BigSkyEarth 2018 Book of Abstracts

Transcription

1 BigSkyEarth 2018 Book of Abstracts Faculty of Technical Sciences Novi Sad, February th, 2018

2 Event of the COST Action TD 1403 Big Data Era in Sky and Earth Observation Supported by Provincial Secretariat for Higher Education and Scientific Research of the Provincial Government of the Autonomous Province of Vojvodina, Serbia

3 Table of Contents P. Skoda, A. Mickaelian, F. Adam, Progress of the Big Data book... 1 A. Nina, Big databases and low ionospheric observations... 2 A. B. Lowndes, Jetson in Space... 3 G. Nico, Persistent SAR imaging by airship: perspective applications... 4 M. Rasol, GPR Data Acquisition and Interpretation... 5 B. Pečnik, D. Vinković, Opportunities for collaboration on Hipersfera's airship demo-flights... 6 G. M. Szabó, The astronomical and engineering challenges of the Gothard Fast Imaging System... 7 K. Armaos, What makes a good Remote Sensing operator and how can we train a better one? Recent evidence and future perspectives... 8 B. Sirmacek, Smart visualization methods for practical usage of big data... 9 C. Ginzler, Value of Earth Observation in forest ecosystem research A. Kupusinac, Big data in medicine D. Vukobratović, Large-Scale Distributed Data Acquisition and Information Processing via 5G Mobile Cellular Networks C. Muller, A review of agriculture applications of Earth observation satellites G. Simeonov, AI Deep Learning. Deep Neural Networks Capsules (CapsNets) N. Sedaghat, TransiNet: Transient Detection with Deep Learning A. Hristov, M. Krsteva, Concurrent parallel programming models for Big-data processing A. Falcão, Computational Intelligence for ESA Projects D. Marmanis, Artificial Generation of Big Data for Improving Image Classification: A Generative Adversarial Network Approach on SAR Data i

4 D. Marmanis, Monitoring of Olive trees - Health and Crop Yield Estimation in the Mediterranean Region N. Chrysoulakis, Massive data analysis in urban climate research D. Jakovetić, Industrial-driven Big Data as a self-service (H2020 I-BiDaaS project) E. M. Nguifo, Deep Learning for Photo-z estimation O. Kounchev, New methods for multivariate non-parametric regression O. Kounchev, B. Srebrov, L. Pashova, Wavelet Analysis of Geomagnetic Storms A. Kovačević, A hybrid method for periodicity detection in the time series I. Nižetić Kosović, Heterogeneous environmental data analytics B. Antić, The Issue of Measurement Uncertainty Propagation in Big Data Metaheuristics M. Gritsevich, G. Maconi, et al., Experimental light scattering by ultrasonically controlled small particles A. Mickaelian, Discovery of new white dwarfs based on Gaia astrometry.. 30 D. Angerhausen, Sniffing Alien Atmospheres: Exoplanet spectrophotometry L. C. Popović, Spectroscopy of active galactic nuclei: sky surveys and spectral (big) data analysis methods V. Debattista, The challenge of orbits from simulations J. Aleksić, Transient events in LSST survey data D. Jevremović, LSST@Europe and Time Domain astronomy ii

5 Petr Skoda Astronomical Institute of the Czech Academy of Sciences, Czech Republic Areg Mickaelian Byurakan Astrophysical Observatory (BAO), Armenia Fathalrahman Adam German Aerospace Center (DLR), Germany Progress of the Big Data book We will present the current status of the work on the BSE Big Data book in Astro geo Informatics. 1

6 Aleksandra Nina Institute of Physics, Belgrade, Serbia Big databases and low ionospheric observations We give a description of the chapter in the BSE book that refers to databases collected in the low ionospheric monitoring by VLF/LF radio signals. This section shows the application of big databases in detection of astro and geo phenomena and for the low ionospheric plasma modelling. 2

7 Alison B Lowndes NVIDIA, UK Jetson in Space Artificial Intelligence will improve productivity, products and services, across a broad range of applications, all benefiting humanity, especially in the Space industry. This talk will briefly cover the HW & SW that comprise NVIDIA's GPU computing platform for AI, across PC to data centre, cloud to The Edge. 3

