24 research outputs found
Approximate Inference for Constructing Astronomical Catalogs from Images
We present a new, fully generative model for constructing astronomical
catalogs from optical telescope image sets. Each pixel intensity is treated as
a random variable with parameters that depend on the latent properties of stars
and galaxies. These latent properties are themselves modeled as random. We
compare two procedures for posterior inference. One procedure is based on
Markov chain Monte Carlo (MCMC) while the other is based on variational
inference (VI). The MCMC procedure excels at quantifying uncertainty, while the
VI procedure is 1000 times faster. On a supercomputer, the VI procedure
efficiently uses 665,000 CPU cores to construct an astronomical catalog from 50
terabytes of images in 14.6 minutes, demonstrating the scaling characteristics
necessary to construct catalogs for upcoming astronomical surveys.Comment: accepted to the Annals of Applied Statistic
The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch
Recent and forthcoming advances in instrumentation, and giant new surveys,
are creating astronomical data sets that are not amenable to the methods of
analysis familiar to astronomers. Traditional methods are often inadequate not
merely because of the size in bytes of the data sets, but also because of the
complexity of modern data sets. Mathematical limitations of familiar algorithms
and techniques in dealing with such data sets create a critical need for new
paradigms for the representation, analysis and scientific visualization (as
opposed to illustrative visualization) of heterogeneous, multiresolution data
across application domains. Some of the problems presented by the new data sets
have been addressed by other disciplines such as applied mathematics,
statistics and machine learning and have been utilized by other sciences such
as space-based geosciences. Unfortunately, valuable results pertaining to these
problems are mostly to be found only in publications outside of astronomy. Here
we offer brief overviews of a number of concepts, techniques and developments,
some "old" and some new. These are generally unknown to most of the
astronomical community, but are vital to the analysis and visualization of
complex datasets and images. In order for astronomers to take advantage of the
richness and complexity of the new era of data, and to be able to identify,
adopt, and apply new solutions, the astronomical community needs a certain
degree of awareness and understanding of the new concepts. One of the goals of
this paper is to help bridge the gap between applied mathematics, artificial
intelligence and computer science on the one side and astronomy on the other.Comment: 24 pages, 8 Figures, 1 Table. Accepted for publication: "Advances in
Astronomy, special issue "Robotic Astronomy
Data Mining and Machine Learning in Astronomy
We review the current state of data mining and machine learning in astronomy.
'Data Mining' can have a somewhat mixed connotation from the point of view of a
researcher in this field. If used correctly, it can be a powerful approach,
holding the potential to fully exploit the exponentially increasing amount of
available data, promising great scientific advance. However, if misused, it can
be little more than the black-box application of complex computing algorithms
that may give little physical insight, and provide questionable results. Here,
we give an overview of the entire data mining process, from data collection
through to the interpretation of results. We cover common machine learning
algorithms, such as artificial neural networks and support vector machines,
applications from a broad range of astronomy, emphasizing those where data
mining techniques directly resulted in improved science, and important current
and future directions, including probability density functions, parallel
algorithms, petascale computing, and the time domain. We conclude that, so long
as one carefully selects an appropriate algorithm, and is guided by the
astronomical problem at hand, data mining can be very much the powerful tool,
and not the questionable black box.Comment: Published in IJMPD. 61 pages, uses ws-ijmpd.cls. Several extra
figures, some minor additions to the tex
LSST Science Book, Version 2.0
A survey that can cover the sky in optical bands over wide fields to faint
magnitudes with a fast cadence will enable many of the exciting science
opportunities of the next decade. The Large Synoptic Survey Telescope (LSST)
will have an effective aperture of 6.7 meters and an imaging camera with field
of view of 9.6 deg^2, and will be devoted to a ten-year imaging survey over
20,000 deg^2 south of +15 deg. Each pointing will be imaged 2000 times with
fifteen second exposures in six broad bands from 0.35 to 1.1 microns, to a
total point-source depth of r~27.5. The LSST Science Book describes the basic
parameters of the LSST hardware, software, and observing plans. The book
discusses educational and outreach opportunities, then goes on to describe a
broad range of science that LSST will revolutionize: mapping the inner and
outer Solar System, stellar populations in the Milky Way and nearby galaxies,
the structure of the Milky Way disk and halo and other objects in the Local
Volume, transient and variable objects both at low and high redshift, and the
