Related papers: Data Mining and Machine-Learning in Time-Domain Di…
The time domain is the emerging forefront of astronomical research with new facilities and instruments providing unprecedented amounts of data on the temporal behavior of astrophysical populations. Dealing with the size and complexity of…
It is argued that the astronomy of the twenty-first century will be dominated by computer-based manipulation of huge homogeneous surveys of various types of astronomical objects. Furthermore combination of all observations with large…
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…
Astronomy has always been at the forefront of information technology, moving from the era of photographic plates, to digital snapshots and now to digital movies of the sky. This has brought about a data explosion with multi- terabyte…
Exploration of the time domain - variable and transient objects and phenomena - is rapidly becoming a vibrant research frontier, touching on essentially every field of astronomy and astrophysics, from the Solar system to cosmology. Time…
Historical materials are abundant. Yet, piecing together how human knowledge has evolved and spread both diachronically and synchronically remains a challenge that can so far only be very selectively addressed. The vast volume of materials…
Over the past decade, neutrino astronomy has emerged as a new window into the extreme and hidden universe. Current-generation experiments have detected high-energy neutrinos of astrophysical origin and identified the first sources, opening…
Given that observational and numerical climate data are being produced at ever more prodigious rates, increasingly sophisticated and automated analysis techniques have become essential. Deep learning is quickly becoming a standard approach…
Machine learning has been widely applied to clearly defined problems of astronomy and astrophysics. However, deep learning and its conceptual differences to classical machine learning have been largely overlooked in these fields. The broad…
Yearslong time series of high-precision brightness measurements have been assembled for thousands of stars with telescopes operating in space. Such data have allowed astronomers to measure the physics of stellar interiors via nonradial…
Could Machine Learning (ML) make fundamental discoveries and tackle unsolved problems in Cosmology? Detailed observations of the present contents of the universe are consistent with the Cosmological Constant Lambda and Cold Dark Matter…
Telescope and detector developments continuously enable deeper and more detailed studies of astronomical objects. Larger collecting areas, improvement in dispersion and detector techniques, and higher sensitivities allow detection of more…
Studying our Galaxy, the Milky Way (MW), gives us a close-up view of the interplay between cosmology, dark matter, and galaxy formation. In the next decade our understanding of the MW's dynamics, stellar populations, and structure will…
Time-domain astronomy is entering an era of unprecedented discovery driven by wide-field, high-cadence surveys such as LSST, Roman, Euclid, SKA, and PLATO. While some of these facilities will generate enormous photometric alert streams, the…
We present recent results from the LCDM (Laboratory for Cosmological Data Mining; http://lcdm.astro.uiuc.edu) collaboration between UIUC Astronomy and NCSA to deploy supercomputing cluster resources and machine learning algorithms for the…
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…
The stellar astronomy has always been considered the fundamental source of knowledge about the basic building blocks of the universe - the stars. It has proved correctness of many physical theories - like e.g. the idea of nuclear fusion in…
Since the first detection of gravitational waves in 2015, gravitational-wave astronomy has emerged as a rapidly advancing field that holds great potential for studying the cosmos, from probing the properties of black holes to testing the…
We summarize the first exploratory investigation into whether Machine Learning techniques can augment science strategic planning. We find that an approach based on Latent Dirichlet Allocation using abstracts drawn from high impact astronomy…
The alignments of galaxies across the large-scale structure of the Universe are known to be a source of contamination for gravitational lensing, but they can also probe cosmology and the physics of galaxy evolution in many ways. In this…