415 research outputs found
Block-Simultaneous Direction Method of Multipliers: A proximal primal-dual splitting algorithm for nonconvex problems with multiple constraints
We introduce a generalization of the linearized Alternating Direction Method
of Multipliers to optimize a real-valued function of multiple arguments
with potentially multiple constraints on each of them. The function
may be nonconvex as long as it is convex in every argument, while the
constraints need to be convex but not smooth. If is smooth, the
proposed Block-Simultaneous Direction Method of Multipliers (bSDMM) can be
interpreted as a proximal analog to inexact coordinate descent methods under
constraints. Unlike alternative approaches for joint solvers of
multiple-constraint problems, we do not require linear operators of a
constraint function to be invertible or linked between each
other. bSDMM is well-suited for a range of optimization problems, in particular
for data analysis, where is the likelihood function of a model and
could be a transformation matrix describing e.g. finite differences or basis
transforms. We apply bSDMM to the Non-negative Matrix Factorization task of a
hyperspectral unmixing problem and demonstrate convergence and effectiveness of
multiple constraints on both matrix factors. The algorithms are implemented in
python and released as an open-source package.Comment: 13 pages, 4 figure
Joint Cosmic Density Reconstruction from Photometric and Spectroscopic Samples
We reconstruct the dark matter density field from spatially overlapping
spectroscopic and photometric redshift catalogs through a forward modelling
approach. Instead of directly inferring the underlying density field, we find
the best fitting initial Gaussian fluctuations that will evolve into the
observed cosmic volume. To account for the substantial uncertainty of
photometric redshifts we employ a differentiable continuous Poisson process. In
the context of the upcoming Prime Focus Spectrograph (PFS), we find
improvements in cosmic structure classification equivalent to 50-100\% more
spectroscopic targets by combining relatively sparse spectroscopic with dense
photometric samples.Comment: 7 pages, 6 figure
Multiscale Feature Attribution for Outliers
Machine learning techniques can automatically identify outliers in massive
datasets, much faster and more reproducible than human inspection ever could.
But finding such outliers immediately leads to the question: which features
render this input anomalous? We propose a new feature attribution method,
Inverse Multiscale Occlusion, that is specifically designed for outliers, for
which we have little knowledge of the type of features we want to identify and
expect that the model performance is questionable because anomalous test data
likely exceed the limits of the training data. We demonstrate our method on
outliers detected in galaxy spectra from the Dark Energy Survey Instrument and
find its results to be much more interpretable than alternative attribution
approaches.Comment: 6 pages, 2 figures, accepted to NeurIPS 2023 Workshop on Machine
Learning and the Physical Sciences. Code available at
https://github.com/al-jshen/im
Plausible Adversarial Attacks on Direct Parameter Inference Models in Astrophysics
In this abstract we explore the possibility of introducing biases in physical
parameter inference models from adversarial-type attacks. In particular, we
inject small amplitude systematics into inputs to a mixture density networks
tasked with inferring cosmological parameters from observed data. The
systematics are constructed analogously to white-box adversarial attacks. We
find that the analysis network can be tricked into spurious detection of new
physics in cases where standard cosmological estimators would be insensitive.
This calls into question the robustness of such networks and their utility for
reliably detecting new physics.Comment: Accepted submission to Machine Learning and the Physical Sciences
workshop, NeurIPS 202
Spotting Hallucinations in Inverse Problems with Data-Driven Priors
Hallucinations are an inescapable consequence of solving inverse problems
with deep neural networks. The expressiveness of recent generative models is
the reason why they can yield results far superior to conventional
regularizers; it can also lead to realistic-looking but incorrect features,
potentially undermining the trust in important aspects of the reconstruction.
We present a practical and computationally efficient method to determine, which
regions in the solutions of inverse problems with data-driven priors are prone
to hallucinations. By computing the diagonal elements of the Fisher information
matrix of the likelihood and the data-driven prior separately, we can flag
regions where the information is prior-dominated. Our diagnostic can directly
be compared to the reconstructed solutions and enables users to decide if
measurements in such regions are robust for their application. Our method
scales linearly with the number of parameters and is thus applicable in
high-dimensional settings, allowing it to be rolled out broadly for the
large-volume data products of future wide-field surveys.Comment: 7 pages 3 figures, Accepted at the ICML 2023 Workshop on Machine
Learning for Astrophysic
First measurement of gravitational lensing by cosmic voids in SDSS
We report the first measurement of the diminutive lensing signal arising from
matter underdensities associated with cosmic voids. While undetectable
individually, by stacking the weak gravitational shear estimates around 901
voids detected in SDSS DR7 by Sutter et al. (2012a), we find substantial
evidence for a depression of the lensing signal compared to the cosmic mean.
This depression is most pronounced at the void radius, in agreement with
analytical models of void matter profiles. Even with the largest void sample
and imaging survey available today, we cannot put useful constraints on the
radial dark-matter void profile. We invite independent investigations of our
findings by releasing data and analysis code to the public at
https://github.com/pmelchior/void-lensingComment: 6 pages, 5 figures, as accepted by MNRA
Lightweight starshade position sensing with convolutional neural networks and simulation-based inference
Starshades are a leading technology to enable the direct detection and
spectroscopic characterization of Earth-like exoplanets. To keep the starshade
and telescope aligned over large separations, reliable sensing of the peak of
the diffracted light of the occluded star is required. Current techniques rely
on image matching or model fitting, both of which put substantial computational
burdens on resource-limited spacecraft computers. We present a lightweight
image processing method based on a convolutional neural network paired with a
simulation-based inference technique to estimate the position of the spot of
Arago and its uncertainty. The method achieves an accuracy of a few centimeters
across the entire pupil plane, while only requiring 1.6 MB in stored data
structures and 5.3 MFLOPs (million floating point operations) per image at test
time. By deploying our method at the Princeton Starshade Testbed, we
demonstrate that the neural network can be trained on simulated images and used
on real images, and that it can successfully be integrated in the control
system for closed-loop formation flying.Comment: submitted to JATI
Parametrising arbitrary galaxy morphologies: potentials and pitfalls
We demonstrate that morphological observables (e.g. steepness of the radial
light profile, ellipticity, asymmetry) are intertwined and cannot be measured
independently of each other. We present strong arguments in favour of
model-based parametrisation schemes, namely reliability assessment,
disentanglement of morphological observables, and PSF modelling. Furthermore,
we demonstrate that estimates of the concentration and Sersic index obtained
from the Zurich Structure & Morphology catalogue are in excellent agreement
with theoretical predictions. We also demonstrate that the incautious use of
the concentration index for classification purposes can cause a severe loss of
the discriminative information contained in a given data sample. Moreover, we
show that, for poorly resolved galaxies, concentration index and M_20 suffer
from strong discontinuities, i.e. similar morphologies are not necessarily
mapped to neighbouring points in the parameter space. This limits the
reliability of these parameters for classification purposes. Two-dimensional
Sersic profiles accounting for centroid and ellipticity are identified as the
currently most reliable parametrisation scheme in the regime of intermediate
signal-to-noise ratios and resolutions, where asymmetries and substructures do
not play an important role. We argue that basis functions provide good
parametrisation schemes in the regimes of high signal-to-noise ratios and
resolutions. Concerning Sersic profiles, we show that scale radii cannot be
compared directly for profiles of different Sersic indices. Furthermore, we
show that parameter spaces are typically highly nonlinear. This implies that
significant caution is required when distance-based classificaton methods are
used.Comment: 18 pages, 13 figure
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