Radio Galaxy Classification with wGAN-Supported Augmentation

Novel techniques are indispensable to process the flood of data from the new generation of radio telescopes. In particular, the classification of astronomical sources in images is challenging. Morphological classification of radio galaxies could be automated with deep learning models that require large sets of labelled training data. Here, we demonstrate the use of generative models, specifically Wasserstein GANs (wGAN), to generate artificial data for different classes of radio galaxies. Subsequently, we augment the training data with images from our wGAN. We find that a simple fully-connected neural network for classification can be improved significantly by including generated images into the training set.Comment: 10 pages, 6 figures; accepted to ml.astro; v2: matches 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

Using Deep Learning to Explore Ultra-Large Scale Astronomical Datasets

In every field that deep learning has infiltrated we have seen a reduction in the use of specialist knowledge, to be replaced with knowledge automatically derived from data. We have already seen this process play out in many ‘applied deep learning’ fields such as computer Go, protein folding, natural language processing, and computer vision. This thesis argues that astronomy is no different to these applied deep learning fields. To this end, this thesis’ introduction serves as a historical background on astronomy’s ‘three waves’ of increasingly automated connectionism: initial work on multilayerperceptrons within astronomy required manually selected emergent properties as input; the second wave coincided with the dissemination of convolutional neural networks and recurrent neural networks, models where the multilayer perceptron’s manually selected inputs are replaced with raw data ingestion; and in the current third wave we are seeing the removal of human supervision altogether with deep learning methods inferring labels and knowledge directly from the data. §2, §3, and §4 of this thesis explore these waves through application. In §2 I show that a convolutional/recurrent encoder/decoder network is capable of emulating a complicated semi-manual galaxy processing pipeline. I find that this ‘Pix2Prof’ neural network can satisfactorily carry out this task over 100x faster than the method it emulates. §3 and §4 explore the application of deep generative models to astronomical simulation. §3 uses a generative adversarial network to generate mock deep field surveys, and finds it capable of generating mock images that are statistically indistinguishable from the real thing. Likewise, §4 demonstrates that a Diffusion model is capable of generating galaxy images that are both qualitatively and quantitatively indistinguishable from the training set. The main benefit of these deep learning based simulations is that they do not rely on a possibly flawed (or incomplete) physical knowledge of their subjects and observation processes. Also, once trained, they are capable of rapidly generating a very large amount of mock data. §5 looks to the future and predicts that we will soon enter a fourth wave of astronomical connectionism. If astronomy follows in the footsteps of other applied deep learning fields we will see the removal of expertly crafted deep learning models, to be replaced with finetuned versions of an all-encompassing ‘foundation’ model. As part of this fourth wave I argue for a symbiosis between astronomy and connectionism. This symbiosis is predicated on astronomy’s relative data wealth, and contemporary deep learning’s enormous data appetite; many ultra-large datasets in machine learning are proprietary or of poor quality, and so astronomy as a whole could develop and provide a high quality multimodal public dataset. In turn, this dataset could be used to train an astronomical foundation model that can be used for state-of-the-art downstream tasks. Due to the foundation models’ hunger for data and compute, a single astronomical research group could not bring about such a model alone. Therefore, I conclude that astronomy as a whole has slim chance of keeping up with a research pace set by the Big Tech goliaths—that is, unless we follow the examples of EleutherAI and HuggingFace and pool our resources in a grassroots open source fashion

Exploring the use of Machine Learning with extragalactic emission-line surveys, in preparation for the Square Kilometre Array

