Deep learning improves weak lensing cosmological constraints.
problem Extracting non-Gaussian information from weak lensing data.
method 2D convolutional neural network trained on simulated lensing maps.
result Neural network yields 5x tighter constraints than power spectrum.
Deep Learning improves cosmological parameter estimation from weak lensing mass maps.
problem Distinguishing between five cosmological models along the σ8 - Ωm degeneracy.
method Design and implementation of a Deep Convolutional Neural Network (DCNN) trained on weak lensing mass maps.
result DCNN outperforms traditional non-Gaussian statistics (skewness and kurtosis) in high noise conditions.
Enhances weak lensing inference with neural summaries.
problem Extracting additional information from weak lensing convergence maps.
method Hybrid approach combining physics-based and neural summaries.
result Neural summaries extract up to 8 times more information than angular power spectra.
Deep learning reduces noise in weak lensing mass maps using GANs.
problem Noise reduction in weak lensing mass maps.
method Generative adversarial networks (GANs) applied to Subaru Hyper Suprime-Cam data.
result GANs successfully reproduce non-Gaussian information in denoised maps, showing stronger cosmological dependence.
Deep models predict gas properties from dark matter to aid cosmological simulations.
problem Computational challenges in running hydrodynamical simulations for large-scale structure and baryonic probes.
method Trained variational auto-encoders and generative adversarial networks on BAHAMAS hydrodynamical simulation data to map matter density to gas pressure.
result Generated tSZ maps are statistically consistent with those from BAHAMAS, enabling SLICS for tSZ covariance estimation.
New emulator bridges simulators using conditional optimal transport.
problem Bridging simulators with minimal distortion.
method Flow-based approach to learn likelihood transport, COT-FM for optimal matching.
result Emulator accurately captures full correction between simulators.
GANs create realistic galaxy images for astronomy.
problem Handling large astronomical datasets.
method Chained generative adversarial networks (GANs).
result GAN-generated galaxy images closely match real galaxies.
In the Friedmann Model of the universe, cosmologists assume that spacelike slices of the universe are Riemannian manifolds of constant sectional curvature. This assumption is justified via Schur's Theorem by stating that the spacelike universe is locally isotropic. Here we define a Riemannian manifold as almost locally…
Machine learning helps infer dark matter substructure from strong lensing images.
problem Extracting information about dark matter substructure from strong lensing images is challenging.
method Simulation-based inference techniques and neural networks trained on simulator data.
result Efficiently trained neural networks can estimate likelihood ratios for substructure parameters.
Method generates joint posterior samples of source and foreground mass distributions for gravitational lensing.
problem Challenging inference problem for high-resolution, high signal-to-noise ratio gravitational lensing.
method Combines diffusion-based generative modeling and recurrent inference machines.
result Can model realistic gravitational lensing simulations down to the noise level.
Deep neural networks improve CMB lensing potential reconstruction for future cosmic microwave background experiments.
problem Low noise levels in upcoming CMB experiments require improved methods for extracting lensing potential.
method Deep convolutional neural networks (ResUNet) trained on simulated data without physical parametrization.
result ResUNets recover lensing potential with higher signal-to-noise ratio than quadratic estimator.
New equations reveal how cylinder power in progressive lenses depends on geodesic curvature.
problem Current understanding of cylinder power in progressive lenses is incomplete.
method Derived complete compatibility equations for spatially-varying curvature surfaces.
result Cylinder power depends on geodesic curvature, not just principal curvature.
ResUNet-CMB neural network reconstructs CMB effects from noisy data.
problem Reconstructing CMB anisotropies from noisy data.
method Convolutional neural network (ResUNet-CMB) for simultaneous reconstruction of lensing and reionization.
result ResUNet-CMB outperforms quadratic estimators at low noise levels and avoids lensing-induced bias.
Estimates how many times a star appears due to gravitational lensing.
problem Estimating the number of times an observer sees a star due to gravitational lensing.
method Use affine linking numbers to estimate the number of times an observer sees a star.
result Estimates the number of times an observer sees a star due to gravitational lensing.
Paper uses ResUNet-CMB to reconstruct cosmic polarization rotation from CMB data.
problem Reconstructing anisotropic cosmic polarization rotation from CMB data.
method Extended ResUNet-CMB to handle gravitational lensing and patchy reionization.
result ResUNet-CMB outperforms standard quadratic estimator in reconstructing all three effects.
Machine learning improves cosmic shear measurements by compensating for feature noise.
problem Accurately measuring cosmic shear from galaxy images in the presence of various nuisance effects.
method Supervised machine learning with artificial neural networks trained on simulated data.
result Demonstrated competitive low shear biases in Euclid-like images.