8 Giovanni Nico Consiglio Nazionale delle Ricerche (CNR), Istituto per le Applicazioni del Calcolo (IAC), Italy Persistent SAR imaging by airship: perspective applications The use of airships for Synthetic Aperture Radar (SAR) imaging. This platform integrates the traditional space and airborne and ground-based SAR acquisitions. The concept of persistent SAR imaging is presented describing a few applications that could be implemented by merging SAR data acquired by different platforms. Some SAR acquisition modes that could be implemented by means of an airship will be introduced. The advantages and possible limitations of airship SAR imaging will be discussed. 4

9 Mezgeen Rasol Universitat Politécnica de Cataluña, Spain GPR Data Acquisition and Interpretation Ground Penetrating Radar is a non-destructive Geophysical test to determine and discover the shallow geology surface. In this presentation the brief introduction of some case studies done in Barcelona, Spain will be described according to Data collections and interpretations. 5

10 Bojan Pečnik, Dejan Vinković Hipersfera Ltd., Croatia Opportunities for collaboration on Hipersfera's airship demo-flights Hipersfera Ltd. developed and tested a new type of UAS that combines the manoeuvrability of drones with the in-air endurance of rigid structure airships. This enables commercial mid-troposphere applications in services that require either stationary or slow moving industrial-scale platforms (~100kg of useful payload capacity and ~10kW of power). The initial applications are developed for the telecommunication sector and remote sensing (precision agriculture, forest management). This year we will engage in demo-flights of the first operational small-scale prototype. In this talk we will describe demo-flight experiments in telecom and agriculture, and briefly go through other secondary experiments under preparation. Options for academic and commercial collaborations will be also described. 6

11 Gyula M Szabó ELTE Gothard Observatory, Hungary The astronomical and engineering challenges of the Gothard Fast Imaging System We started developing both the hardware and the software support of the Gothard Fast Imaging System. This instrument is capable of recording a stream of images at several hundred frames per second, with applications in astronomy and possible developments toward engineering. The astronomy applications include observations of meteors, meteor impacts into the surface of the Moon, occultations by asteroids, etc. The bottleneck of fast imaging is at the I/O, which can be solved by application of a raid of the appropriate (huge) size, or reducing the image size and/or the frame rate, or application of burst imaging modes stored in the RAM and written to HDD off-measurement. We are working on another, software based solution to this issue, which is applicable when a very small fraction of the recorded images are "interesting" and should be archived, and must be identified in the image stream of a long lasting observation. We are now investigating how to make the usage of GPU pre-processing for this issue, and testing the boundaries of this approach. The status of the project will be presented. 7

12 Konstantinos Armaos Biomedical Research Foundation of Academy of Athens, Greece What makes a good Remote Sensing operator and how can we train a better one? Recent evidence and future perspectives Remote Sensing Image Analysis and map-making are fundamentally based on human visual interpretation. However, it has been recently documented that among different operators exists great variability in their interpretation of the same objects on a map and that multiple types of errors occur during this procedure. Frieke et al. (2013) developed and proposed a web-based tool as a method of quantifying operator variability and identifying which factors have the most significant effect in performance. This test, if correctly implemented, would be able to discriminate who are the most suitable candidates to become RS operators. Choosing the most capable applicants is the necessary first step to the quest of ideal RS interpretation. However, apart from choosing the right people, it is also highly important to have them trained correctly, similarly and efficiently. In order to do so, one needs to develop a valid training paradigm that could serve as a protocol for the people chosen to become RS operators. Such a method, as far as we are concerned, does not exist. We propose such a protocol, consisting of the following steps. Firstly, a number of experienced RS operators will use the web-based tool described by Frieke et al. (2013), while their eye movements will be monitored by an eye-tracking device. Their performance will be evaluated and the best-scoring operators will be chosen as training models. The eye-tracking recordings will also be analysed in order to investigate specific patterns that might lead to individual visual strategies adopted by each subject. Afterwards, we will investigate whether any correlations exist among these visual strategies and performance. Provided that such a correlation exists, we will proceed to the second stage of the method, the creation of a guide for visual inspection in collaboration with the experienced RS operators. This guide will consist of visual cues used by them, along with mental strategies related to other facets of the analysis. Then, at the final stage, we will provide this guide to a group of inexperienced trainees and evaluate their performance compared to another group which will be trained conventionally. 8

