Distant galaxies analysis with deep neural networks

In this work we face a very common problem in Astrophysics. One of the first parameters to obtain from a galaxy spectrum is the redshift. The redshift at which a galaxy is, can tell us a lot of things about the large scale structure of the universe. However, the telescope time is limited, and it would take a lot of time to survey the whole sky observing the spectrum of galaxies. This is the reason why surveys using narrowband photometry (for example ALHAMBRA or JPAS) are arising. These surveys allow to observe a large number of galaxies in much less time than using spectroscopy, thus making astronomers able to disentangle the structure of the Universe and the features of very distant galaxies. Traditionally, the features have been derived using the technique known as SED-fitting, which consists in deriving the features of the galaxy from its spectrum. This is not an easy problem, not only because of the large number of variables in play (velocity, velocity dispersion, age and metallicity for each single stellar population, or SSP), but because of the degeneracies. A degeneracy happens when two different SSPs show almost undistinguishable spectra. For example, a degeneracy exists between age and metallicity, with and old and metal-rich1 SSPs showing similar spectrum to that of a young and metal-poor SSPs. In this Master Thesis we evaluate the ability of Deep Neural Networks, using as input the observations of a galaxy, to obtain the parameters of the galaxy (redshift, mass and galaxy type).En este trabajo vamos a afrontar un problema habitual en astrofísica. Uno de los primeros parámetros a medir en el espectro de una galaxia es el redshift. El desplazamiento al rojo de una galaxia puede dar mucha información acerca de la estructura a gran escala del universo. Sin embargo, el tiempo de telescopio es limitado, y llevaría mucho tiempo observar todo el cielo obteniendo el espectro de las galaxias. Por ello están proliferando los catálogos de observaciones basados en fotometría de banda estrecha (por ejemplo, ALHAMBRA o JPAS). Estos catálogos permiten observar un gran número de galaxias en mucho menos tiempo que usando espectroscopía, permitiendo a los astrónomos desentrañar la estructura del Universo a gran escala y pudiendo medir las características de las galaxias más lejanas. Tradicionalmente, las características de las galaxias se ha obtenido usando una técnica conocida como SED-fitting o ajuste espectral. Esta técnica consiste en ajustar un espectro a las observaciones fotométricas, permitiendo obtener las características de la galaxia. Este problema no es sencillo, no solo por la gran cantidad de variables involucradas, sino también por las degeneraciones existentes. Una degeneración ocurre cuando dos poblaciones estelares simples (SSP) tienen espectros prácticamente indistinguibles a pesar de que sus parámetros son completamente diferentes. Es ampliamente conocida, por ejemplo, las degeneraciones existentes entre la edad y la metalicidad, por la que una galaxia vieja y rica en metales 2 tiene un espectro muy parecido al de una galaxia joven pobre en metales. En este trabajo evaluaremos la capacidad de Redes Neuronales Profundas, usando como entrada las observaciones de una galaxia, obtener los parámetros fundamentales de dicha galaxia (desplazamiento al rojo, masa, y tipo de galaxia).En aquest treball afrontarem un problema habitual a l'astrofísica. Un dels primers paràmetres a mesurar en l'espectre d'una galàxia és el redshift. Aquest pot donar molta informació sobre l'estructura a gran escala de l'univers. No obstant això, el temps de telescopi és limitat, i porta molt de temps observar tot el cel obtenint l'espectre de les galàxies. Per això estan proliferant els catàlegs d'observacions basats en fotometria de banda estreta (per exemple, ALHAMBRA o JPAS). Aquests catàlegs permeten observar un gran nombre de galàxies en molt menys temps que usant espectroscòpia, permetent als astrònoms desentranyar l'estructura de l'Univers a gran escala i podent mesurar les característiques de les galàxies més llunyanes. Tradicionalment, les característiques de les galàxies s'han obtingut usant una tècnica coneguda com SED-fitting o ajust espectral. Aquesta tècnica consisteix a ajustar un espectre a les observacions fotomètriques, permetent obtenir les característiques de la galàxia. Aquest problema no és senzill, no només per la gran quantitat de variables involucrades, sinó també per les degeneracions existents. Una degeneració ocorre quan dues poblacions estel·lars simples (SSP) tenen espectres pràcticament indistingibles tot i que els seus paràmetres són completament diferents. És àmpliament coneguda, per exemple, les degeneracions existents entre l'edat i la metal·licitat, per la qual una galàxia vella i rica en metalls 2 té un espectre molt semblant al d'una galàxia jove pobre en metalls. En aquest treball avaluarem la capacitat de xarxes neuronals profundes, usant com a entrada les observacions d'una galàxia, obtenint els paràmetres fonamentals d'aquesta galàxia (redshift, massa i tipus de galàxia)

