Some Pattern Recognition Challenges in Data-Intensive Astronomy

Djorgovski, S. G.; Donalek, C.; Mahabal, A.; Williams, R. D.; Drake, A. J.; Graham, M. J.; Glikman, Eilat

doi:10.1109/icpr.2006.1064

Cited by 10 publications

(8 citation statements)

References 25 publications

(23 reference statements)

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“…Many of the observed phenomena are transient events such as supernovae, gamma-ray bursts, gravitational microlensing events, planetary occultations, stellar flares, accretion flares from supermassive black holes, asteroids, etc. [49]. A key challenge is that the data need to be processed as it streams from the telescopes, comparing it with the previous images of the same parts of the sky, automatically detecting any changes, and classifying and prioritizing the detected events for rapid follow-up observations [50].…”

Section: Machine Learningmentioning

confidence: 99%

“…The system should output a probability of any given event as belonging to any of the possible known classes, or as being unknown. An important requirement is maintaining high level of completeness (do not miss any interesting events) with a low false alarm rate, and the capacity to learn from past experience [49]. The classification must be updated dynamically as more data come in from the telescope and the feedback arrives from the follow-up facilities.…”

Section: Machine Learningmentioning

confidence: 99%

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Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases

Huijse

Estévez

Protopapas

et al. 2014

IEEE Comput. Intell. Mag.

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Time-domain astronomy (TDA) is facing a paradigm shift caused by the exponential growth of the sample size, data complexity and data generation rates of new astronomical sky surveys. For example, the Large Synoptic Survey Telescope (LSST), which will begin operations in northern Chile in 2022, will generate a nearly 150 Petabyte imaging dataset of the southern hemisphere sky. The LSST will stream data at rates of 2 Terabytes per hour, effectively capturing an unprecedented movie of the sky.The LSST is expected not only to improve our understanding of time-varying astrophysical objects, but also to reveal a plethora of yet unknown faint and fast-varying phenomena. To cope with a change of paradigm to data-driven astronomy, the fields of astroinformatics and astrostatistics have been created recently. The new data-oriented paradigms for astronomy combine statistics, data mining, knowledge discovery, machine learning and computational intelligence, in order to provide the automated and robust methods needed for the rapid detection and classification of known astrophysical objects as well as the unsupervised characterization of novel phenomena. In this article we present an overview of machine learning and computational intelligence applications to TDA. Future big data challenges and new lines of research in TDA, focusing on the LSST, are identified and discussed from the viewpoint of computational intelligence/machine learning. Interdisciplinary collaboration will be required to cope with the challenges posed by the deluge of astronomical data coming from the LSST.

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Section: Machine Learningmentioning

confidence: 99%

Section: Machine Learningmentioning

confidence: 99%

Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases

Huijse

Estévez

Protopapas

et al. 2014

IEEE Comput. Intell. Mag.

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“…In what follows we shall focus on some recent developments in the field of astronomical Data Mining (hereafter DM) or Knowledge Discovery in Databases (KDD). Some early reviews of the topic inclide, e.g., [3,4,31,32,33,34,35,36,37].…”

Section: Introductionmentioning

confidence: 99%

Extracting Knowledge from Massive Astronomical Data Sets

Brescia

Cavuoti

Djorgovski

et al. 2012

Astrostatistics and Data Mining

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The exponential growth of astronomical data collected by both ground based and space borne instruments has fostered the growth of Astroinformatics: a new discipline laying at the intersection between astronomy, applied computer science, and information and computation (ICT) technologies. At the very heart of Astroinformatics is a complex set of methodologies usually called Data Mining (DM) or Knowledge Discovery in Data Bases (KDD). In the astronomical domain, DM/KDD are still in a very early usage stage, even though new methods and tools are being continuously deployed in order to cope with the Massive Data Sets (MDS) that can only grow in the future. In this paper, we briefly outline some general problems encountered when applying DM/KDD methods to astrophysical problems, and describe the DAME (DAta Mining & Exploration) web application. While specifically tailored to work on MDS, DAME can be effectively applied also to smaller data sets. As an illustration, we describe two application of DAME to two different problems: the identification of candidate globular clusters in external galaxies, and the classification of active galactic nuclei (AGN). We believe that tools and services of this nature will become increasingly necessary for the data-intensive astronomy (and indeed all sciences) in the 21 st century.

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“…More complicated, are the problems related to image or speech recognition where the input data may not exactly match the stored data but have some level of similarity 2 . The latter makes realtime processing using a general type processor tremendously difficult or even impossible for large data sets 3 . Holographic data processing is one of the possible solutions, which has been extensively studied in optics during the past five decades 4 .…”

mentioning

confidence: 99%

Pattern recognition with magnonic holographic memory device

Kozhevnikov

Gertz

Dudko

et al. 2015

Applied Physics Letters

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Abstract:In this work, we present experimental data demonstrating the possibility of using magnonic holographic devices for pattern recognition. The prototype eight-terminal device consists of a magnetic matrix with micro-antennas placed on the periphery of the matrix to excite and detect spin waves. The principle of operation is based on the effect of spin wave interference, which is similar to the operation of optical holographic devices. Input information is encoded in the phases of the spin waves generated on the several edges of the magnonic matrix, while the output corresponds to the amplitude of the inductive voltage produced by the interfering spin waves on the other side of the matrix. The level of the output voltage depends on the combination of the input phases as well as on the internal structure of the magnonic matrix. Experimental data collected for several magnonic matrixes show the unique output signatures in which maxima and minima correspond to specific input phase patterns. Potentially, magnonic holographic devices may provide a higher storage density compare to the optical counterparts due to a shorter wavelength and compatibility with conventional electronic devices. The challenges and shortcoming of the magnonic holographic devices are also discussed.

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Some Pattern Recognition Challenges in Data-Intensive Astronomy

Cited by 10 publications

References 25 publications

Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases

Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases

Extracting Knowledge from Massive Astronomical Data Sets

Pattern recognition with magnonic holographic memory device

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