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

A flat trend of star-formation rate with X-ray luminosity of galaxies hosting AGN in the SCUBA-2 Cosmology Legacy Survey

© 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society.Feedback processes from active galactic nuclei (AGN) are thought to play a crucial role in regulating star formation in massive galaxies. Previous studies using Herschel have resulted in conflicting conclusions as to whether star formation is quenched, enhanced, or not affected by AGN feedback. We use new deep 850 μm observations from the SCUBA-2 Cosmology Legacy Survey (S2CLS) to investigate star formation in a sample of X-ray selected AGN, probing galaxies up to L 0.5-7keV = 10 46 erg s -1. Here, we present the results of our analysis on a sample of 1957 galaxies at 1 < z < 3, using both S2CLS and ancilliary data at seven additional wavelengths (24-500 μm) from Herschel and Spitzer. We perform a stacking analysis, binning our sample by redshift and X-ray luminosity. By fitting analytical spectral energy distributions (SEDs) to decompose contributions from cold and warm dust, we estimate star formation rates (SFRs) for each 'average' source. We find that the average AGN in our sample resides in a star-forming host galaxy, with SFRs ranging from 80 to 600 M ⊙ yr -1. Within each redshift bin, we see no trend of SFR with X-ray luminosity, instead finding a flat distribution of SFR across ∼3 orders of magnitude of AGN luminosity. By studying instantaneous X-ray luminosities and SFRs, we find no evidence that AGN activity affects star formation in host galaxies.Peer reviewedFinal Accepted Versio

Rapid sorting of radio galaxy morphology using Haralick features

We demonstrate the use of Haralick features for the automated classification of radio galaxies. The set of thirteen Haralick features represent an extremely compact non-parametric representation of image texture, and are calculated directly from imagery using the Grey Level Co-occurrence Matrix (GLCM). The GLCM is an encoding of the relationship between the intensity of neighbouring pixels in an image. Using 10 000 sources detected in the first data release of the LOFAR Two-metre Sky Survey (LoTSS), we demonstrate that Haralick features are highly efficient, rotationally invariant descriptors of radio galaxy morphology. After calculating Haralick features for LoTSS sources, we employ the fast density-based hierarchical clustering algorithm hdbscan to group radio sources into a sequence of morphological classes, illustrating a simple methodology to classify and label new, unseen galaxies in large samples. By adopting a ‘soft’ clustering approach, we can assign each galaxy a probability of belonging to a given cluster, allowing for more flexibility in the selection of galaxies according to combinations of morphological characteristics and for easily identifying outliers: those objects with a low probability of belonging to any cluster in the Haralick space. Although our demonstration focuses on radio galaxies, Haralick features can be calculated for any image, making this approach also relevant to large optical imaging galaxy surveys.Peer reviewedFinal Published versio

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

Eigengalaxies: describing galaxy morphology using principal components in image space

This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society © 2020 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical SocietyWe demonstrate how galaxy morphologies can be represented by weighted sums of "eigengalaxies" and how eigengalaxies can be used in a probabilistic framework to enable principled and simplified approaches in a variety of applications. Eigengalaxies can be derived from a Principal Component Analysis (PCA) of sets of single- or multi-band images. They encode the image space equivalent of basis vectors that can be combined to describe the structural properties of large samples of galaxies in a massively reduced manner. As an illustration, we show how a sample of 10,243 galaxies in the Hubble Space Telescope CANDELS survey can be represented by just 12 eigengalaxies. We show in some detail how this image space may be derived and tested. We also describe a probabilistic extension to PCA (PPCA) which enables the eigengalaxy framework to assign probabilities to galaxies. We present four practical applications of the probabilistic eigengalaxy framework that are particularly relevant for the next generation of large imaging surveys: we (i) show how low likelihood galaxies make for natural candidates for outlier detection (ii) demonstrate how missing data can be predicted (iii) show how a similarity search can be performed on exemplars (iv) demonstrate how unsupervised clustering of objects can be implemented.Peer reviewe

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

An automatic taxonomy of galaxy morphology using unsupervised machine learning

This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2017 the Author (s). Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reservedWe present an unsupervised machine learning technique that automatically segments and labels galaxies in astronomical imaging surveys using only pixel data. Distinct from previous unsupervised machine learning approaches used in astronomy we use no pre-selection or pre-filtering of target galaxy type to identify galaxies that are similar. We demonstrate the technique on the Hubble Space Telescope (HST) Frontier Fields. By training the algorithm using galaxies from one field (Abell 2744) and applying the result to another (MACS 0416.1-2403), we show how the algorithm can cleanly separate early and late type galaxies without any form of pre-directed training for what an 'early' or 'late' type galaxy is. We then apply the technique to the HST Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey (CANDELS) fields, creating a catalogue of approximately 60 000 classifications. We show how the automatic classification groups galaxies of similar morphological (and photometric) type and make the classifications public via a catalogue, a visual catalogue and galaxy similarity search. We compare the CANDELS machine-based classifications to human-classifications from the Galaxy Zoo: CANDELS project. Although there is not a direct mapping between Galaxy Zoo and our hierarchical labelling, we demonstrate a good level of concordance between human and machine classifications. Finally, we show how the technique can be used to identify rarer objects and present lensed galaxy candidates from the CANDELS imaging.Peer reviewe