13 Beril Sirmacek farmar and University of Twente, The Netherlands Smart visualization methods for practical usage of big data Having worked in earth observation and remotely sensed image processing field for over than ten years, I have seen everyone encountering the same problems; 1- the remote sensing data is difficult to access, 2- the remote sensing data is difficult to interpret. Last three years, I have worked on how to make remote sensing data easily accessible and understandable by anyone. During this period, I have encountered with 3 'V' challenges of big data. These were; Volume, Velocity and Variety. In this talk I will address these challenges and our smart solutions for them at farmar framework which creates augmented reality for precision farming based on satellite image and big data processing. 9

14 Christian Ginzler Swiss Federal Institute for Forest, Snow and Landscape Research, Switzerland Value of Earth Observation in forest ecosystem research Over the past ten years significant advances have been made in remote sensing data and methods for forestry applications. In many cases datasets are no longer limited to use for case studies or the development of methods, but are available for large area applications, often with high temporal resolution. Aerial image acquisition, including in near infrared, has become standard thanks to the use of digital cameras. Colour infrared orthophotos are easily embedded into GIS applications at the cantonal and national level. Aerial laser scanning data has almost become the norm for many applications. Tools integrated into common working environments are available which allow for the efficient analysis of 3-D point clouds and the realisation of valuable products describing forested areas. Terrestrial laser scanning is also nearing operational applicability for many purposes. Remote sensing is continually developing into a practical instrument for daily work. As long as users are aware of the possibilities and also the limitations, remote sensing offers substantial potential to support and optimise terrestrial inventory and for the generation of additional large-scale values. 10

15 Aleksandar Kupusinac University of Novi Sad, Faculty of Technical Sciences, Serbia Big data in medicine The modern researches in medicine are based on large datasets, with lot of complex and unconventional cases, where classical statistical methods are usually inapplicable for the data analysis. In that case, methods of machine learning are better solution, since they can learn complex relationships from known data and then apply this knowledge on unknown data. Here will be presented the use of artificial neural network (ANN) for medical predictions. 11

16 Dejan Vukobratović University of Novi Sad, Faculty of Sciences, Serbia Large-Scale Distributed Data Acquisition and Information Processing via 5G Mobile Cellular Networks Massive-scale data acquisition and distributed in-network information processing is becoming reality in the upcoming 5G mobile cellular networks due to emergence of two new technologies: Machine-Type Communications (MTC) and Mobile Edge Computing (MEC). The first MTC standard called Narrowband- IoT (NB-IoT) is recently introduced as part of 3GPP 4G Long-Term Evolution (LTE) cellular technology, and the work on MTC standards will intensify in the forthcoming 5G New Radio (NR) technology. In parallel, by introducing virtualized network core architecture supported by MEC, 5G core network is evolving into an ideal platform for large-scale distributed information processing. In this talk, we review evolution of MTC and MEC as two key technologies for future IoT services in 5G. Moreover, we present our recent work on how these technologies will be fundamental for future large scale monitoring of critical infrastructures such as Smart Grids. 12

