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Measuring the environments of massive galaxies at high redshift is crucial to understanding galaxy evolution and the conditions that gave rise to the distribution of matter we see in the Universe today. While high-$z$ radio galaxies (H$z$RGs) and quasars tend to reside in protocluster-like systems, the environments of their radio-quiet counterparts are relatively unexplored, particularly in the submillimetre, which traces dust-obscured star formation. In this study we search for 850 $\mu$m-selected submillimetre galaxies in the environments of massive ($M_{\star} > 10^{11} M_{\odot}$), radio-quiet ($L_{500 {\rm MHz}} \lesssim 10^{25}$ W Hz$^{-1}$) galaxies at $z \sim 1\text{--}3$ using S2COSMOS data. By constructing number counts in circular regions of radius 1--6 arcmin and comparing with blank-field measurements, we find no significant overdensities of SMGs around massive radio-quiet galaxies at any of these scales, despite being sensitive down to overdensities of $\delta \sim 0.4$. To probe deeper than the catalogue we also examine the distribution of peaks in the SCUBA-2 SNR map, which reveals only tentative signs of any difference in the SMG densities of the radio-quiet galaxy environments compared to the blank field, and only on smaller scales (1$^{\prime}$ radii, corresponding to $\sim0.5$ Mpc) and higher SNR thresholds. We conclude that massive, radio-quiet galaxies at cosmic noon are typically in environments with $\delta\lesssim0.4$, which are either consistent with the blank field or contain only weak overdensities spanning sub-Mpc scales. The contrast between our results and studies of H$z$RGs with similar stellar masses and redshifts implies an intrinsic link between the wide-field environment and radio AGN luminosity at high redshift., Comment: 13 pages, 5 figures. Published in MNRAS

Spectral energy distribution (SED) fitting has been extensively used to determine the nature of the faint radio source population. Recent efforts have combined fits from multiple SED-fitting codes to account for the host galaxy and any active nucleus that may be present. We show that it is possible to produce similar-quality classifications using a single energy-balance SED fitting code, Prospector, to model up to 26 bands of UV$-$far-infrared aperture-matched photometry for $\sim$31,000 sources in the ELAIS-N1 field from the LOFAR Two-Metre Sky Survey (LoTSS) Deep fields first data release. One of a new generation of SED-fitting codes, Prospector accounts for potential contributions from radiative active galactic nuclei (AGN) when estimating galaxy properties, including star formation rates (SFRs) derived using non-parametric star formation histories. Combining this information with radio luminosities, we classify 92 per cent of the radio sources as a star-forming galaxy, high/low-excitation radio galaxy, or radio-quiet AGN and study the population demographics as a function of 150 MHz flux density, luminosity, SFR, stellar mass, redshift and apparent $r$-band magnitude. Finally, we use Prospector SED fits to investigate the SFR$-$150 MHz luminosity relation for a sample of $\sim$$133,000~3.6~\mu$m-selected $z<1$ sources, finding that the stellar mass dependence is significantly weaker than previously reported, and may disappear altogether at $\log_{10} (\mathrm{SFR}/M_\odot~\mathrm{yr}^{-1}) > 0.5$. This approach makes it significantly easier to classify radio sources from LoTSS and elsewhere, and may have important implications for future studies of star-forming galaxies at radio wavelengths., Comment: 20 pages, 13 figures, accepted for publication in MNRAS

This paper presents a physics-informed neural network (PINN) approach for monitoring the health of diesel engines. The aim is to evaluate the engine dynamics, identify unknown parameters in a "mean value" model, and anticipate maintenance requirements. The PINN model is applied to diesel engines with a variable-geometry turbocharger and exhaust gas recirculation, using measurement data of selected state variables. The results demonstrate the ability of the PINN model to predict simultaneously both unknown parameters and dynamics accurately with both clean and noisy data, and the importance of the self-adaptive weight in the loss function for faster convergence. The input data for these simulations are derived from actual engine running conditions, while the outputs are simulated data, making this a practical case study of PINN's ability to predict real-world dynamical systems. The mean value model of the diesel engine incorporates empirical formulae to represent certain states, but these formulae may not be generalizable to other engines. To address this, the study considers the use of deep neural networks (DNNs) in addition to the PINN model. The DNNs are trained using laboratory test data and are used to model the engine-specific empirical formulae in the mean value model, allowing for a more flexible and adaptive representation of the engine's states. In other words, the mean value model uses both the PINN model and the DNNs to represent the engine's states, with the PINN providing a physics-based understanding of the engine's overall dynamics and the DNNs offering a more engine-specific and adaptive representation of the empirical formulae. By combining these two approaches, the study aims to offer a comprehensive and versatile approach to monitoring the health and performance of diesel engines.