properties of normal and active galaxies at low and high redshift. It then
turns to far-field cosmological topics, exploring properties of supernovae to
z~1, strong and weak lensing, the large-scale distribution of galaxies and
baryon oscillations, and how these different probes may be combined to
constrain cosmological models and the physics of dark energy.Comment: 596 pages. Also available at full resolution at
http://www.lsst.org/lsst/sciboo
EuCAPT White Paper: Opportunities and Challenges for Theoretical Astroparticle Physics in the Next Decade
Astroparticle physics is undergoing a profound transformation, due to a
series of extraordinary new results, such as the discovery of high-energy
cosmic neutrinos with IceCube, the direct detection of gravitational waves with
LIGO and Virgo, and many others. This white paper is the result of a
collaborative effort that involved hundreds of theoretical astroparticle
physicists and cosmologists, under the coordination of the European Consortium
for Astroparticle Theory (EuCAPT). Addressed to the whole astroparticle physics
community, it explores upcoming theoretical opportunities and challenges for
our field of research, with particular emphasis on the possible synergies among
different subfields, and the prospects for solving the most fundamental open
questions with multi-messenger observations.Comment: White paper of the European Consortium for Astroparticle Theory
(EuCAPT). 135 authors, 400 endorsers, 133 pages, 1382 reference
LSST Science Book, Version 2.0
A survey that can cover the sky in optical bands over wide fields to faint magnitudes with a fast cadence will enable many of the exciting science opportunities of the next decade. The Large Synoptic Survey Telescope (LSST) will have an effective aperture of 6.7 meters and an imaging camera with field of view of 9.6 deg^2, and will be devoted to a ten-year imaging survey over 20,000 deg^2 south of +15 deg. Each pointing will be imaged 2000 times with fifteen second exposures in six broad bands from 0.35 to 1.1 microns, to a total point-source depth of r~27.5. The LSST Science Book describes the basic parameters of the LSST hardware, software, and observing plans. The book discusses educational and outreach opportunities, then goes on to describe a broad range of science that LSST will revolutionize: mapping the inner and outer Solar System, stellar populations in the Milky Way and nearby galaxies, the structure of the Milky Way disk and halo and other objects in the Local Volume, transient and variable objects both at low and high redshift, and the properties of normal and active galaxies at low and high redshift. It then turns to far-field cosmological topics, exploring properties of supernovae to z~1, strong and weak lensing, the large-scale distribution of galaxies and baryon oscillations, and how these different probes may be combined to constrain cosmological models and the physics of dark energy
Probing the nature of dark energy with 21-cm intensity mapping.
Doctoral Degree. University of KwaZulu-Natal, Durban.Two approaches to measure the BAOs (baryon acoustic oscillations) with optical and radio
telescopes, namely; galaxy redshift and intensity mapping (IM) surveys have been introduced
and discussed in the literature. Among the two methods, the galaxy redshift survey has been
used to great effect and is based on the detection and survey of millions of individual galaxies and
measuring their redshifts by comparing templates of the spectral energy distributions of the light
emitted from the galaxies with optical lines. IM is novel but a robust approach that focuses on
surveys of extremely large volumes of galaxies without resolving each individual galaxy and can
efficiently probe scales over redshift ranges inaccessible to the current galaxy redshift surveys.
However, the IM survey has promisingly shown to have better overall sensitivity to the BAOs
than the galaxy redshift survey but has a number of serious issues to be quantified. The most
obvious of these issues is the presence of foreground contaminants from the Milky Way galaxy
and extragalactic point sources which strongly dominate the neutral hydrogen (Hi) signal of our
interest.
Under this study, we are interested to realize the IM approach, pave the pathway, and optimize
the scientific outputs of future radio experiments. We, therefore, carry out simulations
and present forecasts of the cosmological constraints by employing Hi IM technique with three
near-term radio telescopes by assuming 1 year of observational time. The telescopes considered
here are Five-hundred-meter Aperture Spherical radio Telescope (FAST), BAOs In Neutral
Gas Observations (BINGO), and Square Kilometre Array Phase I (SKA-I) single-dish experiments.
We further forecast the combined constraints of the three radio telescopes with Planck
measurements.
In order to tackle the foreground challenge, we develop strategies to model various sky components
and employ an approach to clean them from our Milky Way galaxy and extragalactic point
sources by considering a typical single-dish radio telescope. Particularly, the Principal Component
Analysis foreground separation approach considered can indeed recover the cosmological
Hi signal to high precision. We show that, although the approach may face some challenges, it
can be fully realized on the selected range of angular scales