This thesis investigates the use of machine learning for analysing the kinematics of galaxies in a time efficient manner. The application of machine learning in astronomy is arguably nascent, and very much so in the case of galaxy kinematics. Being able to extract kinematic information at speed will be important come the advent of next generation telescopes such as the Square Kilometre Array. Such instruments will collect raw data on scales too large to store. Therefore, the use of on the fly modelling techniques, harnessing the power of machine learning, is crucial. I will show that it is possible and beneficial to use machine learning algorithms to tackle scientific questions in extragalactic astronomy in this way. This thesis starts by investigating the use of machine learning algorithms for rapidly discriminating between disturbed and orderly rotating gas discs in galaxies. Specifically, cold dense molecular gas discs are embedded onto a latent manifold using convolutional autoencoders (CAE) which boast powerful automated feature embedding capabilities. Using hydrodynamical simulations to create mock observational data, the CAE is trained on millions of naturally augmented moment one maps before testing on observational HI data from the Local Volume HI Survey (Koribalski et al. 2018), as well CO observational data from various surveys using ALMA. Using a simple binary classifier on the embeddings, it can be shown that disturbed and orderly rotating discs are separately classified with high accuracy even in the presence of injected noise. Such models may be useful as fast filtering tools for identifying mergers or relaxed discs for further kinematic modelling. Bearing in mind that transfer learning for next generation survey datasets holds great risk, a new approach to kinematically characterising gas in galaxies is studied next. Using self-supervised physics-aware neural networks, the need for a throw-away training set is removed entirely, and replaced with a model which can learn physical parameterisations of galaxy rotation curves at rapid speed. With the introduction of monte carlo dropout, it is also possible to recover modelling errors for kinematic parameters, which will be useful in gauging the validity of learned parameters. These models are tested on simulated data as well as observational CO data from the WISDOM survey and HI data from THINGS (Walter et al. 2008). Learned rotation curves match well with those derived from more analytically motivated modelling tools (e.g. Bbarolo, Di Teodoro & Fraternali 2015), but compute parameterisations in a fraction of the time. Finally I study the use of the aforementioned self-supervised physics-aware neural networks, to recover the H-alpha Tully-Fisher relation (TFR) from largest IFU dataset to date. To do so, moment maps from both SAMI and MaNGA IFU surveys are used to derive the rotational velocities of low redshift galaxies. These are then fit against mass to derive both the forward and reverse TFR. The fits are in agreement with those found in the wider literature except that my fits have shallower gradients because a correction for asymmetric drift is applied in this work, but not in the comparison fits from the literature. Here, I identify and quantify trends between position along (and perpendicular to) the TFR and galaxy properties, namely: age and mass-to-light ratio. A clear relation is also discussed between velocity turnover radius, r-turn/r-e, and stellar mass. The application of models originally designed for use with millimetre and radio interferometric data, shows the benefits of using self-supervised physics-aware approaches to circumvent the problems often associated with transfer learning. Such methods will be useful when applied to next generation IFU survey data releases, with instruments such as HECTOR. In summary, in this thesis, I explore the different machine learning approaches to kinematically characterise galaxies in a time-efficient manner. I conclude with some remaining questions and avenues for future research

On Neural Architectures for Astronomical Time-series Classification with Application to Variable Stars

Despite the utility of neural networks (NNs) for astronomical time-series classification, the proliferation of learning architectures applied to diverse datasets has thus far hampered a direct intercomparison of different approaches. Here we perform the first comprehensive study of variants of NN-based learning and inference for astronomical time-series, aiming to provide the community with an overview on relative performance and, hopefully, a set of best-in-class choices for practical implementations. In both supervised and self-supervised contexts, we study the effects of different time-series-compatible layer choices, namely the dilated temporal convolutional neural network (dTCNs), Long-Short Term Memory (LSTM) NNs, Gated Recurrent Units (GRUs) and temporal convolutional NNs (tCNNs). We also study the efficacy and performance of encoder-decoder (i.e., autoencoder) networks compared to direct classification networks, different pathways to include auxiliary (non-time-series) metadata, and different approaches to incorporate multi-passband data (i.e., multiple time-series per source). Performance---applied to a sample of 17,604 variable stars from the MACHO survey across 10 imbalanced classes---is measured in training convergence time, classification accuracy, reconstruction error, and generated latent variables. We find that networks with Recurrent NN (RNNs) generally outperform dTCNs and, in many scenarios, yield to similar accuracy as tCNNs. In learning time and memory requirements, convolution-based layers are more performant. We conclude by discussing the advantages and limitations of deep architectures for variable star classification, with a particular eye towards next-generation surveys such as LSST, WFIRST and ZTF2.Comment: Submitted to ApJ

Application of statistical pattern recognition and deep learning for morphological classification in radio astronomy

Thesis (MSc)--Stellenbosch University, 2022.ENGLISH ABSTRACT: The morphological classification of radio sources is important to gain a full under standing of galaxy evolution processes and their relation with local environmental properties. Furthermore, the complex nature of the problem, its appeal for citi zen scientists and the large data rates generated by existing and upcoming radio telescopes combine to make the morphological classification of radio sources an ideal test case for the application of machine learning techniques. One approach that has shown great promise recently is Convolutional Neural Networks (CNNs). Literature, however, lacks two major things when it comes to CNNs and radio galaxy morphological classification. Firstly, a proper analysis to identify whether overfitting occurs when training CNNs to perform radio galaxy morphological clas sification is needed. Secondly, a comparative study regarding the practical appli cability of the CNN architectures in literature is required. Both of these short comings are addressed in this thesis. Multiple performance metrics are used for the latter comparative study, such as inference time, model complexity, compu tational complexity and mean per class accuracy. A ranking system based upon recognition and computational performance is proposed. MCRGNet, ATLAS and ConvXpress (novel classifier) are the architectures that best balance computational requirements with recognition performance.AFRIKAANSE OPSOMMING: Die morfologiese klassifikasie van radiobronne is belangrik om ’n volledige begrip van die evolusieprosesse binnein sterrestelsels te ontwikkel, asook die rol wat hul plaaslike omgewings hierin speel. As gevolg van die ingewikkelde aard van die probleem, asook die aantrekkingskrag daarvan vir “burgerwetenskaplikes” en die groot hoeveelhede data wat deur bestaande en opkomende radioteleskope gege nereer word, maak die morfologiese klassifikasie van radiobronne ’n ideale proef gebied vir die toepassing van masjienleertegnieke. ’n Benadering wat belowend lyk, is Konvolusionele Neurale Netwerke (KNNe). Literatuur ontbreek egter twee belangrike dinge as dit kom by KNNe en die morfologiese klassifikasie van radio sterrestelsels. Eerstens is daar ’n analise nodig rondom die identifikasie van oor passing wanneer KNNe afgerig word om radio sterrestelsels volgens morfologie te klassifiseer. Tweedens word ’n vergelykende studie oor die praktiese toepaslik heid van die KNN-argitekture in literatuur benodig. Albei hierdie tekortkominge word in hierdie tesis aagespreek. Veelvuldige prestasiemetings word vir laasgenoemde vergelykende studie gebruik, soos inferensietyd, modelkompleksiteit, berekeningkompleksiteit en gemiddelde akkuraatheid per klas. ’n Rangorde skema word voorgestel gebaseer op herkenning en berekeningsprestasie. MCRGNet, AT LAS en ConvXpress (nuwe bydrae) is die argitekture wat berekeningsvereistes en herkenningsprestasie die beste balanseer.Master

