Single-epoch supernova classification with deep convolutional neural networks

Supernovae Type-Ia (SNeIa) play a significant role in exploring the history of the expansion of the Universe, since they are the best-known standard candles with which we can accurately measure the distance to the objects. Finding large samples of SNeIa and investigating their detailed characteristics have become an important issue in cosmology and astronomy. Existing methods relied on a photometric approach that first measures the luminance of supernova candidates precisely and then fits the results to a parametric function of temporal changes in luminance. However, it inevitably requires multi-epoch observations and complex luminance measurements. In this work, we present a novel method for classifying SNeIa simply from single-epoch observation images without any complex measurements, by effectively integrating the state-of-the-art computer vision methodology into the standard photometric approach. Our method first builds a convolutional neural network for estimating the luminance of supernovae from telescope images, and then constructs another neural network for the classification, where the estimated luminance and observation dates are used as features for classification. Both of the neural networks are integrated into a single deep neural network to classify SNeIa directly from observation images. Experimental results show the effectiveness of the proposed method and reveal classification performance comparable to existing photometric methods with multi-epoch observations.Comment: 7 pages, published as a workshop paper in ICDCS2017, in June 201

AI-driven spatio-temporal engine for finding gravitationally lensed supernovae

We present a spatio-temporal AI framework that concurrently exploits both the spatial and time-variable features of gravitationally lensed supernovae in optical images to ultimately aid in the discovery of such exotic transients in wide-field surveys. Our spatio-temporal engine is designed using recurrent convolutional layers, while drawing from recent advances in variational inference to quantify approximate Bayesian uncertainties via a confidence score. Using simulated Young Supernova Experiment (YSE) images as a showcase, we find that the use of time-series images yields a substantial gain of nearly 20 per cent in classification accuracy over single-epoch observations, with a preliminary application to mock observations from the Legacy Survey of Space and Time (LSST) yielding around 99 per cent accuracy. Our innovative deep learning machinery adds an extra dimension in the search for gravitationally lensed supernovae from current and future astrophysical transient surveys.Comment: 6+8 pages, 10 figures, 2 tables. For submission to a peer-reviewed journal. Comments welcom

Pan-chromatic photometric classification of supernovae from multiple surveys and transfer learning for future surveys

Time-domain astronomy is entering a new era as wide-field surveys with higher cadences allow for more discoveries than ever before. The field has seen an increased use of machine learning and deep learning for automated classification of transients into established taxonomies. Training such classifiers requires a large enough and representative training set, which is not guaranteed for new future surveys such as the Vera Rubin Observatory, especially at the beginning of operations. We present the use of Gaussian processes to create a uniform representation of supernova light curves from multiple surveys, obtained through the Open Supernova Catalog for supervised classification with convolutional neural networks. We also investigate the use of transfer learning to classify light curves from the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC) dataset. Using convolutional neural networks to classify the Gaussian process generated representation of supernova light curves from multiple surveys, we achieve an AUC score of 0.859 for classification into Type Ia, Ibc, and II. We find that transfer learning improves the classification accuracy for the most under-represented classes by up to 18% when classifying PLAsTiCC light curves, and is able to achieve an AUC score of 0.945 when including photometric redshifts for classification into six classes (Ia, Iax, Ia-91bg, Ibc, II, SLSN-I). We also investigate the usefulness of transfer learning when there is a limited labelled training set to see how this approach can be used for training classifiers in future surveys at the beginning of operations.Comment: 15 pages, 14 figure

Data-driven Discovery of Transients in the New Era of Time-Domain Astronomy

Time-domain astronomy has reached an incredible new era where unprecedented amounts of data are becoming available. New large-scale astronomical surveys such as the Legacy Survey of Space and Time (LSST) are going to revolutionise transient astronomy, providing opportunities to discover entirely new classes of transients while also enabling a deeper understanding of known classes. LSST is expected to observe over 10 million transient alerts every night, at least two orders of magnitude more than any preceding survey. It has never been more important that astronomers develop fast and automated methods of identifying transient candidates for follow-up observations. In this thesis, I tackle two major challenges facing the future of transient astronomy: the early classification of transients and the detection of rare or previously unknown transients. I detail my development of a number of novel methods dealing with these issues. In the first chapter, I provide an introduction to the field of transient astronomy and motivate why new methods of transient identification are necessary. In the second chapter, I detail the development of a new photometric transient classifier, called RAPID, that is able to automatically classify a range of astronomical transients in real-time. My deep neural network architecture is the first method designed to provide early classifications of astronomical transients. In Chapter 3, I identify the issue that with such large data volumes, the astronomical community will struggle to identify rare and interesting anomalous transients that have previously been found serendipitously. I outline my novel method that uses a Bayesian parametric fit of light curves to identify anomalous transients in real-time. In Chapter 4, I highlight some issues with current photometric classifiers and improve upon RAPID so that it is capable of dealing with real data instead of just simulations. I present classifiers that perform effectively on real data from the Zwicky Transient Facility and the PanSTARRS surveys. Finally, in the last chapter, I discuss the conclusions of my work and highlight some future opportunities and work needed in preparing for discovery in the new era of time-domain astronomy.Cambridge Trust; Cambridge Australia Scholarship

