bluuebunny/crossref_metadata_2025_split huggingface dataset:
Astrophysics and Big Data: Challenges, Methods, and Tools
Authors: Garofalo Mauro , Botta Alessio , Ventre Giorgio 
Published: May 2017

AbstractNowadays there is no field research which is not flooded with data. Among the sciences, astrophysics has always been driven by the analysis of massive amounts of data. The development of new and more sophisticated observation facilities, both ground-based and spaceborne, has led data more and more complex (Variety), an exponential growth of both data Volume (i.e., in the order of petabytes), and Velocity in terms of production and transmission. Therefore, new and advanced processing solutions will be needed to process this huge amount of data. We investigate some of these solutions, based on machine learning models as well as tools and architectures for Big Data analysis that can be exploited in the astrophysical context.
Astroinformatics Challenges from Next-generation Radio Continuum Surveys
Authors: Norris Ray P. 
Published: May 2017

AbstractThe tens of millions of radio sources to be detected with next-generation surveys pose new challenges, quite apart from the obvious ones of processing speed and data volumes. For example, existing algorithms are inadequate for source extraction or cross-matching radio and optical/IR sources, and a new generation of algorithms are needed using machine learning and other techniques. The large numbers of sources enable new ways of testing astrophysical models, using a variety of “large-n astronomy” techniques such as statistical redshifts. Furthermore, while unexpected discoveries account for some of the most significant discoveries in astronomy, it will be difficult to discover the unexpected in large volumes of data, unless specific software is developed to mine the data for the unexpected.
Exploring the spectroscopic diversity of type Ia supernovae with Deep Learning and Unsupervised Clustering
Authors: Ishida Emille E. O. , Sasdelli Michele , Vilalta Ricardo , Aguena Michel , Busti Vinicius C. , Camacho Hugo , Trindade Arlindo M. M. , Gieseke Fabian , de Souza Rafael S. , Fantaye Yabebal T. , Mazzali Paolo A. 
Published: May 2017

AbstractThe existence of multiple subclasses of type Ia supernovae (SNeIa) has been the subject of great debate in the last decade. In this work, we show how machine learning tools facilitate identification of subtypes of SNe Ia. Using Deep Learning for dimensionality reduction, we were capable of performing such identification in a parameter space of significantly lower dimension than its principal component analysis counterpart. This is evidence that the progenitor system and the explosion mechanism can be described with a small number of initial physical parameters. All tools used here are publicly available in the Python package DRACULA (Dimensionality Reduction And Clustering for Unsupervised Learning in Astronomy) and can be found within COINtoolbox (https://github.com/COINtoolbox/DRACULA).
An Effective Method for Modeling Two-dimensional Sky Background of LAMOST
Authors: Haerken Hasitieer , Duan Fuqing , Zhang Jiannan , Guo Ping 
Published: May 2017

AbstractEach CCD of LAMOST accommodates 250 spectra, while about 40 are used to observe sky background during real observations. How to estimate the unknown sky background information hidden in the observed 210 celestial spectra by using the known 40 sky spectra is the problem we solve. In order to model the sky background, usually a pre-observation is performed with all fibers observing sky background. We use the observed 250 skylight spectra as training data, where those observed by the 40 fibers are considered as a base vector set. The Locality-constrained Linear Coding (LLC) technique is utilized to represent the skylight spectra observed by the 210 fibers with the base vector set. We also segment each spectrum into small parts, and establish the local sky background model for each part. Experimental results validate the proposed method, and show the local model is better than the global model.
The Hubble Source Catalog
Authors: Lubow Stephen H. 
Published: May 2017

