1,845 research outputs found
Galaxy shape measurement with convolutional neural networks
We present our results from training and evaluating a convolutional neural
network (CNN) to predict galaxy shapes from wide-field survey images of the
first data release of the Dark Energy Survey (DES DR1). We use conventional
shape measurements as ground truth from an overlapping, deeper survey with less
sky coverage, the Canada-France Hawaii Telescope Lensing Survey (CFHTLenS). We
demonstrate that CNN predictions from single band DES images reproduce the
results of CFHTLenS at bright magnitudes and show higher correlation with
CFHTLenS at fainter magnitudes than maximum likelihood model fitting estimates
in the DES Y1 im3shape catalogue. Prediction of shape parameters with a CNN is
also extremely fast, it takes only 0.2 milliseconds per galaxy, improving more
than 4 orders of magnitudes over forward model fitting. The CNN can also
accurately predict shapes when using multiple images of the same galaxy, even
in different color bands, with no additional computational overhead. The CNN is
again more precise for faint objects, and the advantage of the CNN is more
pronounced for blue galaxies than red ones when compared to the DES Y1
metacalibration catalogue, which fits a single Gaussian profile using riz band
images. We demonstrate that CNN shape predictions within the metacalibration
self-calibrating framework yield shear estimates with negligible multiplicative
bias, , and no significant PSF leakage. Our proposed setup is
applicable to current and next generation weak lensing surveys where higher
quality ground truth shapes can be measured in dedicated deep fields
Weak lensing cosmology with convolutional neural networks on noisy data
Weak gravitational lensing is one of the most promising cosmological probes
of the late universe. Several large ongoing (DES, KiDS, HSC) and planned (LSST,
EUCLID, WFIRST) astronomical surveys attempt to collect even deeper and larger
scale data on weak lensing. Due to gravitational collapse, the distribution of
dark matter is non-Gaussian on small scales. However, observations are
typically evaluated through the two-point correlation function of galaxy shear,
which does not capture non-Gaussian features of the lensing maps. Previous
studies attempted to extract non-Gaussian information from weak lensing
observations through several higher-order statistics such as the three-point
correlation function, peak counts or Minkowski-functionals. Deep convolutional
neural networks (CNN) emerged in the field of computer vision with tremendous
success, and they offer a new and very promising framework to extract
information from 2 or 3-dimensional astronomical data sets, confirmed by recent
studies on weak lensing. We show that a CNN is able to yield significantly
stricter constraints of () cosmological parameters than the
power spectrum using convergence maps generated by full N-body simulations and
ray-tracing, at angular scales and shape noise levels relevant for future
observations. In a scenario mimicking LSST or Euclid, the CNN yields 2.4-2.8
times smaller credible contours than the power spectrum, and 3.5-4.2 times
smaller at noise levels corresponding to a deep space survey such as WFIRST. We
also show that at shape noise levels achievable in future space surveys the CNN
yields 1.4-2.1 times smaller contours than peak counts, a higher-order
statistic capable of extracting non-Gaussian information from weak lensing
maps
DeepSphere: Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications
Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning
toolbox and have led to many breakthroughs in Artificial Intelligence. These
networks have mostly been developed for regular Euclidean domains such as those
supporting images, audio, or video. Because of their success, CNN-based methods
are becoming increasingly popular in Cosmology. Cosmological data often comes
as spherical maps, which make the use of the traditional CNNs more complicated.
The commonly used pixelization scheme for spherical maps is the Hierarchical
Equal Area isoLatitude Pixelisation (HEALPix). We present a spherical CNN for
analysis of full and partial HEALPix maps, which we call DeepSphere. The
spherical CNN is constructed by representing the sphere as a graph. Graphs are
versatile data structures that can act as a discrete representation of a
continuous manifold. Using the graph-based representation, we define many of
the standard CNN operations, such as convolution and pooling. With filters
restricted to being radial, our convolutions are equivariant to rotation on the
sphere, and DeepSphere can be made invariant or equivariant to rotation. This
way, DeepSphere is a special case of a graph CNN, tailored to the HEALPix
sampling of the sphere. This approach is computationally more efficient than
using spherical harmonics to perform convolutions. We demonstrate the method on
a classification problem of weak lensing mass maps from two cosmological models
and compare the performance of the CNN with that of two baseline classifiers.