Event classification in MAGIC through Convolutional Neural Networks

The Major Atmospheric Gamma Imaging Cherenkov (MAGIC) telescopes are able to detect gamma rays from the ground with energies beyond several tens of GeV emitted by the most energetic known objects, including Pulsar Wind Nebulae, Active Galactic Nuclei, and Gamma-Ray Bursts. Gamma rays and cosmic rays are detected by imaging the Cherenkov light produced by the charged superluminal leptons in the extended air shower originated when the primary particle interacts with the atmosphere. These Cherenkov flashes brighten the night sky for short times in the nanosecond scale. From the image topology and other observables, gamma rays can be separated from the unwanted cosmic rays, and thereafter incoming direction and energy of the primary gamma rays can be reconstructed. The standard algorithm in MAGIC data analysis for the gamma/hadron separation is the so-called Random Forest, that works on a parametrization of the stereo events based on the shower image parameters. Until a few years ago, these algorithms were limited by the computational resources but modern devices, such as GPUs, make it possible to work efficiently on the pixel maps information. Most neural network applications in the field perform the training on Monte Carlo simulated data for the gamma-ray sample. This choice is prone to systematics arising from discrepancies between observational data and simulations. Instead, in this thesis I trained a known neural network scheme with observation data from a giant flare of the bright TeV blazar Mrk421 observed by MAGIC in 2013. With this method for gamma/hadron separation, the preliminary results compete with the standard MAGIC analysis based on Random Forest classification, which also shows the potential of this approach for further improvement. In this thesis first an introduction to the High-Energy Astrophysics and the Astroparticle physics is given. The cosmic messengers are briefly reviewed, with a focus on the photons, then astronomical sources of γ rays are described, followed by a description of the detection techniques. In the second chapter the MAGIC analysis pipeline starting from the low level data acquisition to the high level data is described. The MAGIC Instrument Response Functions are detailed. Finally, the most important astronomical sources used in the standard MAGIC analysis are listed. The third chapter is devoted to Deep Neural Network techniques, starting from an historical Artificial Intelligence excursus followed by a Machine Learning description. The basic principles behind an Artificial Neural Network and the Convolutional Neural Network used for this work are explained. Last chapter describes my original work, showing in detail the data selection/manipulation for training the Inception Resnet V2 Convolutional Neural Network and the preliminary results obtained from four test sources

Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection

We introduce Deep-HiTS, a rotation invariant convolutional neural network (CNN) model for classifying images of transients candidates into artifacts or real sources for the High cadence Transient Survey (HiTS). CNNs have the advantage of learning the features automatically from the data while achieving high performance. We compare our CNN model against a feature engineering approach using random forests (RF). We show that our CNN significantly outperforms the RF model reducing the error by almost half. Furthermore, for a fixed number of approximately 2,000 allowed false transient candidates per night we are able to reduce the miss-classified real transients by approximately 1/5. To the best of our knowledge, this is the first time CNNs have been used to detect astronomical transient events. Our approach will be very useful when processing images from next generation instruments such as the Large Synoptic Survey Telescope (LSST). We have made all our code and data available to the community for the sake of allowing further developments and comparisons at https://github.com/guille-c/Deep-HiTS

Efficient Deep Learning for Real-time Classification of Astronomical Transients

A new golden age in astronomy is upon us, dominated by data. Large astronomical surveys are broadcasting unprecedented rates of information, demanding machine learning as a critical component in modern scientific pipelines to handle the deluge of data. The upcoming Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory will raise the big-data bar for time- domain astronomy, with an expected 10 million alerts per-night, and generating many petabytes of data over the lifetime of the survey. Fast and efficient classification algorithms that can operate in real-time, yet robustly and accurately, are needed for time-critical events where additional resources can be sought for follow-up analyses. In order to handle such data, state-of-the-art deep learning architectures coupled with tools that leverage modern hardware accelerators are essential. The work contained in this thesis seeks to address the big-data challenges of LSST by proposing novel efficient deep learning architectures for multivariate time-series classification that can provide state-of-the-art classification of astronomical transients at a fraction of the computational costs of other deep learning approaches. This thesis introduces the depthwise-separable convolution and the notion of convolutional embeddings to the task of time-series classification for gains in classification performance that are achieved with far fewer model parameters than similar methods. It also introduces the attention mechanism to time-series classification that improves performance even further still, with significant improvement in computational efficiency, as well as further reduction in model size. Finally, this thesis pioneers the use of modern model compression techniques to the field of photometric classification for efficient deep learning deployment. These insights informed the final architecture which was deployed in a live production machine learning system, demonstrating the capability to operate efficiently and robustly in real-time, at LSST scale and beyond, ready for the new era of data intensive astronomy