We develop a data-driven deep neural operator framework to approximate multiple output states for a diesel engine and generate real-time predictions with reasonable accuracy. As emission norms become more stringent, the need for fast and accurate models that enable analysis of system behavior have become an essential requirement for system development. The fast transient processes involved in the operation of a combustion engine make it difficult to develop accurate physics-based models for such systems. As an alternative to physics based models, we develop an operator-based regression model (DeepONet) to learn the relevant output states for a mean-value gas flow engine model using the engine operating conditions as input variables. We have adopted a mean-value model as a benchmark for comparison, simulated using Simulink. The developed approach necessitates using the initial conditions of the output states to predict the accurate sequence over the temporal domain. To this end, a sequence-to-sequence approach is embedded into the proposed framework. The accuracy of the model is evaluated by comparing the prediction output to ground truth generated from Simulink model. The maximum $\mathcal L_2$ relative error observed was approximately $6.5\%$. The sensitivity of the DeepONet model is evaluated under simulated noise conditions and the model shows relatively low sensitivity to noise. The uncertainty in model prediction is further assessed by using a mean ensemble approach. The worst-case error at the $(\mu + 2\sigma)$ boundary was found to be $12\%$. The proposed framework provides the ability to predict output states in real-time and enables data-driven learning of complex input-output operator mapping. As a result, this model can be applied during initial development stages, where accurate models may not be available., Comment: Updated manuscript title to better reflect this work and field of study

New-generation radio telescopes like LOFAR are conducting extensive sky surveys, detecting millions of sources. To maximise the scientific value of these surveys, radio source components must be properly associated into physical sources before being cross-matched with their optical/infrared counterparts. In this paper, we use machine learning to identify those radio sources for which either source association is required or statistical cross-matching to optical/infrared catalogues is unreliable. We train a binary classifier using manual annotations from the LOFAR Two-metre Sky Survey (LoTSS). We find that, compared to a classification model based on just the radio source parameters, the addition of features of the nearest-neighbour radio sources, the potential optical host galaxy, and the radio source composition in terms of Gaussian components, all improve model performance. Our best model, a gradient boosting classifier, achieves an accuracy of 95 per cent on a balanced dataset and 96 per cent on the whole (unbalanced) sample after optimising the classification threshold. Unsurprisingly, the classifier performs best on small, unresolved radio sources, reaching almost 99 per cent accuracy for sources smaller than 15 arcsec, but still achieves 70 per cent accuracy on resolved sources. It flags 68 per cent more sources than required as needing visual inspection, but this is still fewer than the manually-developed decision tree used in LoTSS, while also having a lower rate of wrongly accepted sources for statistical analysis. The results have an immediate practical application for cross-matching the next LoTSS data releases and can be generalised to other radio surveys., Comment: Accepted for publication in MNRAS. 24 pages, 13 Figures. Submission includes a data table which is also available at https://drive.google.com/file/d/1mwKWJW6oCqh-joNR1uV3PPPYDdYz0n_J/view?usp=sharing

Radio galaxies are linearly polarized -- an important property that allows us to infer the properties of the magnetic field of the source and its environment. However at low frequencies, Faraday rotation substantially depolarizes the emission, meaning that comparatively few polarized radio galaxies are known at low frequencies. Using the LOFAR Two Metre Sky Survey at 150 MHz and at 20 arcsec resolution, we select 342 radio galaxies brighter than 50 mJy and larger than 100 arcsec in angular size, of which 67 are polarized (18 per cent detection fraction). These are predominantly Fanaroff Riley type II (FR-II) sources. The detection fraction increases with total flux density, and exceeds 50 per cent for sources brighter than 1 Jy. We compare the sources in our sample detected by LOFAR to those also detected in NVSS at 1400 MHz, and find that our selection bias toward bright radio galaxies drives a tendency for sources depolarized between 1400 and 150 MHz to have flatter spectra over that frequency range than those that remain polarized at 150 MHz. By comparing observed rotation measures with an analytic model we find that we are preferentially sensitive to sources in low mass environments. We also infer that sources with one polarized hotspot are inclined by a small angle to the line of sight, while sources with hotspots in both lobes lie in the plane of the sky. We conclude that low frequency polarization in radio galaxies is related to a combination of environment, flux density and jet orientation., Comment: 20 pages, 19 figures. Accepted for publication in MNRAS, December 22nd 2020