17 Christian Muller Royal Belgian Institute for Space Aeronomy, Belgium A review of agriculture applications of Earth observation satellites Since the first earth observations from space, agriculture signals were detected ranging from seasonal variations of vegetation to plankton and aquaculture. The first applications were attempts to evaluate the outcome of crops and to verify agricultural regulations. This second point was especially important for the European Union where the Common Agricultural Policy originally designed to ensure food self-sustainability was so successful that it led to surpluses and thus required a complex set of management rules. This off-line approach was used for most past and current earth observation satellites beginning with Explorer 6 in 1959 which proved the very possibility of earth imaging from space to culminate in the Landsat era going from an experimental system in 1972 to a current data acquisition and treatment chain generating both operational and scientific products. The data retrieval techniques evolved also at about the same pace as meteorological nowcasting and forecasting. Beside the NASA programmes, Europe developed its own national, ESA and E.U. programmes ranging from low spatial resolution to the current PLEIADES satellite and even further projects enabling facial resolution from space. These will make possible an on-line approach leading to a precision management of the agricultural processes, however, some elements of the on-line concept are already used in the field of fisheries and will be described. Examples exist also in the use of accurate weather forecasts in deciding on the time of harvests and on mitigation of cold weather episodes. Another step of evolution is the move from a few colour bands to hyperspectral with related increase in data volume. The history of these different evolutions will be shown with examples of successes and failures. 13

18 Georgi Simeonov IMI-BAS (Institute of Mathematics and Informatics at Bulgarian Academy of Sciences), Bulgaria AI Deep Learning. Deep Neural Networks Capsules (CapsNets) Capsule Neural Networks and Accelerated Neural Networks. Capsules introduce a new building block that can be used in deep learning to better model hierarchical relationships inside of internal knowledge representation of a neural network. In multilayer neural network each layer will be divided into many small groups of neurons called "capsules" and known as Geoffrey Hinton s Capsule Networks. Interesting characteristics of capsules will be briefly reviewed. There are challenges for accelerating current implementations as well as other modern deep learning models. We have new opportunities for hardware acceleration going beyond GPU-based computing. 14

19 Nima Sedaghat University of Freiburg, Germany TransiNet: Transient Detection with Deep Learning Large sky surveys are increasingly relying on image subtraction pipelines for realtime (and archival) transient detection. In this process one has to contend with varying PSF, small brightness variations in many sources, as well as artefacts resulting from saturated stars, and, in general, matching errors. Very often the differencing is done with a reference image that is deeper than individual images and the attendant difference in noise characteristics can also lead to artefacts. We present here a deep-learning approach to transient detection that encapsulates all the steps of a traditional image subtraction pipeline -- image registration, background subtraction, noise removal, psf matching, and subtraction -- into a single real-time convolutional network. Once trained the method works lighteningly fast, and given that it does multiple steps at one go, the advantages for multi-ccd, fast surveys like ZTF and LSST are obvious. 15

20 Atanas Hristov University of Information Science and Technology, FYR Macedonia Marijana Krsteva "St. Clement" University - Bitola, FYR Macedonia Concurrent parallel programming models for Big-data processing Our discussion will be mainly focus on approaches, models and environments for parallel programming with main focus on Big-data processing tools. It has been proven that a pipeline of one or more CPUs and GPUs can do the same job as multiple CPUs/GPUs working on the same problem simultaneously. We will address several major frameworks that allow execution and analysis of data across different platforms. It is basically an overview of the distinguishing features of the platforms for General-purpose computing on graphics processing units. We will further go into the specific features of OpenMP, Intel Cilk, Open ACC, Cuda, OpenHMPP, OpenCL, C++AMP and how each of them works differently towards the same purpose. Taking into account that the data repositories are rapidly increasing in size and exceeding exabytes, it seems prudent to discuss parallelizing of the data analysis to help us decrease the runtime execution of the processes. These technologies will further ease the deployment of parallel systems into energy-sensitive environments such as mobile computing platforms, supercomputers, and data repositories. 16

21 António Falcão Instituto de Desenvolvimento de Novas Tecnologias, Computational Intelligence Research group (CA3), Portugal Computational Intelligence for ESA Projects This talk is a short presentation of 15 years of experience with ESA, providing computational intelligence techniques to support mission operations, data processing, and development of new applications and technologies. This includes big data analytics and visualisation of astronomical data, decision support systems for flight control teams, autonomous safe landing site selection for planetary landers, health monitoring and early warning for on-board systems and ground stations, among others. 17