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

Classification of multiwavelength transients with machine learning

With the advent of powerful telescopes such as the Square Kilometre Array (SKA), its precursor MeerKAT and the Large Synoptic Survey Telescope (LSST), we are entering a golden era of multiwavelength transient astronomy. The large MeerKAT science project ThunderKAT may dramatically increase the detected number of radio transients. Currently radio transient datasets are still very small, allowing spectroscopic classification of all objects of interest. As the event rate increases, follow-up resources must be prioritised by making use of early classification of the radio data. Machine learning algorithms have proven themselves invaluable in the context of optical astronomy, however it has yet to be applied to radio transients. In the burgeoning era of multimessenger astronomy, incorporating data from different telescopes such as MeerLICHT, Fermi, LSST and the gravitational wave observatory LIGO could significantly improve classification of events. Here we present MALT (Machine Learning for Transients): a general machine learning pipeline for multiwavelength transient classification. In order to make use of most machine learning algorithms, "features" must be extracted from complex and often high dimensional datasets. In our approach, we first interpolate the data onto a uniform grid using Gaussian processes, we then perform a wavelet decomposition and finally reduce the dimensionality using principal component analysis. We then classify the light curves with the popular machine learning algorithm random forests. For the first time, we apply machine learning to the classification of radio transients. Unfortunately publicly available radio transient data is scarce and our dataset consists of just 87 light curves, with several classes only consisting of a single example. However machine learning is often applied to such small datasets by making use of data augmentation. We develop a novel data augmentation technique based on Gaussian processes, able to generate new data statistically consistent with the original. As the dataset is currently small, three studies were done on the effect of the training set. The classifier was trained on a non-representative training set, achieving an overall accuracy of 77.8% over all 11 classes with the known 87 lightcurves with just eight hours of observations. The expected increase in performance, as more training data are acquired, is shown by training the classifier on a simulated representative training set, achieving an average accuracy of 95.8% across all 11 classes. Finally, the effectiveness of including multiwavelength data for general transient classification is demonstrated. First the classifier is trained on wavelet features and a contextual feature, achieving an average accuracy of 72.9%. The classifier was then trained on wavelet features and a contextual feature, together with a single optical flux feature. This addition improves the overall accuracy to 94.7%. This work provides a general approach for multiwavelength transient classification and shows that machine learning can be highly effective at classifying the influx of radio transients anticipated with MeerKAT and other radio telescopes

Foreground challenge to CMB polarization: present methodologies and new concepts

In this thesis, I focus on the issue of contamination to the polarization of the Cosmic Microwave Background (CMB) anisotropies from diffuse Galactic foregrounds, which is known to be one of the greatest challenges to the detection of the curl (B) modes of the signal, which might be sourced by cosmological gravitational waves. I take parallel approaches along these lines. I apply the most recent techniques capable of parametrizing, fitting, and removing the main known Galactic foregrounds in a multi-frequency CMB dataset to one of the forthcoming powerful CMB polarization experiments, the Large Scale Polarization Explorer (LSPE). I presented the result of the complete simulation done for the parametric component separation pipeline of this experiment. On the other hand, I explored the latest Machine Learning and Artificial Intelligence algorithms and their application in CMB data analysis, specifically component separation and foreground cleaning. I start the investigation of the relevance of Neural Networks (NNs) in the understanding of the physical properties of foregrounds, as it is necessary before the foreground removal layer, by implementing a novel algorithm, which I test on simulated data from future B-mode probes. The results of the implemented NN\u2019s prediction in discerning the correct foreground model address the high accuracy and suitability of this model as a preceding stage for the component separation procedure. Finally, I also investigate how different NNs, as a generative model, could be used for reconstructing CMB anisotropies where the removal is impossible, and data have to be abandoned in the analysis. Lots remain to be done along each of these three investigations, which have been published in scientific journals, and constitute the basis of my future research

