Generative deep fields : arbitrarily sized, random synthetic astronomical images through deep learning

© 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society.Generative Adversarial Networks (GANs) are a class of artificial neural network that can produce realistic, but artificial, images that resemble those in a training set. In typical GAN architectures these images are small, but a variant known as Spatial-GANs (SGANs) can generate arbitrarily large images, provided training images exhibit some level of periodicity. Deep extragalactic imaging surveys meet this criteria due to the cosmological tenet of isotropy. Here we train an SGAN to generate images resembling the iconic Hubble Space Telescope eXtreme Deep Field (XDF). We show that the properties of 'galaxies' in generated images have a high level of fidelity with galaxies in the real XDF in terms of abundance, morphology, magnitude distributions and colours. As a demonstration we have generated a 7.6-billion pixel 'generative deep field' spanning 1.45 degrees. The technique can be generalised to any appropriate imaging training set, offering a new purely data-driven approach for producing realistic mock surveys and synthetic data at scale, in astrophysics and beyond.Peer reviewe

The linear bias of radio galaxies at z~0.3 via cosmic microwave background lensing

© 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical SocietyWe present a new measurement of the linear bias of radio loud active galactic nuclei (RLAGN) at $z\approx0.3$ and $L_{\rm 1.4GHz}>10^{23}\,{\rm W\,Hz^{-1}}$ selected from the Best & Heckman (2012) sample, made by cross-correlating the RLAGN surface density with a map of the convergence of the weak lensing field of the cosmic microwave background from Planck. We detect the cross-power signal at a significance of $3\sigma$ and use the amplitude of the cross-power spectrum to estimate the linear bias of RLAGN, $b=2.5 \pm 0.8$, corresponding to a typical dark matter halo mass of $\log_{10}(M_{\rm h} /h^{-1} M_\odot)=14.0^{+0.3}_{-0.5}$. When RLAGN associated with optically-selected clusters are removed we measure a lower bias corresponding to $\log_{10}(M_{\rm h} /h^{-1} M_\odot)=13.7^{+0.4}_{-1.0}$. These observations support the view that powerful RLAGN typically inhabit rich group and cluster environments.Peer reviewe

Low-power radio galaxy environments in the Subaru/XMM-Newton Deep Field at z~0.5

We present multi-object spectroscopy of galaxies in the immediate (Mpc-scale) environments of four low-power (L_1.4 GHz < 10^25 W/Hz) radio galaxies at z~0.5, selected from the Subaru/XMM-Newton Deep Field. We use the spectra to calculate velocity dispersions and central redshifts of the groups the radio galaxies inhabit, and combined with XMM-Newton (0.3-10 keV) X-ray observations investigate the L_X--sigma_v and T_X--sigma_v scaling relationships. All the radio galaxies reside in moderately rich groups -- intermediate environments between poor groups and rich clusters, with remarkably similar X-ray properties. We concentrate our discussion on our best statistical example that we interpret as a low-power (FRI) source triggered within a sub-group, which in turn is interacting with a nearby group of galaxies, containing the bulk of the X-ray emission for the system -- a basic scenario which can be compared to more powerful radio sources at both high (z>4) and low (z<0.1) redshifts. This suggests that galaxy-galaxy interactions triggered by group mergers may play an important role in the life-cycle of radio galaxies at all epochs and luminosities.Comment: 12 pages, 7 figures, accepted for publication in MNRAS. High resolution version available upon reques

Cluster richness-mass calibration with cosmic microwave background lensing

This document is the Accepted Manuscript of the following article: James E. Geach, and John A. Peacock, ‘Cluster richness–mass calibration with cosmic microwave background lensing’, Nature Astronomy, Vol. 1: 795-799, 2017. Under embargo until 9 April 2018. This manuscript version is made available under Springer Nature terms for reuse, see http://www.nature.com/authors/policies/license.html#terms The final, definitive version of this paper has been published in Nature Astronomy, at doi: https://doi.org/10.1038/s41550-017-0259-1.Identifying galaxy clusters through overdensities of galaxies in photometric surveys is the oldest and arguably the most economic and mass-sensitive detection method, compared to X-ray and Sunyaev-Zel'dovich Effect surveys that detect the hot intracluster medium. However, a perennial problem has been the mapping of optical 'richness' measurements on to total cluster mass. Emitted at a conformal distance of 14 Gpc, the cosmic microwave background acts as a backlight to all intervening mass in the Universe, and therefore has been gravitationally lensed. Here we present a calibration of cluster optical richness at the 10 per cent level by measuring the average cosmic microwave background lensing convergence measured by Planck towards the positions of large numbers of optically-selected clusters, detecting the deflection of photons by haloes of total mass of the order 10**14 solar masses. Although mainly aimed at the study of larger-scale structures, the Planck lensing reconstruction can yield nearly unbiased results for stacked clusters on arcminute scales. The lensing convergence only depends on the redshift integral of the fractional overdensity of matter, so this approach offers a clean measure of cluster mass over most of cosmic history, largely independent of baryon physics.Peer reviewe

