36,886 research outputs found
SKIRT: the design of a suite of input models for Monte Carlo radiative transfer simulations
The Monte Carlo method is the most popular technique to perform radiative
transfer simulations in a general 3D geometry. The algorithms behind and
acceleration techniques for Monte Carlo radiative transfer are discussed
extensively in the literature, and many different Monte Carlo codes are
publicly available. On the contrary, the design of a suite of components that
can be used for the distribution of sources and sinks in radiative transfer
codes has received very little attention. The availability of such models, with
different degrees of complexity, has many benefits. For example, they can serve
as toy models to test new physical ingredients, or as parameterised models for
inverse radiative transfer fitting. For 3D Monte Carlo codes, this requires
algorithms to efficiently generate random positions from 3D density
distributions. We describe the design of a flexible suite of components for the
Monte Carlo radiative transfer code SKIRT. The design is based on a combination
of basic building blocks (which can be either analytical toy models or
numerical models defined on grids or a set of particles) and the extensive use
of decorators that combine and alter these building blocks to more complex
structures. For a number of decorators, e.g. those that add spiral structure or
clumpiness, we provide a detailed description of the algorithms that can be
used to generate random positions. Advantages of this decorator-based design
include code transparency, the avoidance of code duplication, and an increase
in code maintainability. Moreover, since decorators can be chained without
problems, very complex models can easily be constructed out of simple building
blocks. Finally, based on a number of test simulations, we demonstrate that our
design using customised random position generators is superior to a simpler
design based on a generic black-box random position generator.Comment: 15 pages, 4 figures, accepted for publication in Astronomy and
Computin
Diffusive optical tomography in the Bayesian framework
Many naturally-occuring models in the sciences are well-approximated by
simplified models, using multiscale techniques. In such settings it is natural
to ask about the relationship between inverse problems defined by the original
problem and by the multiscale approximation. We develop an approach to this
problem and exemplify it in the context of optical tomographic imaging.
Optical tomographic imaging is a technique for infering the properties of
biological tissue via measurements of the incoming and outgoing light
intensity; it may be used as a medical imaging methodology. Mathematically,
light propagation is modeled by the radiative transfer equation (RTE), and
optical tomography amounts to reconstructing the scattering and the absorption
coefficients in the RTE from boundary measurements. We study this problem in
the Bayesian framework, focussing on the strong scattering regime. In this
regime the forward RTE is close to the diffusion equation (DE). We study the
RTE in the asymptotic regime where the forward problem approaches the DE, and
prove convergence of the inverse RTE to the inverse DE in both nonlinear and
linear settings. Convergence is proved by studying the distance between the two
posterior distributions using the Hellinger metric, and using Kullback-Leibler
divergence
Probabilistic Dalek -- Emulator framework with probabilistic prediction for supernova tomography
Supernova spectral time series can be used to reconstruct a spatially
resolved explosion model known as supernova tomography. In addition to an
observed spectral time series, a supernova tomography requires a radiative
transfer model to perform the inverse problem with uncertainty quantification
for a reconstruction. The smallest parametrizations of supernova tomography
models are roughly a dozen parameters with a realistic one requiring more than
100. Realistic radiative transfer models require tens of CPU minutes for a
single evaluation making the problem computationally intractable with
traditional means requiring millions of MCMC samples for such a problem. A new
method for accelerating simulations known as surrogate models or emulators
using machine learning techniques offers a solution for such problems and a way
to understand progenitors/explosions from spectral time series. There exist
emulators for the TARDIS supernova radiative transfer code but they only
perform well on simplistic low-dimensional models (roughly a dozen parameters)
with a small number of applications for knowledge gain in the supernova field.
In this work, we present a new emulator for the radiative transfer code TARDIS
that not only outperforms existing emulators but also provides uncertainties in
its prediction. It offers the foundation for a future active-learning-based
machinery that will be able to emulate very high dimensional spaces of hundreds
of parameters crucial for unraveling urgent questions in supernovae and related
fields.Comment: 7 pages, accepted at ICML 2022 Workshop on Machine Learning for
Astrophysic
Deep learning for the modeling and inverse design of radiative heat transfer
Deep learning is having a tremendous impact in many areas of computer science and engineering. Motivated by this success, deep neural networks are attracting increasing attention in many other disciplines,
including the physical sciences. In this work, we show that artificial neural networks can be successfully used in the theoretical modeling and analysis of a variety of radiative-heat-transfer phenomena and
devices. By using a set of custom-designed numerical methods able to efficiently generate the required
training data sets, we demonstrate this approach in the context of three very different problems, namely
(i) near-field radiative heat transfer between multilayer systems that form hyperbolic metamaterials,
(ii) passive radiate cooling in photonic crystal slab structures, and (iii) thermal emission of subwavelength objects. Despite their fundamental differences in nature, in all three cases we show that simple
neural-network architectures trained with data sets of moderate size can be used as fast and accurate
surrogates for doing numerical simulations, as well as engines for solving inverse design and optimization in the context of radiative heat transfer. Overall, our work shows that deep learning and artificial
neural networks provide a valuable and versatile toolkit for advancing the field of thermal radiatio
Ray-tracing for complex astrophysical high-opacity structures
We present a ray-tracing technique for radiative transfer modeling of complex
three-dimensional (3D) structures which include dense regions of high optical
depth like in dense molecular clouds, circumstellar disks, envelopes of evolved
stars, and dust tori around active galactic nuclei. The corresponding continuum
radiative transfer problem is described and the numerical requirements for
inverse 3D density and temperature modeling are defined. We introduce a
relative intensity and transform the radiative transfer equation along the rays
to solve machine precision problems and to relax strong gradients in the source
term. For the optically thick regions where common ray-tracers are forced to
perform small trace steps, we give two criteria for making use of a simple
approximative solver crossing the optically thick region quickly. Using an
example of a density structure with optical depth changes of 6 orders of
magnitude and sharp temperature variations, we demonstrate the accuracy of the
proposed scheme using a common 5th-order Runge-Kutta ray-tracer with adaptive
step size control. In our test case, the gain in computational speed is about a
factor of 870. The method is applied to calculate the temperature distribution
within a massive molecular cloud core for different boundary conditions for the
radiation field.Comment: 21 pages, 5 figures to appear in Astrophysical Journa
- …