We test the efficacy of the energy-balance spectral energy distribution (SED) fitting code Magphys for recovering the spatially-resolved properties of a simulated isolated disc galaxy, for which it was not designed. We perform 226,950 Magphys SED fits to regions between 0.2kpc and 25kpc in size across the galaxy's disc, viewed from three different sight-lines, to probe how well Magphys can recover key galaxy properties based on 21 bands of UV--far-infrared model photometry. Magphys yields statistically acceptable fits to $> 99$ per cent of the pixels within the $r$-band effective radius and between 59 and 77 per cent of pixels within 20kpc of the nucleus. Magphys is able to recover the distribution of stellar mass, star formation rate (SFR), specific SFR, dust luminosity, dust mass, and $V$-band attenuation reasonably well, especially when the pixel size is $> \sim1$ kpc, whereas non-standard outputs (stellar metallicity and mass-weighted age) are recovered less well. Accurate recovery is more challenging in the smallest sub-regions of the disc (pixel scale $< \sim 1$ kpc), where the energy balance criterion becomes increasingly incorrect. Estimating integrated galaxy properties by summing the recovered pixel values, the true integrated values of all parameters considered except metallicity and age are well recovered at all spatial resolutions, ranging from 0.2kpc to integrating across the disc, albeit with some evidence for resolution-dependent biases. These results must be considered when attempting to analyse the structure of real galaxies with actual observational data, for which the `ground truth' is unknown., Comment: 21 pages, 15 figures, Accepted for publication in MNRAS

Interferometric observations have demonstrated that a significant fraction of single-dish submillimetre (submm) sources are blends of multiple submm galaxies (SMGs), but the nature of this multiplicity, i.e. whether the galaxies are physically associated or chance projections, has not been determined. We performed spectroscopy of 11 SMGs in six multi-component submm sources, obtaining spectroscopic redshifts for nine of them. For an additional two component SMGs, we detected continuum emission but no obvious features. We supplement our observed sources with four sources from the literature. This sample allows us to statistically constrain the physical nature of single-dish submm source multiplicity for the first time. In three [3/7, or 43 (-33/+39) per cent at 95% confidence] of the single-dish sources for which the nature of the blending is unambiguous, the components for which spectroscopic redshifts are available are physically associated, whereas 4/7 [57 (-39/+33) per cent] have at least one unassociated component. When components whose spectra exhibit continuum but no features and for which the photometric redshift is significantly different from the spectroscopic redshift of the other component are also considered, 6/9 [67 (-37/+26) per cent] of the single-dish sources are comprised of at least one unassociated component. The nature of the multiplicity of one single-dish source is ambiguous. We conclude that physically associated systems and chance projections both contribute to the multi-component single-dish submm source population. This result contradicts the conventional wisdom that bright submm sources are solely a result of merger-induced starbursts, as blending of unassociated galaxies is also important., Comment: Accepted for publication in MNRAS. 11 pages, 5 figures, 2 tables. Fig. 5 shows the key result

We present the largest submillimeter images that have been made of the extragalactic sky. The Herschel Astrophysical Terahertz Large Area Survey (H-ATLAS) is a survey of 660 deg$^2$ with the PACS and SPIRE cameras in five photometric bands: 100, 160, 250, 350, and 500{\mu}m. In this paper we present the images from our two largest fields which account for ~75% of the survey. The first field is 180.1 deg$^2$ in size centered on the North Galactic Pole (NGP) and the second field is 317.6 deg$^2$ in size centered on the South Galactic Pole. The NGP field serendipitously contains the Coma cluster. Over most (~80%) of the images, the pixel noise, including both instrumental noise and confusion noise, is approximately 3.6, and 3.5 mJy/pix at 100 and 160{\mu}m, and 11.0, 11.1 and 12.3 mJy/beam at 250, 350 and 500{\mu}m, respectively, but reaches lower values in some parts of the images. If a matched filter is applied to optimize point-source detection, our total 1{\sigma} map sensitivity is 5.7, 6.0, and 7.3 mJy at 250, 350, and 500{\mu}m, respectively. We describe the results of an investigation of the noise properties of the images. We make the most precise estimate of confusion in SPIRE maps to date finding values of 3.12+/-0.07, 4.13+/-0.02 and 4.45+/-0.04 mJy/beam at 250, 350, and 500{\mu}m in our un-convolved maps. For PACS we find an estimate of the confusion noise in our fast-parallel observations of 4.23 and 4.62 mJy/beam at 100 and 160{\mu}m. Finally, we give recipes for using these images to carry out photometry, both for unresolved and extended sources., Comment: 26 Pages, 14 Figures. Submitted to ApJS July 2017, accepted November 2017

We present a study of photometric redshift performance for galaxies and active galactic nuclei detected in deep radio continuum surveys. Using two multi-wavelength datasets, over the NOAO Deep Wide Field Survey Bo\"otes and COSMOS fields, we assess photometric redshift (photo-z) performance for a sample of $\sim 4,500$ radio continuum sources with spectroscopic redshifts relative to those of $\sim 63,000$ non radio-detected sources in the same fields. We investigate the performance of three photometric redshift template sets as a function of redshift, radio luminosity and infrared/X-ray properties. We find that no single template library is able to provide the best performance across all subsets of the radio detected population, with variation in the optimum template set both between subsets and between fields. Through a hierarchical Bayesian combination of the photo-z estimates from all three template sets, we are able to produce a consensus photo-z estimate which equals or improves upon the performance of any individual template set., Comment: 20 pages, 17 figures. Accepted for publication in MNRAS