Related papers: Data engineering for archive evolution
The body of photometric and astrometric data on stars in the Galaxy has been growing very fast in recent years (Hipparcos/Tycho, OGLE-3, 2-Mass, DENIS, UCAC2, SDSS, RAVE, Pan Starrs, Hermes, ...) and in two years ESA will launch the Gaia…
Observatories need to measure and evaluate the scientific output and overall impact of their facilities. An observatory bibliography consists of the papers published using that observatory's data, typically gathered by searching the major…
Radio-astronomy is about to embark on a new way of doing science. The revolution that is about to take place is not due to the enormous sensitivity of the Square Kilometre Array, which is still a decade away, but due to its pathfinders,…
Astronomical observations, physical experiments as well as computer simulations often involve discrete data sets supposed to represent a fair sample of an underlying smooth and continuous field. Reconstructing the underlying fields from a…
Gamma-ray astronomy is able to acquire large data volumes that astronomers use to draw scientific conclusions from. Ensuring the possibility of accessing and utilizing this data also after the lifetime of currently running experiments…
Astronomy is experiencing a rapid growth in data size and complexity. This change fosters the development of data-driven science as a useful companion to the common model-driven data analysis paradigm, where astronomers develop automatic…
We shall present with examples how analysis of astronomy data can be used for an educational purpose to train students in methods of data analysis, statistics, programming skills and research problems. Special reference will be made to our…
For decades now, scientific data volumes have experienced relentless, exponential growth. As a result, legacy astronomical data formats are straining under a burden not conceived when these formats were first introduced. With future…
Accumulation of new data on stellar hierarchical systems and the progress in numerical simulations of their formation open the door to genetic classification of these systems, where properties of a certain group (family) of objects are…
Our planet is experiencing an accelerated process of change associated to a variety of anthropogenic phenomena. The future of this transformation is uncertain, but there is general agreement about its negative unfolding that might threaten…
The amount of data produced by large observational facilities and space missions has led to the archiving and on-line accessibility of much of this data, available to the entire astronomical community. This allows a much wider…
The prevalent paradigm of machine learning today is to use past observations to predict future ones. What if, however, we are interested in knowing the past given the present? This situation is indeed one that astronomers must contend with…
High-dimensional astronomical data cubes provide a wealth of spectral and structural information that can be used to study astrophysical and chemical processes. The complexity and sheer size of these datasets pose significant challenges in…
A pervasive problem in Data Science is that the knowledge generated by possibly expensive analytics processes is subject to decay over time, as the data used to compute it drifts, the algorithms used in the processes are improved, and the…
Cyber-physical systems come with increasingly complex architectures and failure modes, which complicates the task of obtaining accurate system reliability models. At the same time, with the emergence of the (industrial) Internet-of-Things,…
The continuous increase in the availability of data of any kind, coupled with the development of networks of high-speed communications, the popularization of cloud computing and the growth of data centers and the emergence of…
Astronomy is entering an era of data-driven discovery, due in part to modern machine learning (ML) techniques enabling powerful new ways to interpret observations. This shift in our scientific approach requires us to consider whether we can…
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),…
We examine contributions of second order physical processes to results of stellar evolution calculations amenable to direct observational testing. In the first paper in the series (Young et al. 2001) we established baseline results using…
With the volume and availability of astronomical data growing rapidly, astronomers will soon rely on the use of machine learning algorithms in their daily work. This proceeding aims to give an overview of what machine learning is and delve…