We propose flow-based likelihoods to accurately capture non-Gaussian data.
problem Bypassing the Gaussian assumption in scientific analyses.
method Use optimization targets of flow-based generative models to reconstruct likelihoods.
result Flow-based likelihoods can accurately capture non-Gaussian data, improving parameter constraints.
Deep learning helps remove secondary B-mode polarization to detect primordial gravitational waves.
problem Removing secondary B-mode polarization from CMB data to detect primordial gravitational waves. method Applied deep learning (ResUNet-CMB) to estimate and remove multiple sources of secondary B-mode polarization. result Deep learning can produce nearly optimal, unbiased estimates of the amplitude of primordial gravitational waves.
A fast deep-learning PSF model accurately reproduces SDSS PSF.
problem Accurate modeling of Point Spread Function (PSF) for wide-field surveys.
method Fast deep-learning approach to estimate PSF parameters from noisy images.
result The model accurately reproduces SDSS PSF at the pixel level.
New model reduces bias in cosmic shear measurements.
problem Bias in cosmic shear measurements due to non-well-defined ellipticity.
method Hybrid physical and deep learning Hierarchical Bayesian Model.
result Unbiased estimate of shear on realistic galaxies.
TopoFisher learns topological summaries by maximizing Fisher information, improving parameter efficiency and inference quality.
problem Simulation-based inference misses key information in low-order statistics, especially for non-Gaussian fields.
method TopoFisher uses a differentiable persistent-homology pipeline that learns topological summaries by maximizing local Gaussian Fisher information.
result TopoFisher recovers much of the available information and outperforms fixed topological vectorizations in weak gravitational lensing.
We provide a geometric explanation for the existence of magnification relations for the A, D, E family of caustic singularities, which were established in recent work. In particular, it was shown that for families of general mappings between planes exhibiting any of these caustic singularities, and for any non-caustic …
New method estimates MTF from photos without expensive equipment.
problem Costly MTF measurement limits lens performance evaluation.
method Custom grid display for ground truth, CNN for MTF estimation.
result Estimates MTF from natural images, generalizes to unseen lenses.
Machine learning detects subhalos in lensed images with high accuracy and low false positives.
problem Detecting substructure in strongly lensed images.
method Developed a neural network for image segmentation to locate and mass estimate subhalos.
result The network can detect subhalos with masses m≳108.5M⊙ and measure the subhalo mass function. The paper solves the isoperimetric problem in Riemannian optical geometry, proving circles minimize lengths with area constraints.
problem Optical geometry of static spherically symmetric spacetimes.
method Applying isoperimetric problem results to curves in Riemannian optical geometry.
result Length-minimizing curves with area constraints are circles, with implications for photon spheres.
Unified approach for sample aggregation in transfer learning across various divergence measures.
problem Optimizing sample aggregation from source to target distributions for improved target performance.
method Unified algorithmic approach that adapts to multiple divergence measures via a weak modulus of transfer.
result Unified approach achieves near optimal rates in terms of the unknown strong modulus, applicable in more general settings.
Introduces CSLC models to bridge deep generative models and classical algorithms.
problem Mode collapse and memorization issues in deep generative models and restrictive assumptions in classical algorithms.
method Introduces conditionally strongly log-concave (CSLC) models, factorizing data distribution into strongly log-concave conditional distributions.
result Efficient parameter estimation and sampling algorithms with theoretical guarantees for non-log-concave data distributions.
Study of stationary vacuum black holes in 5D spacetime.
problem Asymptotically flat bi-axially symmetric stationary solutions of the vacuum Einstein equations.
method Solving axially symmetric harmonic map equations from R^3 into SL(3,R)/SO(3).
result First candidates for smooth vacuum non-degenerate black lenses produced.
Co-eye combines multiple symbolic representations to improve time series classification accuracy.
problem Challenges in time series classification due to domain diversity.
method Inspired by compound eyes, Co-eye uses multiple symbolic representations and hyper-parameterised lenses to classify time series data.
result Co-eye outperforms state-of-the-art techniques in accuracy and robustness across various domains.
First we review the definition of a negative point mass singularity. Then we examine the gravitational lensing effects of these singularities in isolation and with shear and convergence from continuous matter. We review the Inverse Mean Curvature Flow and use this flow to prove some new results about the mass of a sing…
We pursue a geometrical approach to gravitational lensing theory. We present a survey of the background theory of General Relativity, including particular properties of the Schwarzschild and Kerr solutions. Next we outline a proof of the Gauss Bonnet theorem and its applications to surfaces in optical geometry, as deve…
Vogt's theorem, concerning boundary angles of a convex arc with monotonic curvature (spiral arc), is taken as a starting point to establish basic properties of spirals. The theorem is expanded by removing requirements of convexity and curvature continuity; the cases of inflection and multiple windings are considered. P…
Evidence Networks simplify Bayesian model comparison for complex models.
problem Bayesian model comparison challenges with intractable likelihoods or priors.
method Loss functions and neural networks for fast, amortized estimation of Bayes factors.
result Evidence Networks provide accurate and scalable Bayes factor estimation.