22 Dimitrios Marmanis German Aerospace Centre (DLR), Germany Artificial Generation of Big Data for Improving Image Classification: A Generative Adversarial Network Approach on SAR Data Very High Spatial Resolution (VHSR) large-scale SAR image databases are still an unresolved issue in the Remote Sensing field. In this work, we propose such a dataset and use it to explore patch-based classification in urban and periurban areas, considering 7 distinct semantic classes. In this context, we investigate the accuracy of large CNN classification models and pre-trained networks for SAR imaging systems. Furthermore, we propose a Generative Adversarial Network (GAN) for SAR image generation and test, whether the synthetic data can actually improve classification accuracy. 18

23 Dimitrios Marmanis German Aerospace Centre (DLR), Germany Monitoring of Olive trees - Health and Crop Yield Estimation in the Mediterranean Region In this scientific proposal we present the importance of monitoring olive trees through Remote Sensing means, for evaluating their overall health (soil moisture, tree temperature, contamination etc.) and ensuring high-crop productivity. Furthermore, we are interested in investigating aspects such as crop yield estimation by machine learning means combined with in-situ measurements. Through this presentation we are interested in establishing a scientific proposal in this line of research. 19

24 Nektarios Chrysoulakis FORTH (Foundation for Research and Technology Hellas), Greece Massive data analysis in urban climate research The H2020-Space project URBANFLUXES (URBan ANthrpogenic heat FLUX from Earth observation Satellites) analyzed massive satellite data and ground based energy fluxes observations to investigate the potential of Copernicus Sentinels to retrieve anthropogenic heat flux, as a key component of the Urban Energy Budget (UEB). URBANFLUXES advanced the current knowledge of the impacts of UEB fluxes on urban heat island and consequently on energy consumption in cities. In URBANFLUXES, the anthropogenic heat flux was estimated as a residual of UEB. Therefore, the rest UEB components, namely, the net all-wave radiation, the net change in heat storage and the turbulent sensible and latent heat fluxes were independently estimated from Earth Observation (EO), whereas the advection term was included in the error of the anthropogenic heat flux estimation from the UEB closure. Almost of two year of daily observations at local scale (100 m x 100 m) were processed for three European cities (London, Basel and Heraklion). The Discrete Anisotropic Radiative Transfer (DART) model was employed to improve the estimation of the net all-wave radiation balance, whereas the Element Surface Temperature Method (ESTM), adjusted to satellite observations was used to improve the estimation the estimation of the net change in heat storage. Furthermore, the estimation of the turbulent sensible and latent heat fluxes was based on the Aerodynamic Resistance Method (ARM). Insitu flux measurements were used to evaluate URBANFLUXES outcomes, whereas uncertainties were specified and analyzed. URBANFLUXES prepared the ground for further innovative exploitation of EO in scientific activities (climate variability studies at local and regional scales) and future and emerging applications (sustainable urban planning, mitigation technologies) to benefit climate change mitigation/adaptation. More detailed information on the project is available at: 20

25 Dušan Jakovetić University of Novi Sad, Faculty of Sciences, Serbia Industrial-driven Big Data as a self-service (H2020 I-BiDaaS project) The convergence of the internet of things (IoT), cloud, and big data, create new challenges and opportunities for data analytics. Human- and machine-created data is being aggregated, transforming our economy and society. To face these challenges, companies call upon expert analysts and consultants to assist them. I- BiDaaS is a EU H2020 research and innovation project that proposes a self-service solution for Big Data analytics. This solution will be transformative for organizations, as it will empower their employees with the right knowledge, and give the true decision-makers the insights they need to make the right decisions. It will shift the power balance within an organization, increase efficiency, reduce costs, improve employee empowerment, and increase profitability. I-BiDaaS aims to empower users to easily utilize and interact with Big Data technologies, by designing, building, and demonstrating, a unified solution that significantly increases the speed of data analysis while coping with the rate of data asset growth, and facilitates cross-domain data-flow towards a thriving data-driven EU economy. IBiDaaS will be tangibly validated by three real-world, industry-lead experiments, in the domains of banking, manufacturing, and telecommunications. The aim of the talk is to give an overview of the I-BiDaaS project and present its vision and main objectives. 21

