We use the energy-balance code MAGPHYS to determine stellar and dust masses, and dust corrected star-formation rates for over 200,000 GAMA galaxies, 170,000 G10-COSMOS galaxies and 200,000 3D-HST galaxies. Our values agree well with previously reported measurements and constitute a representative and homogeneous dataset spanning a broad range in stellar mass (10⁸—10¹²M_⊙), dust mass (10⁶—10⁹M_⊙), and star-formation rates (0.01—100M_⊙yr⁻¹), and over a broad redshift range (0.0 < z < 5.0). We combine these data to measure the cosmic star-formation history (CSFH), the stellar-mass density (SMD), and the dust-mass density (DMD) over a 12 Gyr timeline. The data mostly agree with previous estimates, where they exist, and provide a quasi-homogeneous dataset using consistent mass and star-formation estimators with consistent underlying assumptions over the full time range. As a consequence our formal errors are significantly reduced when compared to the historic literature. Integrating our cosmic star-formation history we precisely reproduce the stellar-mass density with an ISM replenishment factor of 0.50 ± 0.07, consistent with our choice of Chabrier IMF plus some modest amount of stripped stellar mass. Exploring the cosmic dust density evolution, we find a gradual increase in dust density with lookback time. We build a simple phenomenological model from the CSFH to account for the dust mass evolution, and infer two key conclusions: (1) For every unit of stellar mass which is formed 0.0065—0.004 units of dust mass is also formed; (2) Over the history of the Universe approximately 90 to 95 per cent of all dust formed has been destroyed and/or ejected.

We present the Galaxy And Mass Assembly (GAMA) Panchromatic Data Release (PDR) constituting over 230 deg² of imaging with photometry in 21 bands extending from the far-UV to the far-IR. These data complement our spectroscopic campaign of over 300k galaxies, and are compiled from observations with a variety of facilities including: GALaxy Evolution eXplorer, Sloan Digital Sky Survey, Visible and Infrared Telescope for Astronomy (VISTA), Wide-field Infrared Survey Explorer, and Herschel, with the GAMA regions currently being surveyed by VLT Survey Telescope (VST) and scheduled for observations by Australian Square Kilometer Array Pathfinder (ASKAP). These data are processed to a common astrometric solution, from which photometry is derived for ∼221 373 galaxies with r < 19.8 mag. Online tools are provided to access and download data cutouts, or the full mosaics of the GAMA regions in each band. We focus, in particular, on the reduction and analysis of the VISTA VIsta Kilo-degree INfrared Galaxy data, and compare to earlier data sets (i.e. 2MASS and UKIDSS) before combining the data and examining its integrity. Having derived the 21-band photometric catalogue, we proceed to fit the data using the energy balance code magphys. These measurements are then used to obtain the first fully empirical measurement of the 0.1–500 μm energy output of the Universe. Exploring the cosmic spectral energy distribution across three time-intervals (0.3–1.1, 1.1–1.8, and 1.8–2.4 Gyr), we find that the Universe is currently generating (1.5 ± 0.3) × 10³⁵ h₇₀ W Mpc⁻³, down from (2.5 ± 0.2) × 10³⁵ h₇₀ W Mpc⁻³ 2.3 Gyr ago. More importantly, we identify significant and smooth evolution in the integrated photon escape fraction at all wavelengths, with the UV escape fraction increasing from 27(18) per cent at z = 0.18 in NUV(FUV) to 34(23) per cent at z = 0.06. The GAMA PDR can be found at: http://gama-psi.icrar.org/.

Accurate extractions of the detected gravitational wave (GW) signal waveforms are essential to validate a detection and to probe the astrophysics behind the sources producing the GWs. This however could be difficult in realistic scenarios where the signals detected by existing GW detectors could be contaminated with nonstationary and non-Gaussian noise. While the performance of existing waveform extraction methods are optimal, they are not fast enough for online application, which is important for multimessenger astronomy. In this paper, we demonstrate that a deep learning architecture consisting of convolutional neural network and bidirectional long short-term memory components can be used to extract binary black hole (BBH) GW waveforms from realistic noise in a few milliseconds. We have tested our network systematically on injected GW signals, with component masses uniformly distributed in the range of 10 to $80M_{\odot }$, on Gaussian noise and Laser Interferometer Gravitational Wave Observatory (LIGO) detector noise. We find that our model can extract GW waveforms with overlaps of more than 0.95 with pure numerical relativity templates for signals with signal-to-noise ratio greater than six and is also robust against interfering “glitches”. We then apply our model to all ten detected BBH events from LIGO-Virgo’s first (O1) and second (O2) observation runs, obtaining $\ge 0.97$ overlaps for all ten extracted BBH waveforms with the corresponding pure templates. We discuss the implication of our result and its future applications to GW localization and mass estimation.