New system studies trapped light paths in Euclidean space.
problem Trapping of light paths in Euclidean space with negative refractive index.
method Introduces wind-tree tiling billiards system to study trajectories of rays in Euclidean space with rectangular obstacles.
result Almost every configuration of the system traps trajectories with initial vertical direction in an infinite strip.
Rubin LSST DESC uses AI/ML for dark energy research.
problem Challenges in uncertainty quantification and model robustness for AI/ML in DESC.
method Bayesian inference, physics-informed methods, validation frameworks, active learning.
result AI/ML methods are essential but require rigorous evaluation and governance.
We give a characterization of critical points that allows us to define a metric invariant on all Riemannian manifolds M with a lower sectional curvature bound and an upper radius bound. We show there is a uniform upper volume bound for all such manifolds with an upper bound on this invariant. We generalize results by…
A bug's perspective on polyhedral surfaces using exponential maps.
problem Understanding visual effects on polyhedral surfaces from a bug's viewpoint.
method Computed the exponential map of a polyhedron by cutting and rotating faces into the tangent plane of the bug.
result Visual effects like lensing and cloaking can be explained using the exponential map.
Network Lens identifies node behaviors in heterogeneous networks with high accuracy.
problem Identifying different behaviors in various parts of large heterogeneous networks.
method Zoom into network using different-sized lenses to capture local structure, weight signatures to predict node labels.
result Achieved a peak accuracy of ~42% on two networks with ~100,000 and ~1,000,000 nodes, significantly better than random.
New black hole solutions with lens space horizons in 5D Kaluza-Klein theory.
problem Finding black hole solutions with specific horizon topologies.
method Formally asymptotically flat black hole solutions constructed through Kaluza-Klein reduction.
result Explicit construction of regular black hole solutions with L(p,q) horizons. Reinterprets Granger causality with causal Bayesian networks and Reichenbach's principles.
problem Lack of a rigorous causal foundation in Granger causality.
method Reinterpreting Granger causality through Reichenbach's principles and causal Bayesian networks, implementing as c-GC.
result c-GC provides a more principled framework for causal discovery in observational datasets.
We discuss general notions of metrics and of Finsler structures which we call weak metrics and weak Finsler structures. Any convex domain carries a canonical weak Finsler structure, which we call its tautological weak Finsler structure. We compute distances in the tautological weak Finsler structure of a domain and we …
Paper studies DRO with MMD uncertainty sets and reveals connections to regularization and generalization.
problem Addressing the limitations of existing DRO uncertainty sets in machine learning.
method Introduces DRO with uncertainty sets measured via maximum mean discrepancy (MMD) and derives connections to regularization and generalization.
result Obtains an alternative proof of a generalization bound for Gaussian kernel ridge regression via DRO lens and suggests a new regularizer.
New structures defined for studying contact foliations and their geometry.
problem Understanding dynamics of contact foliations and their applications.
method Define and study weak nearly S- and weak nearly C-structures.
result Characterize weak nearly S- and weak nearly C- submanifolds in weak nearly Kähler manifolds.
Tensoring p-weak differentiable structures preserves their properties.
problem Tensorization of p-weak differentiable structures. method Proving the product of p-weak charts is a p-weak chart, and showing isometric embeddings. result Tensorization of p-weak differentiable structures is possible under certain conditions. The study examines conditions for weak nearly cosymplectic manifolds to split into products.
problem Understanding the curvature and topology of weak nearly cosymplectic manifolds.
method Analyzes the conditions for splitting and characterizes specific manifolds.
result Conditions for weak nearly cosymplectic manifolds to become Riemannian products are identified.
Subset selection improves weak supervision performance.
problem Optimizing the use of weakly-labeled data.
method Combining pretrained data representations with the cut statistic for subset selection.
result Subset selection improves weak supervision performance by up to 19%.
Defines weak geodesics on specific subsets of manifolds.
problem Characterizing geodesics on prox-regular subsets of Riemannian manifolds.
method Defining weak geodesics as continuous curves with weak regularities, and characterizing them as viscosity critical points of the energy functional.
result Characterizes weak geodesics on prox-regular subsets of Riemannian manifolds.
Framework synthesizes programs for simulating complex models and estimating parameters.
problem Parameter estimation for complex models requires manual encoding of fixed model structures.
method Combines LLMs for program synthesis with neural simulation-based inference.
result Identifies plausible model families from open-ended prompts with high accuracy.