26 Engelbert Mephu Nguifo Université Clermont Auvergne - LIMOS, France Deep Learning for Photo-z estimation Photometric redshift estimation is an important problem in astrophysics. Accurate redshift predictions for all astronomical objects are still needed, as well as scalable algorithms to leverage big data in sky digital surveys. The goal of this talk is to explore scalable deep learning architectures and algorithms, which can improve current state-of-the-art results. 22

27 Ognyan Kounchev Institute of mathematics and informatics, Bulgarian Academy of Sciences, Bulgaria New methods for multivariate non-parametric regression We have develop new methods for multivariate nonparametric regression which are based on the theory of polysplines. We will demonstrate their advantage in terms of fast computations on large arrays of data from Earth observations. 23

28 Ognyan Kounchev, Bozhidar Srebrov, Lyubka Pashova Institute of mathematics and informatics, Bulgarian Academy of Sciences, Bulgaria Wavelet Analysis of Geomagnetic Storms We analyze geomagnetic storms by using continuous wavelet transform. Some new effects appear to become visible due to the wavelet properties. 24

29 Anđelka Kovačević University of Belgrade, Faculty of Mathematics, Department of Astronomy, Serbia A hybrid method for periodicity detection in the time series Periodicity mining is a tool for predicting the behaviour of time series data. Mostly periodicity mining algorithms assume that users either know the value of the period beforehand or are varying period values until satisfactory periodic patterns emerge. Here we present a hybrid time series processing method for comparing two signals of arbitrary length and sampling rate. Beyond numerical determination of periods, the links between periodicities present in the two signals is visualized via 2D or 3D images. The method is applied on astronomical time series obtained from more than 20 years long monitoring campaigns with unfavourable sampling rates. 25

30 Ivana Nižetić Kosović Ericsson Nikola Tesla, Croatia Heterogeneous environmental data analytics Focus of the proposal are challenges of modelling and combining heterogeneous data (from different sources - sensor data, drone images etc., different time scales and different spatial dimensions) and adding context and semantics to raw data (expert knowledge). 26

31 Boris Antić University of Novi Sad, Faculty of Sciences, Serbia The Issue of Measurement Uncertainty Propagation in Big Data Metaheuristics As the big data era folds itself around almost every aspect of contemporary technology and natural sciences the underlying metaheuristics used to process and analyse the data seemed to have being widely neglected. Yet more and more often scientists have to face this seemingly incomprehensible issue on tracking down the impact of the data quality at the input on decision making processes performed at the end. It is well known that the quality of data and its heterogeneity can be represented as an n-dimensional vector the uncertainty of data values. This n- dimensional vector expressed either as an absolute or as a relative multidimensional physical quantity is known as the measurement uncertainty. When using heuristic mathematical methods for data processing, tracking the transformation of the measurement uncertainty from the input into the uncertainty (quality) of the output is a well-known deterministic problem in science and engineering and it can easily be solved using various derivative calculus. However, in case of metaheuristics, there is no deterministic transformation from end to end and, hence the existing methods known to the science of measurements and data processing can t be applied. Instead new approaches have to be investigated in order to analyse the impact of the input data quality on final decision making. Furthermore, the propagation of data uncertainty has not being taken into account seriously until quite recently. In this presentation we will demonstrate some possible approaches in dealing with uncertainty propagation in big data sets and its possible impact on the outcomes of some metaheuristic processing methods. 27

