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Data analysis in space sciences has been performed exclusively visually for years, despite the fact that the largest amount of data belongs to non-visible portions of the electromagnetic spectrum. This, on the one hand, limits the study of…
Astronomy is increasingly encountering two fundamental truths: (1) The field is faced with the task of extracting useful information from extremely large, complex, and high dimensional datasets; (2) The techniques of astroinformatics and…
Object cross-identification in multiple observations is often complicated by the uncertainties in their astrometric calibration. Due to the lack of standard reference objects, an image with a small field of view can have significantly…
Astronomy produces extremely large data sets from ground-based telescopes, space missions, and simulation. The volume and complexity of these rich data sets require new approaches and advanced tools to understand the information contained…
Modern scientific data mainly consist of huge datasets gathered by a very large number of techniques and stored in very diversified and often incompatible data repositories. More in general, in the e-science environment, it is considered as…
Developers perform online sensemaking on a daily basis, such as researching and choosing libraries and APIs. Prior research has introduced tools that help developers capture information from various sources and organize it into structures…
The rapid growth of imaging and spectroscopic surveys has intensified the need for efficient tools that support visual inspection, a practice that remains essential for tasks such as classification, catalog refinement, and validation of…
High dimensional parameter space optimization is crucial in many applications. The parameters affecting this performance can be both numerical and categorical in their type. The existing techniques of black-box optimization and visual…
Data visualization is the process by which data of any size or dimensionality is processed to produce an understandable set of data in a lower dimensionality, allowing it to be manipulated and understood more easily by people. The goal of…
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…
Petabyte-scale data volumes are generated by observations and simulations in modern astronomy and astrophysics. Storage, access, and data analysis are significantly hampered by such data volumes and are leading to the development of a new…
Celeste is a procedure for inferring astronomical catalogs that attains state-of-the-art scientific results. To date, Celeste has been scaled to at most hundreds of megabytes of astronomical images: Bayesian posterior inference is…
Sky surveys represent a fundamental data basis for astronomy. We use them to map in a systematic way the universe and its constituents, and to discover new types of objects or phenomena. We review the subject, with an emphasis on the…
Where appropriate repositories are not available to support all relevant astronomical data products, data can fall into darkness: unseen and unavailable for future reference and re-use. Some data in this category are legacy or old data, but…
Formal representations of the visualization design space, such as knowledge bases and graphs, consolidate design practices into a shared resource and enable automated reasoning and interpretable design recommendations. However, prior…
We present a state-of-the-art report on visualization in astrophysics. We survey representative papers from both astrophysics and visualization and provide a taxonomy of existing approaches based on data analysis tasks. The approaches are…
A set of preferred records can be obtained from a large database in a multi-criteria setting using various computational methods which either depend on the concept of dominance or on the concept of utility or scoring function based on the…
Astronomy has a long history of acquiring, systematizing, and interpreting large quantities of data. Starting from the earliest sky atlases through the first major photographic sky surveys of the 20th century, this tradition is continuing…
We introduce AXS (Astronomy eXtensions for Spark), a scalable open-source astronomical data analysis framework built on Apache Spark, a widely used industry-standard engine for big data processing. Building on capabilities present in Spark,…
Modern astronomy relies on massive databases collected by robotic telescopes and digital sky surveys, acquiring data in a much faster pace than what manual analysis can support. Among other data, these sky surveys collect information about…