31 research outputs found
Quasar microlensing light curve analysis using deep machine learning
We introduce a deep machine learning approach to studying quasar microlensing
light curves for the first time by analyzing hundreds of thousands of simulated
light curves with respect to the accretion disc size and temperature profile.
Our results indicate that it is possible to successfully classify very large
numbers of diverse light curve data and measure the accretion disc structure.
The detailed shape of the accretion disc brightness profile is found to play a
negligible role, in agreement with Mortonson et al. (2005). The speed and
efficiency of our deep machine learning approach is ideal for quantifying
physical properties in a `big-data' problem setup. This proposed approach looks
promising for analyzing decade-long light curves for thousands of microlensed
quasars, expected to be provided by the Large Synoptic Survey Telescope.Comment: 11 pages, 7 figures, accepted for publication in MNRA
A new parameter space study of cosmological microlensing
Cosmological gravitational microlensing is a useful technique for
understanding the structure of the inner parts of a quasar, especially the
accretion disk and the central supermassive black hole. So far, most of the
cosmological microlensing studies have focused on single objects from ~90
currently known lensed quasars. However, present and planned all-sky surveys
are expected to discover thousands of new lensed systems. Using a graphics
processing unit (GPU) accelerated ray-shooting code, we have generated 2550
magnification maps uniformly across the convergence ({\kappa}) and shear
({\gamma}) parameter space of interest to microlensing. We examine the effect
of random realizations of the microlens positions on map properties such as the
magnification probability distribution (MPD). It is shown that for most of the
parameter space a single map is representative of an average behaviour. All of
the simulations have been carried out on the GPU-Supercomputer for Theoretical
Astrophysics Research (gSTAR).Comment: 16 pages, 10 figures, accepted for publication in MNRA
A Quasar Microlensing Light Curve Generator for LSST
We present a tool to generate mock quasar microlensing light curves and
sample them according to any observing strategy. An updated treatment of the
fixed and random velocity components of observer, lens, and source is used,
together with a proper alignment with the external shear defining the
magnification map caustic orientation. Our tool produces quantitative results
on high magnification events and caustic crossings, which we use to study three
lensed quasars known to display microlensing, viz. RX J1131-1231, HE 0230-2130,
and Q 2237+0305, as they would be monitored by The Rubin Observatory Legacy
Survey of Space and Time (LSST). We conclude that depending on the location on
the sky, the lens and source redshift, and the caustic network density, the
microlensing variability may deviate significantly than the expected
20-year average time scale (Mosquera & Kochanek 2011, arXiv:1104.2356).
We estimate that high magnification events with mag mag
could potentially be observed by LSST each year. The duration of the majority
of high magnification events is between 10 and 100 days, requiring a very high
cadence to capture and resolve them. Uniform LSST observing strategies perform
the best in recovering microlensing high magnification events. Our web tool can
be extended to any instrument and observing strategy, and is freely available
as a service at http://gerlumph.swin.edu.au/tools/lsst_generator/, along with
all the related code.Comment: 10 pages, 6 figures, 2 tables. Published in MNRAS. Updated Table
Microlensing flux ratio predictions for Euclid
Quasar microlensing flux ratios are used to unveil properties of the lenses
in large collections of lensed quasars, like the ones expected to be produced
by the Euclid survey. This is achieved by using the direct survey products,
without any (expensive) follow-up observations or monitoring. First, the
theoretical flux ratio distribution of samples of hundreds of mock quasar
lenses is calculated for different Initial Mass Functions (IMFs) and Sersic
radial profiles for the lens compact matter distribution. Then, mock
observations are created and compared to the models to recover the underlying
one. The most important factor for determining the flux ratio properties of
such samples is the value of the smooth matter fraction at the location of the
multiple images. Doubly lensed CASTLES-like quasars are the most promising
systems to constrain the IMF and the mass components for a sample of lenses.Comment: 14 pages, 12 figures, 3 tables, accepted for publication in MNRA
Data Compression in the Petascale Astronomy Era: a GERLUMPH case study
As the volume of data grows, astronomers are increasingly faced with choices
on what data to keep -- and what to throw away. Recent work evaluating the
JPEG2000 (ISO/IEC 15444) standards as a future data format standard in
astronomy has shown promising results on observational data. However, there is
still a need to evaluate its potential on other type of astronomical data, such
as from numerical simulations. GERLUMPH (the GPU-Enabled High Resolution
cosmological MicroLensing parameter survey) represents an example of a data
intensive project in theoretical astrophysics. In the next phase of processing,
the ~27 terabyte GERLUMPH dataset is set to grow by a factor of 100 -- well
beyond the current storage capabilities of the supercomputing facility on which
it resides. In order to minimise bandwidth usage, file transfer time, and
storage space, this work evaluates several data compression techniques.
Specifically, we investigate off-the-shelf and custom lossless compression
algorithms as well as the lossy JPEG2000 compression format. Results of
lossless compression algorithms on GERLUMPH data products show small
compression ratios (1.35:1 to 4.69:1 of input file size) varying with the
nature of the input data. Our results suggest that JPEG2000 could be suitable
for other numerical datasets stored as gridded data or volumetric data. When
approaching lossy data compression, one should keep in mind the intended
purposes of the data to be compressed, and evaluate the effect of the loss on
future analysis. In our case study, lossy compression and a high compression
ratio do not significantly compromise the intended use of the data for
constraining quasar source profiles from cosmological microlensing.Comment: 15 pages, 9 figures, 5 tables. Published in the Special Issue of
Astronomy & Computing on The future of astronomical data format
Quasar microlensing light-curve analysis using deep machine learning
We introduce a deep machine learning approach to studying quasar microlensing light curves for the first time by analysing hundreds of thousands of simulated light curves with respect to the accretion disc size and temperature profile. Our results indicate that it is possible to successfully classify very large numbers of diverse light-curve data and measure the accretion disc structure. The detailed shape of the accretion disc brightness profile is found to play a negligible role. The speed and efficiency of our deep machine learning approach is ideal for quantifying physical properties in a `big-data' problem set-up. This proposed approach looks promising for analysing decade-long light curves for thousands of microlensed quasars, expected to be provided by the Large Synoptic Survey Telescope
Modeling lens potentials with continuous neural fields in galaxy-scale strong lenses
Strong gravitational lensing is a unique observational tool for studying the
dark and luminous mass distribution both within and between galaxies. Given the
presence of substructures, current strong lensing observations demand more
complex mass models than smooth analytical profiles, such as power-law
ellipsoids. In this work, we introduce a continuous neural field to predict the
lensing potential at any position throughout the image plane, allowing for a
nearly model-independent description of the lensing mass. We apply our method
on simulated Hubble Space Telescope imaging data containing different types of
perturbations to a smooth mass distribution: a localized dark subhalo, a
population of subhalos, and an external shear perturbation. Assuming knowledge
of the source surface brightness, we use the continuous neural field to model
either the perturbations alone or the full lensing potential. In both cases,
the resulting model is able to fit the imaging data, and we are able to
accurately recover the properties of both the smooth potential and of the
perturbations. Unlike many other deep learning methods, ours explicitly retains
lensing physics (i.e., the lens equation) and introduces high flexibility in
the model only where required, namely, in the lens potential. Moreover, the
neural network does not require pre-training on large sets of labelled data and
predicts the potential from the single observed lensing image. Our model is
implemented in the fully differentiable lens modeling code Herculens