32 M. Gritsevich (a,b), G. Maconi (a), P. Helander (a), A. Penttilä (a), I. Kassamakov (a,c), T. Puranen (a), A. Salmi (a), K. Muinonen (a,d), and E. Hæggström (a) (a) Department of Physics, University of Helsinki, Finland (b) Institute of Physics and Technology, Ural Federal University, Russia (c) Helsinki Institute of Physics, Finland (d) Finnish Geospatial Research Institute FGI, Finland Experimental light scattering by ultrasonically controlled small particles Electromagnetic scattering is a fundamental physical process that allows inferring characteristics of an object studied remotely. This possibility is enhanced by obtaining the light-scattering response at multiple wavelengths and viewing geometries, i.e., by considering a wider range of the phase angle (the angle between the incident light and the light reflected from the object) in the experiment. Planetary environments represent numerous examples of scattering media composed of particles. There is a fundamental difficulty, however, in bridging the gap between the light-scattering theory and experiment: while existing theoretical models can be used reliably to simulate scattering by a fixed finite object or random particles [1], thorough experimental work has mostly been performed with light scattered from surfaces, see e.g. [2-4]. We present an overview of our newly developed 3D scatterometer a setup for precise multiangular measurements of light scattered by mm- to μm-sized samples held in place by sound [5]. The measurements obtained with the scatterometer can be crossvalidated against the modeled light-scattering characteristics of the sample, i.e., the intensity and the degree of linear polarization of the reflected light, calculated with state-of-the-art electromagnetic techniques. [1] Mishchenko M.I., Travis L.D. and Lacis A.A. 2006: Multiple Scattering of Light by Particles: Radiative Transfer and Coherent Backscattering. Cambridge University Press. [2] Videen G., Kocifaj M. (Eds.) 2002: Optics of cosmic dust (NATO Science Series), Kluwer Academic Publishers. 28

33 [3] Peltoniemi J.I., Gritsevich M., Hakala T., Dagsson-Waldhauserová P., et al. 2015: Soot on snow experiment: bidirectional reflectance factor measurements of contaminated snow. The Cryosphere, 9, [4] Zubko N., Gritsevich M., Zubko E., Hakala T., Peltoniemi J.I. 2016: Optical measurements of chemically heterogeneous particulate surfaces. JQSRT 178, [5] 29

34 Areg Mickaelian Byurakan Astrophysical Observatory (BAO), Armenia Discovery of new white dwarfs based on Gaia astrometry ESA s Gaia space observatory s astrometric accuracy allows detailed studies of the kinematics of the Milky Way, the proper motions of more than one billion stars, including many white dwarfs (WDs). WDs are evolutionary signatures of the Milky Way, as most of stars pass through this evolutionary stage. Gaia DR1 contains astrometric results for > 1 billion stars with < Our method of combined calculation of proper motions (PM) and estimation of stellar distances (Mickaelian & Sinamyan 2010, MNRAS 407, 681) resulted in accuracy of 3.9 mas/yr for PM in each coordinate and revelation of 640 First Byurakan Survey UV-excess stars with PM >10 mas/yr. Adopting 50 km/s upper limit for tangential velocities, we calculated maximum distances and absolute magnitudes and estimated luminosity types for these objects, revealing 185 probable (M > 8), 69 possible WDs (6 < M < 8) and 42 candidate subdwarfs/wds. The Digitized First Byurakan Survey is more efficient for discovery of new WDs by spectral energy distribution. Using Gaia data for accurate PM in combination with DFBS lowdispersion spectra and additional multiwavelength data, during the pilot survey we have revealed thousands of new WDs and candidate subdwarf/wds. Many of them are subject for studies on binarity and variability. Gaia DR2 data to be accessible in April 2018 will provide much better accuracy and will result in the discovery of millions of new WDs. 30

35 Daniel Angerhausen Center for Space and Habitability, University of Bern, Switzerland Sniffing Alien Atmospheres: Exoplanet spectrophotometry In my presentation I will give a short introduction to the science of extrasolar planets, in particular the technique of transit, eclipse and phase curve spectrophotometry. I will describe my various projects in this emerging field using state of the art spectroscopic and photometric instruments on the largest ground based telescopes, the 'flying telescope' SOFIA (Stratospheric Observatory for Infrared Astronomy) and the Kepler and Hubble space telescopes. 31

