motion is strictly apparent or actually physical in nature is discussed, as well as possible explanations, and what implications a physical contraction of J11584+2450 would have for current CSO models.Comment: 16 pages, 6 figures, 5 tables. Accepted for publication in Ap