Symposium S14  29 – 30 June 2017

Aims and scope

Analysis and accurate interpretation of large and high dimensional data-sets is becoming increasingly important throughout all scientific branches covering scales ranging from the microscopic world of particle physics, to mega-scales of cosmology. Astronomy has experienced a data deluge growing from Terabytes to Petabytes. Besides the volume it is the complexity, and diversity, of the data that bring interesting challenges that border the fields of mathematics, statistics, computer science in general, and machine learning in particular. In astronomy we can boast of datasets not only of billions of rows, but also hundreds of columns. The extra features and measurements bring opportunities to discover newer correlations but also increase computational complexities if not handled properly. Astronomy often leads the way in posing problems and solving them with its data that are heteroscedastic, sparse, and span orders of magnitude in brightness over the entire electromagnetic range.

From current surveys like Kepler, Gaia, OGLE, CRTS, PTF, Pan-STARRS, to the era of ZTF, LSST, SKA, BlackGem, Euclid, WFIRST, we will face newer challenges of converting the huge datasets to actionable knowledge - choosing a few objects that need to be followed up right away (the real-time aspect), and understanding entire families and sub-families that likely lurk in the dataset (the archival aspect).

Time-Domain Astronomy has taken us from static snapshots to digital panoramic cinematography of the universe. The resulting complex and rich data require parallel and rapid computation before the most interesting phenomena fade to oblivion. Rapid decisions and follow up are required to push the boundaries of our understanding.

Owing to the differences in wavelengths/filters, aperture/depth, time-span, and cadence from one survey to another, combining data is extremely non-trivial. Yet, that is something that needs to be done if one is after results beyond the low-hanging fruit. The complexity of incorporating ancillary data along with diverse datasets implies that one needs to combine various strategies and also look closely at other fields and borrow methods that may have been used in parallel situations.

In the US there are many Astroinformatics initiatives underway through the American Astronomical Society, American Statistical Association, LSST and SAMSI through focused groups and meetings. COST Big Sky Earth and the Cosmostatistics Initiative (COIN) are in a similar position for Europe. Through this symposium we hope to bring together many experts to better integrate astroinformatics and standardization in all fields of astronomy, in order to convert data to knowledge.

We aim to cover aspects of Supervised, Semi-supervised, and Unsupervised learning, Convolutional Networks, visualization, and updates on various surveys through review talks, invited talks, contributed talks, panel discussions and posters with 1-minute lightning talks. The format will encourage thinking and interaction among participants well after the Symposium.

To promote its interdisciplinary character the symposium is organised in close collaboration with Faculty of Information Technology of the Czech Technical University in Prague, Faculty of Information Technology of Brno University of Technology and with support of European COST Action TD1403 Big Sky Earth, which aims at identifying the problems and methodologies common to both astronomy and Earth observation in the Big Data era. As no Astroinformatics session was held during past EWASS meetings, this symposium will be a great way to introduce various aspects of Data Science to the wider EWASS community.

Programme

  • Finding hidden correlations in the complex astronomical Big Data
  • Challenges of spatial queries in peta-scale surveys
  • Massively parallel data mining in huge distributed databases
  • Scientific visualisation of complex many-dimensional data sets
  • Standardization of meta-data for better science
  • Advanced statistical inference in cosmology
  • Real time transient detection and classification
  • Applications of machine learning for source classification and clustering
  • Deep neural networks for feature extraction and image classification
  • Active learning and Domain Adaptation
  • Interdisciplinary panel discussions

Invited speakers

  • Mark Allen, Strasbourg astronomical Data Center, FR
  • Laurent Eyer, University of Geneva , CH
  • Emille E. O. Ishida, University Blaise Pascal, Clermont-Ferrand, FR
  • Darko Jevremovic, Astronomical observatory Belgrade, RS
  • Johan Knapen, Instituto de Astrofísica de Canarias (IAC), Tenerife, ES
  • Chris Lintott, University of Oxford, UK
  • Giuseppe Longo, University of Federico II, Naples, IT
  • Ashish Mahabal, Center for Data-Driven Discovery, Caltech, Pasadena, USA
  • Sabine McConnell , Trent University, Peterborough, Ontario, CA
  • Samaya Nissanke, Radboud University, Nijmegen, NL
  • Agnieszka Pollo, National Center for Nuclear Research Warsaw+University Krakow, PL
  • Kai Lars Polsterer, Heidelberg Institute for Theoretical Studies, DE
  • Enrique Solano, Centro de Astrobiologia, INTA-CSIC, Villafranca, ES
  • Aleksandra Solarz, National Center for Nuclear Research, Warsaw, PL
  • Edwin. A. Valentijn, University of Groningen, NL
  • Dejan Vinkovic, University of Split, HR
  • Lukasz Wyrzykowski, University of Warsaw, PL
  • Karine Zeitouni, University of Versailles, FR

Scientific organisers

  • Petr Škoda (Chair), Astronomical Institute of the Czech Academy of Sciences, Ondřejov, CZ
  • Massimo Brescia, Astronomical Observatory of Capodimonte, Naples, IT
  • Maria Gritsevich, Finnish Geospatial Research Institute, Helsinki, FI
  • Emille Ishida, Blaise Pascal University, Clermont-Ferrand, FR
  • Johan Knapen, Instituto de Astrofísica de Canarias , Tenerife, ES
  • Ashish Mahabal, Caltech Center for Data-Driven Discovery, Pasadena, USA
  • Agnieszka Pollo, National Center for Nuclear Research Warsaw, PL
  • Pavel Smrž ,Faculty of Information Technology, Brno University of Technology, CZ
  • Rafael de Souza, University Sao Paulo, BR
  • Felix Stoehr, ESO ALMA, Garching, DE
  • Karine Zeitouni, University of Versailles, FR

Contact
Petr Škoda (skoda at sunstel.asu.cas.cz), Ashish Mahabal (aam at astro.caltech.edu)

Updated on Wed Jan 04 13:42:43 CET 2017