36 Luka C. Popović Astronomical Observatory Belgrade, Serbia Spectroscopy of active galactic nuclei: spectral (big) data analysis methods sky surveys and Quasars or active galactic nuclei (AGNs) are one of the most powerful sources of energy in the Universe. They emit radiation from the gamma to radio spectral range, where in the UV/optical spectral band strong spectral emission lines are present. The emission lines are coming from the central part of these object and their parameters can give information about the physics of the emitting regions and characteristics of the central supermassive black hole. Sky surveys, as e.g. Sloan Digital Sky Survey, provide us a huge amount of spectral data that open new possibilities in investigation of active galactic nuclei, but also several new problems as big data management and analysis of this amount of data on an accurate way, to avoid some misleading conclusions. Here we discuss the tools for big AGN spectral data analysis. 32

37 Victor Debattista University of Central Lancashire, UK The challenge of orbits from simulations I will describe orbits in barred galaxy simulations and illustrate some of the science results that comes from computing them, most especially with regard to understanding the bulge of the Milky Way. This will provide us with great insights into the formation of the Milky Way once the Gaia data returns data from the region of the bulge. However computing orbits from simulations presents some challenges. Aside from the sheer computing needed to calculate the orbits themselves, we also need to contend with how to represent the orbits across time. A lot of these problems are open and I will be interested in learning from my audience about some techniques that are suitable and suggestions for collaborations. 33

38 Jovan Aleksić Astronomical Observatory Belgrade, Serbia Transient events in LSST survey data Large Synoptic Survey Telescope will make great contributions to many scientific fields. One of the modules will be time-domain astronomy and detection of transient events. In this paper, some considerations about transient events and alerts are presented. 34

39 Darko Jevremović Astronomical Observatory Belgrade, Serbia and Time Domain astronomy We are going to summarize some ideas about future COST actions in which current members of BSE could be involved. has potential to bring together not only stake-holders in LSST (PI's and their students/postdocs) from many countries but also to widen usage of data and help new generation of scientist to get up to speed with Big Data developments. With an advent of LSST and SKA Time Domain Astronomy (TDA) is going to be hot topic in 2020's. Our experience of TDA is limited - usually on small number of objects and it is going to be challenging to sift through data deluge. We expect that network of interested scientist would benefit from more structured organization such as COST action. For both actions relatively rapid follow-ups with different instruments is necessary to maximize understanding of events and that brings even more potential users/network members. All the standard COST networking activities (meetings, schools, developing materials...) will be used. Also we expect involvement of Astronomy ESFRI and Infrastructure Cluster (Asterics) and especially its work package OBELICS (Observatory E-environments LINked by common ChallengeS). We expect that at least one proposal for these actions to be submitted in the fall

40 BigSkyEarth 2018 BOOK OF ABSTRACTS Scientific Organizing Committee Boris Antić, Faculty of Technical Sciences in Novi Sad, Serbia Dejan Vinković, Hipersfera Ltd., Croatia Fathalrahman Adam, German Aerospace Centre (DLR), Germany Areg Mickaelian, Byurakan Astrophysical Observatory, Armenia Gottfried Schwartz, German Aerospace Centre (DLR), Germany Ognyan Kounchev, Institute of mathematics and informatics, BAS, Bulgaria Victor Debattista, University of Central Lancashire, UK Dimitrios Marmanis. German Aerospace Centre (DLR), Germany Darko Jevremović, Astronomical Observatory Belgrade, Serbia Petr Skoda, Astronomical Instit. of the Czech Acad. of Sciences, Czech Republic Marco Quartulli, Vicomtech, Spain and the rest of the Management Committee of COST Action TD1403 Design and editing Ivana Čokrlić, Zoran Mitrović, Đorđe Novaković Printing GRID Faculty of Technical Sciences in Novi Sad Publisher Faculty of Technical Sciences in Novi Sad Trg Dositeja Obradovića 6, Novi Sad Circulation: 80 copies Copyright COST Action TD 1403 BigSkyEarth (bigskyearth.eu) ISBN CIP CATALOGUING IN PUBLICATION available at National Library of Serbia, Belgrade COBISS.SR-ID