Fig. 1:
Simulated observation showing a 32 × 32 arcmin2 patch of the
sky with a resolution of 0.1 arcmin.
Fig. 2:
Reconstruction of the point-like photon flux, where each marker
indicates a source, and its gray scale the corresponding flux.
Fig. 3:
Reconstruction of the diffuse photon flux in which noise and
instrumental artefacts have been removed.
A common problem for scientists analysing astronomical images is the
separation of diffuse and point-like components. This analysis has
now become easier: scientists at the Max Planck Institute for
Astrophysics have recently published the D³PO algorithm,
which removes noise effects and instrumental artefacts from the
observed images, while simultaneously separating diffuse and
point-like contributions.
Modern observatories provide raw images of the sky with high spatial
resolution. In the X-ray and gamma-ray domain, individual photons are
collected and depicted in photon count images. Since the number of
photons detected is random to a certain degree, the raw image suffers
from granularity due to the so-called shot noise. Further, an
inhomogeneous sky exposure - especially for larger area surveys - and
other instrumental effects leave unwanted imprints in the
observational data. Imperfect instrument optics can, for example,
cause point sources to be spread out so that they appear as smeared
out blobs in the raw image. Furthermore, the sky emission is often an
overlay of emission from different sources. Distinguishing between
them on the image is ambiguous as it is often not clear from which of
the sources a particular photon originates. It is therefore a real
challenge to extract the original, astrophysical information contained
in these noisy images and to sharpen them to high resolution.
To refine such raw images and reconstruct the original emission
sources as reliably as possible, researchers from Garching have now
developed a novel, intelligent imaging algorithm, which denoises,
deconvolves, and decomposes photon observations — thus the name
"D³PO". The removal or suppression of noise is commonly
denoted as "denoising". In case of photon count images, this requires
that the shot noise statistics is taken fully into account.
"Deconvolution" in this context denotes the rectification of
instrumental artefacts such as by imperfect optics. Spread out point
sources are hereby remapped and sharpened to a single position on the
image. Finally, "decomposition" is the separation of the photon count
image into two different images, one for the extended and one for the
point-like sources. The distinction between these morphologically
different components is the most difficult task since the algorithm
needs to decide on how to split the observed photons into the two
possible source classes.
In order to achieve all this simultaneously, the D³PO
algorithm relies on probabilistic inference that considers and weighs
virtually all possible images of the sky while taking into account the
raw photon image and all available a priori knowledge of how the sky
could look like. For example, from the knowledge of how the
observatory works, one has a decent idea of how a point source should
look like in the raw image. Given an observation, one can judge how
likely it is that a certain feature is a point source, diffuse
emission, or just shot noise. This probabilistic reasoning has been
designed using the framework of
Information Field Theory,
which provides a convenient language for the derivation of optimal
imaging methods.
The images delivered by the D³PO algorithm are not only
cured from noise and instrumental artefacts, but also provide a
separation of the photon flux into extended and point-like sources.
This is crucial for analysing high energy observations with respect to
the astrophysical nature of the emission. On the one hand, extended
emission regions, such as galactic clouds, galaxy
clusters, or unresolved cosmic background emission, can be studied in
the images without blooming point sources. And on the other hand, the
analysis of point sources, like neutron stars and quasi-stellar
objects (so-called quasars), can be carried out in images, where the
background has been removed.
The D³PO algorithm is currently applied to data from the
Chandra X-ray observatory and the Fermi gamma-ray space telescope at
the Max Planck Institute for Astrophysics. The resulting images will
hopefully provide the astrophysical community with a sharpened view on
the high energy Universe.
Marco Selig, Torsten Enßlin, and Hannelore Hämmerle
Background:
The D³PO algorithm has been developed by Marco Selig at the
Max Planck Institute for Astrophysics. Marco Selig is currently a PhD
student in the research group of Torsten Enßlin and investigates
information field theory-based imaging methods for high-energy
astrophysics. His implementation of the D³PO algorithm will
be released to the public in the near future.
References:
Marco Selig and Torsten A. Enßlin,
"Denoising, Deconvolving, and Decomposing Photon Observations",
submitted to Astronomy & Astrophysics,
http://arxiv.org/abs/1311.1888