AbstractThe Hubble Source Catalog (HSC) is designed to enhance the science obtained from the Hubble Space Telescope by combining the tens of thousands of visit-based source lists in the Hubble Legacy Archive (HLA) across filters and detectors into a single master catalog. The catalog contains data from the major Hubble imaging instruments: Wide Field Planetary Camera 2 (WFPC2), Advanced Camera for Surveys (ACS), and Wide Field Camera 3 (WFC3). It is based on cross matching and astrometry algorithms developed by Budavari & Lubow (2012). We recently released Version 2 that is three times the size of Version 1 and includes some new features. The catalog can be accessed through a variety of interfaces (see http://archive.stsci.edu/hst/hsc/). The HSC provides descriptions of astronomical objects involving multiple wavelengths and epochs. High relative positional accuracy of objects is achieved across the Hubble images, often with sub-pixel precision of a few milliarcseconds.
AlertSim - Serbian Contribution to the LSST
Authors: Jevremović Darko , Vujčić Veljko , Srećković Vladimir A. , Aleksić Jovan , Erkapić Sanja , Milovanović Nenad 
Published: May 2017

AbstractWe present simulator of alerts for the Large Synoptic Survey Telescope (LSST) developed by Belgrade group. This simulator will be used in testing the functionality of external event brokers/Complex Event Processing (CEP) engines. It is based on current LSST Simulation framework and allows for different classes of objects to be ‘alerted’. A Web service based on our simulator is prototyped and can be accessed by developers of brokers/CEP engines.
C3: A Command-line Catalogue Cross-matching tool for modern astrophysical survey data
Authors: Riccio Giuseppe , Brescia Massimo , Cavuoti Stefano , Mercurio Amata , Di Giorgio Anna Maria , Molinari Sergio 
Published: May 2017

AbstractIn the current data-driven science era, it is needed that data analysis techniques has to quickly evolve to face with data whose dimensions has increased up to the Petabyte scale. In particular, being modern astrophysics based on multi-wavelength data organized into large catalogues, it is crucial that the astronomical catalog cross-matching methods, strongly dependant from the catalogues size, must ensure efficiency, reliability and scalability. Furthermore, multi-band data are archived and reduced in different ways, so that the resulting catalogues may differ each other in formats, resolution, data structure, etc, thus requiring the highest generality of cross-matching features. We presentC3(Command-line Catalogue Cross-match), a multi-platform application designed to efficiently cross-match massive catalogues from modern surveys. Conceived as a stand-alone command-line process or a module within generic data reduction/analysis pipeline, it provides the maximum flexibility, in terms of portability, configuration, coordinates and cross-matching types, ensuring high performance capabilities by using a multi-core parallel processing paradigm and a sky partitioning algorithm.
Everything we’d like to do with LSST data, but we don’t know (yet) how
Authors: Ivezić Željko , Connolly Andrew J. , Jurić Mario 
Published: May 2017

AbstractThe Large Synoptic Survey Telescope (LSST), the next-generation optical imaging survey sited at Cerro Pachon in Chile, will provide an unprecedented database of astronomical measurements. The LSST design, with an 8.4m (6.7m effective) primary mirror, a 9.6 sq. deg. field of view, and a 3.2 Gigapixel camera, will allow about 10,000 sq. deg. of sky to be covered twice per night, every three to four nights on average, with typical 5-sigma depth for point sources of r=24.5 (AB). With over 800 observations in ugrizy bands over a 10-year period, these data will enable a deep stack reaching r=27.5 (about 5 magnitudes deeper than SDSS) and faint time-domain astronomy. The measured properties of newly discovered and known astrometric and photometric transients will be publicly reported within 60 sec after observation. The vast database of about 30 trillion observations of 40 billion objects will be mined for the unexpected and used for precision experiments in astrophysics. In addition to a brief introduction to LSST, we discuss a number of astro-statistical challenges that need to be overcome to extract maximum information and science results from LSST dataset.
An Automated Galaxy Spectra Recognition Method Basing on Spectral Lines Information
Authors: Zhang Jiannan , Chen Xiaoyan , Wu Yue , Li Xiangru 
Published: May 2017

