The evolving relation between star-formation rate and stellar mass in the VIDEO Survey since z=3z=3

We investigate the star-formation rate (SFR) and stellar mass ($M_*$) relation of a star-forming (SF) galaxy sample in the XMM-LSS field to $z\sim 3.0$ using the near-infrared data from the VISTA Deep Extragalactic Observations (VIDEO) survey. Combining VIDEO with broad-band photometry, we use the SED fitting algorithm CIGALE to derive SFRs and $M_*$ and have adapted it to account for the full photometric redshift PDF uncertainty. Applying a SF selection using the D4000 index, we find evidence for strong evolution in the normalisation of the SFR-$M_*$ relation out to $z\sim 3$ and a roughly constant slope of (SFR $\propto M_*^{\alpha}$) $\alpha=0.69\pm0.02$ to $z\sim 1.7$. We find this increases close to unity toward $z\sim2.65$. Alternatively, if we apply a colour selection, we find a distinct turnover in the SFR-$M_*$ relation between $0.7\lesssim z\lesssim2.0$ at the high mass end, and suggest that this is due to an increased contamination from passive galaxies. We find evolution of the specific SFR $\propto(1+z)^{2.60}$ at $\log(M_*)\sim$10.5, out to $z\lesssim2.4$ with an observed flattening beyond $z\sim$ 2 with increased stellar mass. Comparing to a range of simulations we find the analytical scaling relation approaches, that invoke an equilibrium model, a good fit to our data, suggesting that a continual smooth accretion regulated by continual outflows may be a key driver in the overall growth of SFGs.Comment: 19 pages, 18 figures, accepted for publication in MNRA

The Likelihood Ratio as a tool for Radio Continuum Surveys with SKA precursor telescopes

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.Comment: 10 pages, 7 figures, accepted for publication in mnra

Computation and Numerical Simulation of Focused Undulator Radiation for Optical Stochastic Cooling

Optical stochastic cooling (OSC) is a promising technique for the cooling of dense particle beams. Its operation at optical frequencies enables obtaining a much larger bandwidth compared to the wellknown microwave-based stochastic cooling. In the OSC undulator radiation generated by a particle in an upstream \pickup" undulator is amplified and focused at the location of a downstream "kicker" undulator. Inside the kicker, a particle interacts with its own radiation field from the pickup. The resulting interaction produces a longitudinal kick with its value depending on the particles momentum which, when correctly phased, yields to longitudinal cooling. The horizontal cooling is achieved by introducing a coupling between longitudinal and horizontal degrees of freedom. Vertical cooling is achieved by coupling between horizontal and vertical motions in the ring. In this paper, we present formulae for computation of the corrective kick and validate them against numerical simulations performed using a wave-optics computer program.Comment: 9 pages, 7 figure

The Stripe 82 1-2 GHz Very Large Array Snapshot Survey: Multiwavelength Counterparts

We have combined spectrosopic and photometric data from the Sloan Digital Sky Survey (SDSS) with $1.4$ GHz radio observations, conducted as part of the Stripe 82 $1-2$ GHz Snapshot Survey using the Karl G. Jansky Very Large Array (VLA), which covers $\sim100$ sq degrees, to a flux limit of 88 $\mu$Jy rms. Cross-matching the $11\,768$ radio source components with optical data via visual inspection results in a final sample of $4\,795$ cross-matched objects, of which $1\,996$ have spectroscopic redshifts and $2\,799$ objects have photometric redshifts. Three previously undiscovered Giant Radio Galaxies (GRGs) were found during the cross-matching process, which would have been missed using automated techniques. For the objects with spectroscopy we separate radio-loud Active Galactic Nuclei (AGN) and star-forming galaxies (SFGs) using three diagnostics and then further divide our radio-loud AGN into the HERG and LERG populations. A control matched sample of HERGs and LERGs, matched on stellar mass, redshift and radio luminosity, reveals that the host galaxies of LERGs are redder and more concentrated than HERGs. By combining with near-infrared data, we demonstrate that LERGs also follow a tight $K-z$ relationship. These results imply the LERG population are hosted by population of massive, passively evolving early-type galaxies. We go on to show that HERGs, LERGs, QSOs and star-forming galaxies in our sample all reside in different regions of a WISE colour-colour diagram. This cross-matched sample bridges the gap between previous `wide but shallow' and `deep but narrow' samples and will be useful for a number of future investigations.Comment: 17 pages, 19 figures. Resubmitted to MNRAS after the initial comment

Autopia: An AI Collaborator for Live Coding Music Performances

Live coding is “the activity of writing (parts of) a program while it runs” (Ward et al., 2004). One significant application of live coding is in algorithmic music, where the performer modifies the code generating the music in a live context. Utopia is a software tool for collaborative live coding performances, allowing several performers (each with their own laptop producing its own sound) to communicate and share code during a performance. We have made an AI bot, Autopia, which can participate in such performances, communicating with human performers through Utopia. This form of human-AI collaboration allows us to explore the implications of computational creativity from the perspective of live coding

SquidLab—A user-friendly program for background subtraction and fitting of magnetization data

We present an open-source program free to download for academic use with a full user-friendly graphical interface for performing flexible and robust background subtraction and dipole fitting on magnetization data. For magnetic samples with small moment sizes or sample environments with large or asymmetric magnetic backgrounds, it can become necessary to separate background and sample contributions to each measured raw voltage measurement before fitting the dipole signal to extract magnetic moments. Originally designed for use with pressure cells on a Quantum Design MPMS3 SQUID magnetometer, SquidLab is a modular object-oriented platform implemented in Matlab with a range of importers for different widely available magnetometer systems (including MPMS, MPMS-XL, MPMS-IQuantum, MPMS3, and S700X models) and has been tested with a broad variety of background and signal types. The software allows background subtraction of baseline signals, signal preprocessing, and performing fits to dipole data using Levenberg–Marquardt non-linear least squares or a singular value decomposition linear algebra algorithm that excels at picking out noisy or weak dipole signals. A plugin system allows users to easily extend the built-in functionality with their own importers, processes, or fitting algorithms. SquidLab can be downloaded, under Academic License, from the University of Warwick depository (wrap.warwick.ac.uk/129665)