13,247 research outputs found
Searching the Sky with CONFIGR-STARS
SyNAPSE program of the Defense Advanced Projects Research Agency (HRL Laboratories LLC, subcontract #801881-BS under DARPA prime contract HR0011-09-C-0001); CELEST, a National Science Foundation Science of Learning Center (SBE-0354378)CONFIGR-STARS, a new methodology based on a model of the human visual system, is developed for registration of star images. The algorithm first applies CONFIGR, a neural model that connects sparse and noisy image components. CONFIGR produces a web of connections between stars in a reference starmap or in a test patch of unknown location. CONFIGR-STARS splits the resulting, typically highly connected, web into clusters, or "constellations." Cluster geometry is encoded as a signature vector that records edge lengths and angles relative to the clusterās baseline edge. The location of a test patch cluster is identified by comparing its signature to signatures in the codebook of a reference starmap, where cluster locations are known. Simulations demonstrate robust performance in spite of image perturbations and omissions, and across starmaps from different sources and seasons. Further studies would test CONFIGR-STARS and algorithm variations applied to very large starmaps and to other technologies that may employ geometric signatures. Open-source code, data, and demos are available from http://techlab.bu.edu/STARS/
Finding strong lenses in CFHTLS using convolutional neural networks
We train and apply convolutional neural networks, a machine learning
technique developed to learn from and classify image data, to
Canada-France-Hawaii Telescope Legacy Survey (CFHTLS) imaging for the
identification of potential strong lensing systems. An ensemble of four
convolutional neural networks was trained on images of simulated galaxy-galaxy
lenses. The training sets consisted of a total of 62,406 simulated lenses and
64,673 non-lens negative examples generated with two different methodologies.
The networks were able to learn the features of simulated lenses with accuracy
of up to 99.8% and a purity and completeness of 94-100% on a test set of 2000
simulations. An ensemble of trained networks was applied to all of the 171
square degrees of the CFHTLS wide field image data, identifying 18,861
candidates including 63 known and 139 other potential lens candidates. A second
search of 1.4 million early type galaxies selected from the survey catalog as
potential deflectors, identified 2,465 candidates including 117 previously
known lens candidates, 29 confirmed lenses/high-quality lens candidates, 266
novel probable or potential lenses and 2097 candidates we classify as false
positives. For the catalog-based search we estimate a completeness of 21-28%
with respect to detectable lenses and a purity of 15%, with a false-positive
rate of 1 in 671 images tested. We predict a human astronomer reviewing
candidates produced by the system would identify ~20 probable lenses and 100
possible lenses per hour in a sample selected by the robot. Convolutional
neural networks are therefore a promising tool for use in the search for lenses
in current and forthcoming surveys such as the Dark Energy Survey and the Large
Synoptic Survey Telescope.Comment: 16 pages, 8 figures. Accepted by MNRA
Applying Deep Learning to Fast Radio Burst Classification
Upcoming Fast Radio Burst (FRB) surveys will search 10\, beams on
sky with very high duty cycle, generating large numbers of single-pulse
candidates. The abundance of false positives presents an intractable problem if
candidates are to be inspected by eye, making it a good application for
artificial intelligence (AI). We apply deep learning to single pulse
classification and develop a hierarchical framework for ranking events by their
probability of being true astrophysical transients. We construct a tree-like
deep neural network (DNN) that takes multiple or individual data products as
input (e.g. dynamic spectra and multi-beam detection information) and trains on
them simultaneously. We have built training and test sets using false-positive
triggers from real telescopes, along with simulated FRBs, and single pulses
from pulsars. Training of the DNN was independently done for two radio
telescopes: the CHIME Pathfinder, and Apertif on Westerbork. High accuracy and
recall can be achieved with a labelled training set of a few thousand events.