ALMA Observations of Lyα Blob 1: Halo Substructure Illuminated from Within

We present new Atacama Large Millimeter/Submillimeter Array (ALMA) 850 μm continuum observations of the original Lyα Blob (LAB) in the SSA22 field at z = 3.1 (SSA22-LAB01). The ALMA map resolves the previously identified submillimeter source into three components with a total flux density of S_(850) = 1.68 ± 0.06 mJy, corresponding to a star-formation rate of ~150 M ⊙ yr^(−1). The submillimeter sources are associated with several faint (m ≈ 27 mag) rest-frame ultraviolet sources identified in Hubble Space Telescope Imaging Spectrograph (STIS) clear filter imaging (λ ≈ 5850 Å). One of these companions is spectroscopically confirmed with the Keck Multi-Object Spectrometer For Infra-Red Exploration to lie within 20 projected kpc and 250 km s^(−1) of one of the ALMA components. We postulate that some of these STIS sources represent a population of low-mass star-forming satellites surrounding the central submillimeter sources, potentially contributing to their growth and activity through accretion. Using a high-resolution cosmological zoom simulation of a 10^(13) M⊙ halo at z = 3, including stellar, dust, and Lyα radiative transfer, we can model the ALMA+STIS observations and demonstrate that Lyα photons escaping from the central submillimeter sources are expected to resonantly scatter in neutral hydrogen, the majority of which is predicted to be associated with halo substructure. We show how this process gives rise to extended Lyα emission with similar surface brightness and morphology to observed giant LABs

Fatalism and Future Contingents

In this paper I address issues related to the problem of future contingents and the metaphysical doctrine of fatalism. Two classical responses to the problem of future contingents are the third truth value view and the all-false view. According to the former, future contingents take a third truth value which goes beyond truth and falsity. According to the latter, they are all false. I here illustrate and discuss two ways to respectively argue for those two views. Both ways are similar in spirit and intimately connected with fatalism, in the sense that they engage with the doctrine of fatalism and accept a large part of a standard fatalistic machinery

Paralysis and delayed Z-disc formation in the Xenopus tropicalis unc45b mutant dicky ticker

<p>Abstract</p> <p>Background</p> <p>The protein components of mature skeletal muscle have largely been characterized, but the mechanics and sequence of their assembly during normal development remain an active field of study. Chaperone proteins specific to sarcomeric myosins have been shown to be necessary in zebrafish and invertebrates for proper muscle assembly and function.</p> <p>Results</p> <p>The <it>Xenopus tropicalis </it>mutation <it>dicky ticker </it>results in disrupted skeletal muscle myofibrillogenesis, paralysis, and lack of heartbeat, and maps to a missense mutation in the muscle-specific chaperone <it>unc45b</it>. <it>Unc45b </it>is known to be required for folding the head domains of myosin heavy chains, and mutant embryos fail to incorporate muscle myosin into sarcomeres. Mutants also show delayed polymerization of α-actinin-rich Z-bodies into the Z-disks that flank the myosin-containing A-band.</p> <p>Conclusions</p> <p>The <it>dicky ticker </it>phenotype confirms that a requirement for myosin-specific chaperones is conserved in tetrapod sarcomerogenesis, and also suggests a novel role for myosin chaperone function in Z-body maturation.</p

Astronomia ex machina: a history, primer, and outlook on neural networks in astronomy

In recent years, deep learning has infiltrated every field it has touched, reducing the need for specialist knowledge and automating the process of knowledge discovery from data. This review argues that astronomy is no different, and that we are currently in the midst of a deep learning revolution that is transforming the way we do astronomy. We trace the history of astronomical connectionism from the early days of multilayer perceptrons, through the second wave of convolutional and recurrent neural networks, to the current third wave of self-supervised and unsupervised deep learning. We then predict that we will soon enter a fourth wave of astronomical connectionism, in which finetuned versions of an all-encompassing 'foundation' model will replace expertly crafted deep learning models. We argue that such a model can only be brought about through a symbiotic relationship between astronomy and connectionism, whereby astronomy provides high quality multimodal data to train the foundation model, and in turn the foundation model is used to advance astronomical research.Comment: 60 pages, 269 references, 29 figures. Review submitted to Royal Society Open Science. Comments and feedback welcom

EarthPT: a foundation model for Earth Observation

We introduce EarthPT -- an Earth Observation (EO) pretrained transformer. EarthPT is a 700 million parameter decoding transformer foundation model trained in an autoregressive self-supervised manner and developed specifically with EO use-cases in mind. We demonstrate that EarthPT is an effective forecaster that can accurately predict future pixel-level surface reflectances across the 400-2300 nm range well into the future. For example, forecasts of the evolution of the Normalised Difference Vegetation Index (NDVI) have a typical error of approximately 0.05 (over a natural range of -1 -> 1) at the pixel level over a five month test set horizon, out-performing simple phase-folded models based on historical averaging. We also demonstrate that embeddings learnt by EarthPT hold semantically meaningful information and could be exploited for downstream tasks such as highly granular, dynamic land use classification. Excitingly, we note that the abundance of EO data provides us with -- in theory -- quadrillions of training tokens. Therefore, if we assume that EarthPT follows neural scaling laws akin to those derived for Large Language Models (LLMs), there is currently no data-imposed limit to scaling EarthPT and other similar `Large Observation Models.'Comment: 7 pages, 4 figures, submitted to NeurIPS CCAI worksho