We present neutral hydrogen (H i) and ionized hydrogen (H α) observations of 10 galaxies out to a redshift of 0.1. The H i observations are from the first epoch (178 h) of the COSMOS H i Large Extragalactic Survey (CHILES). Our sample is H i biased and consists of 10 late-type galaxies with H i masses that range from 1.8 × 10⁷ M_⊙ to 1.1 × 10¹⁰ M_⊙. We find that although the majority of galaxies show irregularities in the morphology and kinematics, they generally follow the scaling relations found in larger samples. We find that the H i and H α velocities reach the flat part of the rotation curve. We identify the large-scale structure in the nearby CHILES volume using DisPerSE with the spectroscopic catalogue from SDSS. We explore the gaseous properties of the galaxies as a function of location in the cosmic web. We also compare the angular momentum vector (spin) of the galaxies to the orientation of the nearest cosmic web filament. Our results show that galaxy spins tend to be aligned with cosmic web filaments and show a hint of a transition mass associated with the spin angle alignment.

We present the dust mass function (DMF) of 15 750 galaxies with redshift $z$ < 0.1, drawn from the overlapping area of the GAMA and H-ATLAS surveys. The DMF is derived using the density corrected Vmax method, where we estimate Vmax using: (i) the normal photometric selection limit (pVmax) and (ii) a bivariate brightness distribution (BBD) technique, which accounts for two selection effects. We fit the data with a Schechter function, and find $M^{*}=(4.65 \pm 0.18)\times 10^{7}\,h^2_{70}\, \mathrm{ M}_{\odot }$, α = (−1.22 ± 0.01), $\phi ^{*}=(6.26 \pm 0.28)\times 10^{-3}\,h^3_{70}\,\rm Mpc^{-3}\,dex^{-1}$. The resulting dust mass density parameter integrated down to 104 M⊙ is Ωd = (1.11 ± 0.02) × 10−6 which implies the mass fraction of baryons in dust is $f_{m_\mathrm{ b}}=(2.40\pm 0.04)\times 10^{-5}$; cosmic variance adds an extra 7–17 per cent uncertainty to the quoted statistical errors. Our measurements have fewer galaxies with high dust mass than predicted by semi-analytic models. This is because the models include too much dust in high stellar mass galaxies. Conversely, our measurements find more galaxies with high dust mass than predicted by hydrodynamical cosmological simulations. This is likely to be from the long time-scales for grain growth assumed in the models. We calculate DMFs split by galaxy type and find dust mass densities of Ωd = (0.88 ± 0.03) × 10−6 and Ωd = (0.060 ± 0.005) × 10−6 for late types and early types, respectively. Comparing to the equivalent galaxy stellar mass functions (GSMF) we find that the DMF for late types is well matched by the GSMF scaled by (8.07 ± 0.35) × 10−4.

Deep learning algorithms, in particular neural networks, have been steadily gaining popularity among the gravitational wave community for the last few years. The reliability and accuracy of deep learning approaches in gravitational wave detection, parameter estimation, and glitch classification have already been proved and verified by several groups in recent years. In this paper, we report on the construction of the deep artificial neural network (ANN) to localize simulated gravitational wave signals in the sky with high accuracy. We have modeled the sky as a sphere and have considered cases in which the sphere is divided into 18, 50, 128, 1024, 2048, and 4096 sectors. The sky direction of the gravitational wave source is estimated by classifying the signal into one of these sectors based on its right ascension and declination values for each of these cases. To do this, we have injected simulated binary black hole gravitational wave signals of component masses sampled uniformly between $30M_{\odot }$ and $80M_{\odot }$ into Gaussian noise and used the whitened strain values to obtain the input features for training our ANN. We input features such as the delays in arrival times, phase differences, and amplitude ratios at each of the three detectors Hanford, Livingston, and Virgo, from the raw time-domain strain values as well as from analytical versions of these signals, obtained through Hilbert transformation. We show that our model is able to classify gravitational wave samples, not used in the training process, into their correct sectors with very high accuracy (greater than 90%) for coarse angular resolution using 18, 50, and 128 sectors. We also test our localization on test samples with injection parameters of the published LIGO binary black hole merger events GW150914, GW170818, and GW170823 for 1024, 2048, and 4096 sectors and compare the result with that from BAYESTAR and parameter estimation. In addition, we report that the time taken by our model to localize one gravitational wave signal is around 0.018 s on 14 Intel Xeon CPU cores.