Related papers: WISeREP - An Interactive Supernova Data Repository
(Abridged) In this first paper of a series, we report the creation of large and well-defined database that combines extensive new measurements and a literature search of 3876 supernovae (SNe) and their 3679 host galaxies located in the sky…
Hyperspectral imaging has become a significant source of valuable data for astronomers over the past decades. Current instrumental and observing time constraints allow direct acquisition of multispectral images, with high spatial but low…
We describe the atomic database of the XSTAR spectral modeling code, summarizing the systematic upgrades carried out in the past twenty years to enable the modeling of K lines from chemical elements with atomic number $Z\leq 30$ and recent…
In this paper, we present a study on learning visual recognition models from large scale noisy web data. We build a new database called WebVision, which contains more than $2.4$ million web images crawled from the Internet by using queries…
We review some aspects of the current state of data-intensive astronomy, its methods, and some outstanding data analysis challenges. Astronomy is at the forefront of "big data" science, with exponentially growing data volumes and data…
Scientific endeavors such as large astronomical surveys generate databases on the terabyte scale. These, usually multidimensional databases must be visualized and mined in order to find interesting objects or to extract meaningful and…
We present a description of the design and usage of a new synoptic pipeline and database model for time series photometry in the VISTA Data Flow System (VDFS). All UKIRT-WFCAM data and most of the VISTA main survey data will be processed…
A key requirement for leveraging supervised deep learning methods is the availability of large, labeled datasets. Unfortunately, in the context of RGB-D scene understanding, very little data is available -- current datasets cover a small…
Seismic data interpolation is a critical pre-processing step for improving seismic imaging quality and remains a focus of academic innovation. To address the computational inefficiencies caused by extensive iterative resampling in current…
The SuperNova/Acceleration Probe (SNAP) will measure precisely the cosmological expansion history over both the acceleration and deceleration epochs and thereby constrain the nature of the dark energy that dominates our universe today. The…
Earth observation is a fundamental tool for monitoring the evolution of land use in specific areas of interest. Observing and precisely defining change, in this context, requires both time-series data and pixel-wise segmentations. To that…
The upcoming Legacy Survey of Space and Time (LSST) conducted by the Vera C. Rubin Observatory will detect millions of supernovae (SNe) and generate millions of nightly alerts, far outpacing available spectroscopic resources. Rapid,…
We present an alternate deployment of the GrayStarServer (now ChromaStarServer (CSS)) pedagogical stellar atmosphere and spectrum synthesis WWW-application, namely ChromaStarDB (CSDB), in which the atomic line list used for spectrum…
Data access -- or the availability of new and archival data for use by the larger community -- is key for scientific advancement. How data is presented, searched, and formatted determines accessibility and it can be difficult to find a…
Large modern surveys require efficient review of data in order to find transient sources such as supernovae, and to distinguish such sources from artefacts and noise. Much effort has been put into the development of automatic algorithms,…
Discovery rates of supernovae are expected to surpass one million events annually with the Vera C. Rubin Observatory. With unprecedented sample sizes of both common and rare transient types, photometric classification alone will be…
In this paper, a synthetic hyperspectral video database is introduced. Since it is impossible to record ground truth hyperspectral videos, this database offers the possibility to leverage the evaluation of algorithms in diverse…
Spiking neural networks (SNNs) are emerging as a promising alternative to traditional artificial neural networks (ANNs), offering biological plausibility and energy efficiency. Despite these merits, SNNs are frequently hampered by limited…
Pedestrian detection has achieved significant progress with the availability of existing benchmark datasets. However, there is a gap in the diversity and density between real world requirements and current pedestrian detection benchmarks:…
Mergers play a complex role in galaxy formation and evolution. Continuing to improve our understanding of these systems require ever larger samples, which can be difficult (even impossible) to select from individual surveys. We use the new…