Foregrounds and their mitigation

The low-frequency radio sky is dominated by the diffuse synchrotron emission of our Galaxy and extragalactic radio sources related to Active Galactic Nuclei and star-forming galaxies. This foreground emission is much brighter than the cosmological 21 cm emission from the Cosmic Dawn and Epoch of Reionization. Studying the physical properties of the foregrounds is therefore of fundamental importance for their mitigation in the cosmological 21 cm experiments. This chapter gives a comprehensive overview of the foregrounds and our current state- of-the-art knowledge about their mitigation

Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

Classification and Segmentation of Galactic Structuresin Large Multi-spectral Images

Extensive and exhaustive cataloguing of astronomical objects is imperative for studies seeking to understand mechanisms which drive the universe. Such cataloguing tasks can be tedious, time consuming and demand a high level of domain specific knowledge. Past astronomical imaging surveys have been catalogued through mostly manual effort. Immi-nent imaging surveys, however, will produce a magnitude of data that cannot be feasibly processed through manual cataloguing. Furthermore, these surveys will capture objects fainter than the night sky, termed low surface brightness objects, and at unprecedented spatial resolution owing to advancements in astronomical imaging. In this thesis, we in-vestigate the use of deep learning to automate cataloguing processes, such as detection, classification and segmentation of objects. A common theme throughout this work is the adaptation of machine learning methods to challenges specific to the domain of low surface brightness imaging.We begin with creating an annotated dataset of structures in low surface brightness images. To facilitate supervised learning in neural networks, a dataset comprised of input and corresponding ground truth target labels is required. An online tool is presented, allowing astronomers to classify and draw over objects in large multi-spectral images. A dataset produced using the tool is then detailed, containing 227 low surface brightness images from the MATLAS survey and labels made by four annotators. We then present a method for synthesising images of galactic cirrus which appear similar to MATLAS images, allowing pretraining of neural networks.A method for integrating sensitivity to orientation in convolutional neural networks is then presented. Objects in astronomical images can present in any given orientation, and thus the ability for neural networks to handle rotations is desirable. We modify con-volutional filters with sets of Gabor filters with different orientations. These orientations are learned alongside network parameters during backpropagation, allowing exact optimal orientations to be captured. The method is validated extensively on multiple datasets and use cases.We propose an attention based neural network architecture to process global contami-nants in large images. Performing analysis of low surface brightness images requires plenty of contextual information and local textual patterns. As a result, a network for processing low surface brightness images should ideally be able to accommodate large high resolu-tion images without compromising on either local or global features. We utilise attention to capture long range dependencies, and propose an efficient attention operator which significantly reduces computational cost, allowing the input of large images. We also use Gabor filters to build an attention mechanism to better capture long range orientational patterns. These techniques are validated on the task of cirrus segmentation in MAT-LAS images, and cloud segmentation on the SWIMSEG database, where state of the art performance is achieved.Following, cirrus segmentation in MATLAS images is further investigated, and a com-prehensive study is performed on the task. We discuss challenges associated with cirrus segmentation and low surface brightness images in general, and present several tech-niques to accommodate them. A novel loss function is proposed to facilitate training of the segmentation model on probabilistic targets. Results are presented on the annotated MATLAS images, with extensive ablation studies and a final benchmark to test the limits of the detailed segmentation pipeline.Finally, we develop a pipeline for multi-class segmentation of galactic structures and surrounding contaminants. Techniques of previous chapters are combined with a popu-lar instance segmentation architecture to create a neural network capable of segmenting localised objects and extended amorphous regions. The process of data preparation for training instance segmentation models is thoroughly detailed. The method is tested on segmentation of five object classes in MATLAS images. We find that unifying the tasks of galactic structure segmentation and contaminant segmentation improves model perfor-mance in comparison to isolating each task