The results show that the performance of DeepSphere is always superior or equal
to both of these baselines. For high noise levels and for data covering only a
smaller fraction of the sphere, DeepSphere achieves typically 10% better
classification accuracy than those baselines. Finally, we show how learned
filters can be visualized to introspect the neural network.Comment: arXiv admin note: text overlap with arXiv:astro-ph/0409513 by other
author
An improved cosmological parameter inference scheme motivated by deep learning
Dark matter cannot be observed directly, but its weak gravitational lensing
slightly distorts the apparent shapes of background galaxies, making weak
lensing one of the most promising probes of cosmology. Several observational
studies have measured the effect, and there are currently running, and planned
efforts to provide even larger, and higher resolution weak lensing maps. Due to
nonlinearities on small scales, the traditional analysis with two-point
statistics does not fully capture all the underlying information. Multiple
inference methods were proposed to extract more details based on higher order
statistics, peak statistics, Minkowski functionals and recently convolutional
neural networks (CNN). Here we present an improved convolutional neural network
that gives significantly better estimates of and
cosmological parameters from simulated convergence maps than the state of art
methods and also is free of systematic bias. We show that the network exploits
information in the gradients around peaks, and with this insight, we construct
a new, easy-to-understand, and robust peak counting algorithm based on the
'steepness' of peaks, instead of their heights. The proposed scheme is even
more accurate than the neural network on high-resolution noiseless maps. With
shape noise and lower resolution its relative advantage deteriorates, but it
remains more accurate than peak counting
Fast Point Spread Function Modeling with Deep Learning
Modeling the Point Spread Function (PSF) of wide-field surveys is vital for
many astrophysical applications and cosmological probes including weak
gravitational lensing. The PSF smears the image of any recorded object and
therefore needs to be taken into account when inferring properties of galaxies
from astronomical images. In the case of cosmic shear, the PSF is one of the
dominant sources of systematic errors and must be treated carefully to avoid
biases in cosmological parameters. Recently, forward modeling approaches to
calibrate shear measurements within the Monte-Carlo Control Loops ()
framework have been developed. These methods typically require simulating a
large amount of wide-field images, thus, the simulations need to be very fast
yet have realistic properties in key features such as the PSF pattern. Hence,
such forward modeling approaches require a very flexible PSF model, which is
quick to evaluate and whose parameters can be estimated reliably from survey
data. We present a PSF model that meets these requirements based on a fast
deep-learning method to estimate its free parameters. We demonstrate our
approach on publicly available SDSS data. We extract the most important
features of the SDSS sample via principal component analysis. Next, we
construct our model based on perturbations of a fixed base profile, ensuring
that it captures these features. We then train a Convolutional Neural Network
to estimate the free parameters of the model from noisy images of the PSF. This
allows us to render a model image of each star, which we compare to the SDSS
stars to evaluate the performance of our method. We find that our approach is
able to accurately reproduce the SDSS PSF at the pixel level, which, due to the
speed of both the model evaluation and the parameter estimation, offers good
prospects for incorporating our method into the framework.Comment: 25 pages, 8 figures, 1 tabl
GREAT3 results I: systematic errors in shear estimation and the impact of real galaxy morphology
We present first results from the third GRavitational lEnsing Accuracy
Testing (GREAT3) challenge, the third in a sequence of challenges for testing
methods of inferring weak gravitational lensing shear distortions from
simulated galaxy images. GREAT3 was divided into experiments to test three
specific questions, and included simulated space- and ground-based data with
constant or cosmologically-varying shear fields. The simplest (control)
experiment included parametric galaxies with a realistic distribution of
signal-to-noise, size, and ellipticity, and a complex point spread function
(PSF). The other experiments tested the additional impact of realistic galaxy
morphology, multiple exposure imaging, and the uncertainty about a
spatially-varying PSF; the last two questions will be explored in Paper II. The
24 participating teams competed to estimate lensing shears to within systematic
error tolerances for upcoming Stage-IV dark energy surveys, making 1525
submissions overall. GREAT3 saw considerable variety and innovation in the
types of methods applied. Several teams now meet or exceed the targets in many
of the tests conducted (to within the statistical errors). We conclude that the
presence of realistic galaxy morphology in simulations changes shear
calibration biases by per cent for a wide range of methods. Other
effects such as truncation biases due to finite galaxy postage stamps, and the
impact of galaxy type as measured by the S\'{e}rsic index, are quantified for
the first time. Our results generalize previous studies regarding sensitivities
to galaxy size and signal-to-noise, and to PSF properties such as seeing and
defocus. Almost all methods' results support the simple model in which additive
shear biases depend linearly on PSF ellipticity.Comment: 32 pages + 15 pages of technical appendices; 28 figures; submitted to
MNRAS; latest version has minor updates in presentation of 4 figures, no
changes in content or conclusion
Synergy between the Large Synoptic Survey Telescope and the Square Kilometre Array
We provide an overview of the science benefits of combining information from
the Square Kilometre Array (SKA) and the Large Synoptic Survey Telescope
(LSST). We first summarise the capabilities and timeline of the LSST and
overview its science goals. We then discuss the science questions in common
between the two projects, and how they can be best addressed by combining the
data from both telescopes. We describe how weak gravitational lensing and
galaxy clustering studies with LSST and SKA can provide improved constraints on
the causes of the cosmological acceleration. We summarise the benefits to
galaxy evolution studies of combining deep optical multi-band imaging with
radio observations. Finally, we discuss the excellent match between one of the
most unique features of the LSST, its temporal cadence in the optical waveband,
and the time resolution of the SKA.Comment: SKA Synergies Chapter, Advancing Astrophysics with the SKA (AASKA14)
Conference, Giardini Naxos (Italy), June 9th-13th 201
- …