Even with high triggering rates, classification can be done very quickly on
Graphical Processing Units (GPUs). That speed is essential for selective
voltage dumps or issuing real-time VOEvents. Next, we investigate whether
dedispersion back-ends could be completely replaced by a real-time DNN
classifier. It is shown that a single forward propagation through a moderate
convolutional network could be faster than brute-force dedispersion; but the
low signal-to-noise per pixel makes such a classifier sub-optimal for this
problem. Real-time automated classification may prove useful for bright,
unexpected signals, both now and in the era of radio astronomy when data
volumes and the searchable parameter spaces further outgrow our ability to
manually inspect the data, such as for SKA and ngVLA
Artificial neural networks for selection of pulsar candidates from the radio continuum surveys
Pulsar search with time-domain observation is very computationally expensive
and data volume will be enormous with the next generation telescopes such as
the Square Kilometre Array. We apply artificial neural networks (ANNs), a
machine learning method, for efficient selection of pulsar candidates from
radio continuum surveys, which are much cheaper than time-domain observation.
With observed quantities such as radio fluxes, sky position and compactness as
inputs, our ANNs output the "score" that indicates the degree of likeliness of
an object to be a pulsar. We demonstrate ANNs based on existing survey data by
the TIFR GMRT Sky Survey (TGSS) and the NRAO VLA Sky Survey (NVSS) and test
their performance. Precision, which is the ratio of the number of pulsars
classified correctly as pulsars to that of any objects classified as pulsars,
is about 96. Finally, we apply the trained ANNs to unidentified radio
sources and our fiducial ANN with five inputs (the galactic longitude and
latitude, the TGSS and NVSS fluxes and compactness) generates 2,436 pulsar
candidates from 456,866 unidentified radio sources. These candidates need to be
confirmed if they are truly pulsars by time-domain observations. More
information such as polarization will narrow the candidates down further.Comment: 11 pages, 13 figures, 3 tables, accepted for publication in MNRA
ASPECT: A spectra clustering tool for exploration of large spectral surveys
We present the novel, semi-automated clustering tool ASPECT for analysing
voluminous archives of spectra. The heart of the program is a neural network in
form of Kohonen's self-organizing map. The resulting map is designed as an icon
map suitable for the inspection by eye. The visual analysis is supported by the
option to blend in individual object properties such as redshift, apparent
magnitude, or signal-to-noise ratio. In addition, the package provides several
tools for the selection of special spectral types, e.g. local difference maps
which reflect the deviations of all spectra from one given input spectrum (real
or artificial). ASPECT is able to produce a two-dimensional topological map of
a huge number of spectra. The software package enables the user to browse and
navigate through a huge data pool and helps him to gain an insight into
underlying relationships between the spectra and other physical properties and
to get the big picture of the entire data set. We demonstrate the capability of
ASPECT by clustering the entire data pool of 0.6 million spectra from the Data
Release 4 of the Sloan Digital Sky Survey (SDSS). To illustrate the results
regarding quality and completeness we track objects from existing catalogues of
quasars and carbon stars, respectively, and connect the SDSS spectra with
morphological information from the GalaxyZoo project.Comment: 15 pages, 14 figures; accepted for publication in Astronomy and
Astrophysic
Convolutional neural networks: a magic bullet for gravitational-wave detection?
In the last few years, machine learning techniques, in particular
convolutional neural networks, have been investigated as a method to replace or
complement traditional matched filtering techniques that are used to detect the
gravitational-wave signature of merging black holes. However, to date, these
methods have not yet been successfully applied to the analysis of long
stretches of data recorded by the Advanced LIGO and Virgo gravitational-wave
observatories. In this work, we critically examine the use of convolutional
neural networks as a tool to search for merging black holes. We identify the
strengths and limitations of this approach, highlight some common pitfalls in
translating between machine learning and gravitational-wave astronomy, and
discuss the interdisciplinary challenges. In particular, we explain in detail
why convolutional neural networks alone cannot be used to claim a statistically
significant gravitational-wave detection. However, we demonstrate how they can
still be used to rapidly flag the times of potential signals in the data for a
more detailed follow-up. Our convolutional neural network architecture as well
as the proposed performance metrics are better suited for this task than a
standard binary classifications scheme. A detailed evaluation of our approach
on Advanced LIGO data demonstrates the potential of such systems as trigger
generators. Finally, we sound a note of caution by constructing adversarial
examples, which showcase interesting "failure modes" of our model, where inputs
with no visible resemblance to real gravitational-wave signals are identified
as such by the network with high confidence.Comment: First two authors contributed equally; appeared at Phys. Rev.
- ā¦