AbstractFor the vast amounts of spectra produced by LAMOST, the pipeline basing on PCAZ method is limited by the bad flux calibration and low S/N data. This work focuses on the study of the efficient recognition methods of galaxy spectra of LAMOST basing on spectral lines information. The new method searches spectral lines and extracts the information of spectral lines (position, height, and width et al.) automatically. Using the spectral lines information which are less influenced by the quality of flux calibration and the S/N ratio, galaxy spectra are recognized with the redshift measured through spectral lines matching method. The experiment verified it is feasible for the LAMOST galaxy spectra: the correct recognition rate > 80% for the data with SNR_g > 5, and > 90% for the data with SNR_r > 5. Compared with the redshift of SDSS, the systematic error of our method is 0, and the standard deviation of the error is 0.0002.
Identification of Interesting Objects in Large Spectral Surveys Using Highly Parallelized Machine Learning
Authors: Škoda Petr , Palička Andrej , Koza Jakub , Shakurova Ksenia 
Published: May 2017

AbstractThe current archives of LAMOST multi-object spectrograph contain millions of fully reduced spectra, from which the automatic pipelines have produced catalogues of many parameters of individual objects, including their approximate spectral classification. This is, however, mostly based on the global shape of the whole spectrum and on integral properties of spectra in given bandpasses, namely presence and equivalent width of prominent spectral lines, while for identification of some interesting object types (e.g. Be stars or quasars) the detailed shape of only a few lines is crucial. Here the machine learning is bringing a new methodology capable of improving the reliability of classification of such objects even in boundary cases.We present results of Spark-based semi-supervised machine learning of LAMOST spectra attempting to automatically identify the single and double-peak emission of Hα line typical for Be and B[e] stars. The labelled sample was obtained from archive of 2m Perek telescope at Ondřejov observatory. A simple physical model of spectrograph resolution was used in domain adaptation to LAMOST training domain. The resulting list of candidates contains dozens of Be stars (some are likely yet unknown), but also a bunch of interesting objects resembling spectra of quasars and even blazars, as well as many instrumental artefacts. The verification of a nature of interesting candidates benefited considerably from cross-matching and visualisation in the Virtual Observatory environment.
Detection of quasars in the time domain
Authors: Graham Matthew J. , Djorgovski S. G. , Stern Daniel J. , Drake Andrew , Mahabal Ashish 
Published: May 2017

AbstractThe time domain is the emerging forefront of astronomical research with new facilities and instruments providing unprecedented amounts of data on the temporal behavior of astrophysical populations. Dealing with the size and complexity of this requires new techniques and methodologies. Quasars are an ideal work set for developing and applying these: they vary in a detectable but not easily quantifiable manner whose physical origins are poorly understood. In this paper, we will review how quasars are identified by their variability and how these techniques can be improved, what physical insights into their variability can be gained from studying extreme examples of variability, and what approaches can be taken to increase the number of quasars known. These will demonstrate how astroinformatics is essential to discovering and understanding this important population.
Deep data: discovery and visualization Application to hyperspectral ALMA imagery
Authors: Merényi Erzsébet , Taylor Joshua , Isella Andrea 
Published: May 2017

AbstractLeading-edge telescopes such as the Atacama Large Millimeter and sub-millimeter Array (ALMA), and near-future ones, are capable of imaging the same sky area at hundreds-to-thousands of frequencies with both high spectral and spatial resolution. This provides unprecedented opportunities for discovery about the spatial, kinematical and compositional structure of sources such as molecular clouds or protoplanetary disks, and more. However, in addition to enormous volume, the data also exhibit unprecedented complexity, mandating new approaches for extracting and summarizing relevant information. Traditional techniques such as examining images at selected frequencies become intractable while tools that integrate data across frequencies or pixels (like moment maps) can no longer fully exploit and visualize the rich information. We present a neural map-based machine learning approach that can handle all spectral channels simultaneously, utilizing the full depth of these data for discovery and visualization of spectrally homogeneous spatial regions (spectral clusters) that characterize distinct kinematic behaviors. We demonstrate the effectiveness on an ALMA image cube of the protoplanetary disk HD142527. The tools we collectively name “NeuroScope” are efficient for “Big Data” due to intelligent data summarization that results in significant sparsity and noise reduction. We also demonstrate a new approach to automate our clustering for fast distillation of large data cubes.
Supercomputer simulations of structure formation in the Universe
Authors: Ishiyama Tomoaki 
Published: May 2017