Characterising & classifying the local population of ultracool dwarfs with Gaia DR2 and EDR3

Ultracool dwarfs (UCDs) are the lowest mass products of star formation and span the end of the stellar main sequence from very-low mass, hydrogen-burning M stars to the coolest brown dwarfs. In this thesis we characterise and classify the ultracool dwarf population in the solar neighbourhood using the accuracy and precision of data from the Gaia space observatory. Combining astrometric (in particular parallax) and photometric data from Gaia DR2 and EDR3 with photometry from UKIDSS, SDSS and 2MASS, we prepare some of the largest and most accurate, near-100% complete volume-limited populations of nearby, field late-M, L and T dwarfs. From these samples we derive key population characteristics such as colour-absolute magnitude relationships, the stellar luminosity function, the binary fraction and the binary mass ratio. Our statistical-based approach differs from much of the UCD literature to date which seeks to prepare meta-catalogues from disparate surveys and individual spectroscopic observations with distance determined by indirect methods. Our approach offers improvements in scale, completeness, and distance accuracy. In particular we use Gaia to update the colour-magnitude relations and derive the stellar luminosity functions in MJ and MG of the UCDs. We calculate the binary fraction of the late-M and early-L dwarfs as a function of spectral type by carefully modelling the over-luminous unresolved binary population and show that late-M dwarf binaries reside almost exclusively in equal-mass pairs or twins. Given the complex spectral features of UCDs, consistent and accurate classification is challenging. We investigate the current traditional methods of classification and evaluate a range of alternative techniques including supervised and unsupervised machine learning. In a separate study we use Gaia data to prepare a large, cylindrical sample of FGK main sequence dwarf stars to calculate the structure of the vertical density distribution close to the galactic plane, in fine detail, as a function of colour. Using our derived colour-dependent thin disk scale height we directly determine the star formation history of the solar neighbourhood by modelling the evolution of stellar populations using state-of-the-art PARSEC isochrones.Open Acces

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

IberSPEECH 2020: XI Jornadas en Tecnología del Habla and VII Iberian SLTech

IberSPEECH2020 is a two-day event, bringing together the best researchers and practitioners in speech and language technologies in Iberian languages to promote interaction and discussion. The organizing committee has planned a wide variety of scientific and social activities, including technical paper presentations, keynote lectures, presentation of projects, laboratories activities, recent PhD thesis, discussion panels, a round table, and awards to the best thesis and papers. The program of IberSPEECH2020 includes a total of 32 contributions that will be presented distributed among 5 oral sessions, a PhD session, and a projects session. To ensure the quality of all the contributions, each submitted paper was reviewed by three members of the scientific review committee. All the papers in the conference will be accessible through the International Speech Communication Association (ISCA) Online Archive. Paper selection was based on the scores and comments provided by the scientific review committee, which includes 73 researchers from different institutions (mainly from Spain and Portugal, but also from France, Germany, Brazil, Iran, Greece, Hungary, Czech Republic, Ucrania, Slovenia). Furthermore, it is confirmed to publish an extension of selected papers as a special issue of the Journal of Applied Sciences, “IberSPEECH 2020: Speech and Language Technologies for Iberian Languages”, published by MDPI with fully open access. In addition to regular paper sessions, the IberSPEECH2020 scientific program features the following activities: the ALBAYZIN evaluation challenge session.Red Española de Tecnologías del Habla. Universidad de Valladoli

Discovery in Physics

Volume 2 covers knowledge discovery in particle and astroparticle physics. Instruments gather petabytes of data and machine learning is used to process the vast amounts of data and to detect relevant examples efficiently. The physical knowledge is encoded in simulations used to train the machine learning models. The interpretation of the learned models serves to expand the physical knowledge resulting in a cycle of theory enhancement