AbstractWe describe the implementation and performance results of our massively parallel MPI†/OpenMP‡ hybrid TreePM code for large-scale cosmological N-body simulations. For domain decomposition, a recursive multi-section algorithm is used and the size of domains are automatically set so that the total calculation time is the same for all processes. We developed a highly-tuned gravity kernel for short-range forces, and a novel communication algorithm for long-range forces. For two trillion particles benchmark simulation, the average performance on the fullsystem of K computer (82,944 nodes, the total number of core is 663,552) is 5.8 Pflops, which corresponds to 55% of the peak speed.
Deep learning for studies of galaxy morphology
Authors: Tuccillo D. , Huertas-Company M. , Decencière E. , Velasco-Forero S. 
Published: May 2017

AbstractEstablishing accurate morphological measurements of galaxies in a reasonable amount of time for future big-data surveys such as EUCLID, the Large Synoptic Survey Telescope or the Wide Field Infrared Survey Telescope is a challenge. Because of its high level of abstraction with little human intervention, deep learning appears to be a promising approach. Deep learning is a rapidly growing discipline that models high-level patterns in data as complex multilayered networks. In this work we test the ability of deep convolutional networks to provide parametric properties of Hubble Space Telescope like galaxies (half-light radii, Sérsic indices, total flux etc..). We simulate a set of galaxies including point spread function and realistic noise from the CANDELS survey and try to recover the main galaxy parameters using deep-learning. We compare the results with the ones obtained with the commonly used profile fitting based software GALFIT. This way showing that with our method we obtain results at least equally good as the ones obtained with GALFIT but, once trained, with a factor 5 hundred time faster.
Cooperative photometric redshift estimation
Authors: Cavuoti S. , Tortora C. , Brescia M. , Longo G. , Radovich M. , Napolitano N. R. , Amaro V. , Vellucci C. 
Published: May 2017

AbstractIn the modern galaxy surveys photometric redshifts play a central role in a broad range of studies, from gravitational lensing and dark matter distribution to galaxy evolution. Using a dataset of ~ 25,000 galaxies from the second data release of the Kilo Degree Survey (KiDS) we obtain photometric redshifts with five different methods: (i) Random forest, (ii) Multi Layer Perceptron with Quasi Newton Algorithm, (iii) Multi Layer Perceptron with an optimization network based on the Levenberg-Marquardt learning rule, (iv) the Bayesian Photometric Redshift model (or BPZ) and (v) a classical SED template fitting procedure (Le Phare). We show how SED fitting techniques could provide useful information on the galaxy spectral type which can be used to improve the capability of machine learning methods constraining systematic errors and reduce the occurrence of catastrophic outliers. We use such classification to train specialized regression estimators, by demonstrating that such hybrid approach, involving SED fitting and machine learning in a single collaborative framework, is capable to improve the overall prediction accuracy of photometric redshifts.
The Euclid Data Processing Challenges
Authors: Dubath Pierre , Apostolakos Nikolaos , Bonchi Andrea , Belikov Andrey , Brescia Massimo , Cavuoti Stefano , Capak Peter , Coupon Jean , Dabin Christophe , Degaudenzi Hubert , Desai Shantanu , Dubath Florian , Fontana Adriano , Fotopoulou Sotiria , Frailis Marco , Galametz Audrey , Hoar John , Holliman Mark , Hoyle Ben , Hudelot Patrick , Ilbert Olivier , Kuemmel Martin , Melchior Martin , Mellier Yannick , Mohr Joe , Morisset Nicolas , Paltani Stéphane , Pello Roser , Pilo Stefano , Polenta Gianluca , Poncet Maurice , Saglia Roberto , Salvato Mara , Sauvage Marc , Schefer Marc , Serrano Santiago , Soldati Marco , Tramacere Andrea , Williams Rees , Zacchei Andrea 
Published: May 2017

AbstractEuclid is a Europe-led cosmology space mission dedicated to a visible and near infrared survey of the entire extra-galactic sky. Its purpose is to deepen our knowledge of the dark content of our Universe. After an overview of the Euclid mission and science, this contribution describes how the community is getting organized to face the data analysis challenges, both in software development and in operational data processing matters. It ends with a more specific account of some of the main contributions of the Swiss Science Data Center (SDC-CH).
Hierarchical Matching and Regression with Application to Photometric Redshift Estimation
Authors: Murtagh Fionn 
Published: May 2017

AbstractThis work emphasizes that heterogeneity, diversity, discontinuity, and discreteness in data is to be exploited in classification and regression problems. A global a priori model may not be desirable. For data analytics in cosmology, this is motivated by the variety of cosmological objects such as elliptical, spiral, active, and merging galaxies at a wide range of redshifts. Our aim is matching and similarity-based analytics that takes account of discrete relationships in the data. The information structure of the data is represented by a hierarchy or tree where the branch structure, rather than just the proximity, is important. The representation is related to p-adic number theory. The clustering or binning of the data values, related to the precision of the measurements, has a central role in this methodology. If used for regression, our approach is a method of cluster-wise regression, generalizing nearest neighbour regression. Both to exemplify this analytics approach, and to demonstrate computational benefits, we address the well-known photometric redshift or ‘photo-z’ problem, seeking to match Sloan Digital Sky Survey (SDSS) spectroscopic and photometric redshifts.
METAPHOR: Probability density estimation for machine learning based photometric redshifts
Authors: Amaro V. , Cavuoti S. , Brescia M. , Vellucci C. , Tortora C. , Longo G. 
Published: May 2017

AbstractWe present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z’s and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF’s derived from a traditional SED template fitting method (Le Phare).
Morphogenesis of liquid crystal topological defects during the nematic-smectic A phase transition
Authors: Gim Min-Jun , Beller Daniel A. , Yoon Dong Ki 
Published: May 2017

AbstractThe liquid crystalline phases of matter each possess distinct types of defects that have drawn great interest in areas such as topology, self-assembly and material micropatterning. However, relatively little is known about how defects in one liquid crystalline phase arise from defects or deformations in another phase upon crossing a phase transition. Here, we directly examine defects in the in situ thermal phase transition from nematic to smectic A in hybrid-aligned liquid crystal droplets on water substrates, using experimental, theoretical and numerical analyses. The hybrid-aligned nematic droplet spontaneously generates boojum defects. During cooling, toric focal conic domains arise through a sequence of morphological transformations involving nematic stripes and locally aligned focal conic domains. This simple experiment reveals a surprisingly complex pathway by which very different types of defects may be related across the nematic–smectic A phase transition, and presents new possibilities for controlled deformation and patterning of liquid crystals.
Leukocyte integrin Mac-1 regulates thrombosis via interaction with platelet GPIbα
Authors: Wang Yunmei , Gao Huiyun , Shi Can , Erhardt Paul W. , Pavlovsky Alexander , A. Soloviev Dmitry , Bledzka Kamila , Ustinov Valentin , Zhu Liang , Qin Jun , Munday Adam D. , Lopez Jose , Plow Edward , Simon Daniel I. 
Published: May 2017

AbstractInflammation and thrombosis occur together in many diseases. The leukocyte integrin Mac-1 (also known as integrin αMβ2, or CD11b/CD18) is crucial for leukocyte recruitment to the endothelium, and Mac-1 engagement of platelet GPIbα is required for injury responses in diverse disease models. However, the role of Mac-1 in thrombosis is undefined. Here we report that mice with Mac-1 deficiency (Mac-1−/−) or mutation of the Mac-1-binding site for GPIbα have delayed thrombosis after carotid artery and cremaster microvascular injury without affecting parameters of haemostasis. Adoptive wild-type leukocyte transfer rescues the thrombosis defect in Mac-1−/− mice, and Mac-1-dependent regulation of the transcription factor Foxp1 contributes to thrombosis as evidenced by delayed thrombosis in mice with monocyte-/macrophage-specific overexpression of Foxp1. Antibody and small-molecule targeting of Mac-1:GPIbα inhibits thrombosis. Our data identify a new pathway of thrombosis involving leukocyte Mac-1 and platelet GPIbα, and suggest that targeting this interaction has anti-thrombotic therapeutic potential with reduced bleeding risk.