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Found 24 papers, 14 papers with code
Differentiable and accelerated wavelet transforms on the sphere and ball
We observe up to a $300$-fold and $21800$-fold acceleration for signals on the sphere and ball, respectively, compared to existing software, whilst maintaining 64-bit machine precision.
Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging
This work proposes a method coined QuantifAI to address UQ in radio-interferometric imaging with data-driven (learned) priors for high-dimensional settings.
Differentiable and accelerated spherical harmonic and Wigner transforms
We develop novel algorithmic structures for accelerated and differentiable computation of generalised Fourier transforms on the sphere $\mathbb{S}^2$ and rotation group $\text{SO}(3)$, i. e. spherical harmonic and Wigner transforms, respectively.
Learned harmonic mean estimation of the marginal likelihood with normalizing flows
The learned harmonic mean estimator solves the exploding variance problem of the original harmonic mean estimation of the marginal likelihood.
Proximal nested sampling with data-driven priors for physical scientists
Proximal nested sampling was introduced recently to open up Bayesian model selection for high-dimensional problems such as computational imaging.
The Tiny Time-series Transformer: Low-latency High-throughput Classification of Astronomical Transients using Deep Model Compression
We also show that in addition to the deep compression techniques, careful choice of file formats can improve inference latency, and thereby throughput of alerts, on the order of $8\times$ for local processing, and $5\times$ in a live production setting.
Learned Interferometric Imaging for the SPIDER Instrument
These approaches use deep learning to learn prior information from training data, increasing the reconstruction quality, and significantly reducing the computation time required to recover images by orders of magnitude.
Scalable and Equivariant Spherical CNNs by Discrete-Continuous (DISCO) Convolutions
For 4k spherical images we realize a saving of $10^9$ in computational cost and $10^4$ in memory usage when compared to the most efficient alternative equivariant spherical convolution.
Paying Attention to Astronomical Transients: Introducing the Time-series Transformer for Photometric Classification
Recent efforts have sought to leverage machine learning methods to tackle the challenge of astronomical transient classification, with ever improving success.
Scattering Networks on the Sphere for Scalable and Rotationally Equivariant Spherical CNNs
Convolutional neural networks (CNNs) constructed natively on the sphere have been developed recently and shown to be highly effective for the analysis of spherical data.
Multiscale Optimal Filtering on the Sphere
We present a framework for the optimal filtering of spherical signals contaminated by realizations of an additive, zero-mean, uncorrelated and anisotropic noise process on the sphere.
Efficient Generalized Spherical CNNs
We develop two new strictly equivariant layers with reduced complexity $\mathcal{O}(CL^4)$ and $\mathcal{O}(CL^3 \log L)$, making larger, more expressive models computationally feasible.
Evaluating machine learning techniques for predicting power spectra from reionization simulations
We then present a proof-of-concept technique for mapping between two different simulations, exploiting our best emulator's fast prediction speed.
Cosmology and Nongalactic Astrophysics
The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Data set
The Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC) is an open data challenge to classify simulated astronomical time-series data in preparation for observations from the Large Synoptic Survey Telescope (LSST), which will achieve first light in 2019 and commence its 10-year main survey in 2022.
Instrumentation and Methods for Astrophysics Solar and Stellar Astrophysics
A fast and exact $w$-stacking and $w$-projection hybrid algorithm for wide-field interferometric imaging
We show that the same calculation can be performed with a radially symmetric $w$-projection kernel, where we use one dimensional adaptive quadrature to calculate the resulting Hankel transform, decreasing the computation required for kernel generation by several orders of magnitude, whilst preserving the accuracy.
Instrumentation and Methods for Astrophysics
Science-Driven Optimization of the LSST Observing Strategy
The Large Synoptic Survey Telescope is designed to provide an unprecedented optical imaging dataset that will support investigations of our Solar System, Galaxy and Universe, across half the sky and over ten years of repeated observation.
Instrumentation and Methods for Astrophysics Cosmology and Nongalactic Astrophysics Earth and Planetary Astrophysics Astrophysics of Galaxies Solar and Stellar Astrophysics
An Optimal Dimensionality Multi-shell Sampling Scheme with Accurate and Efficient Transforms for Diffusion MRI
The sampling scheme uses an optimal number of samples, equal to the degrees of freedom of the band-limited diffusion signal in the SPF domain, and allows for computationally efficient reconstruction.
Robust sparse image reconstruction of radio interferometric observations with purify
However, they produce reconstructed inter\-ferometric images that are limited in quality and scalability for big data.
Instrumentation and Methods for Astrophysics
Wavelet-Based Segmentation on the Sphere
Wavelets on the sphere have been developed to solve such problems for data defined on the sphere, which arise in numerous fields such as cosmology and geophysics.
Second-Generation Curvelets on the Sphere
We present a new second-generation curvelet transform, where scale-discretised curvelets are constructed directly on the sphere.
Information Theory Instrumentation and Methods for Astrophysics Information Theory
Scale-discretised ridgelet transform on the sphere
The restriction to antipodal signals is expected since the spherical Radon and ridgelet transforms themselves result in signals that exhibit antipodal symmetry.
Information Theory Information Theory
Directional spin wavelets on the sphere
We construct a directional spin wavelet framework on the sphere by generalising the scalar scale-discretised wavelet transform to signals of arbitrary spin.
Information Theory Instrumentation and Methods for Astrophysics Information Theory
Sparsity averaging for radio-interferometric imaging
We propose a novel regularization method for compressive imaging in the context of the compressed sensing (CS) theory with coherent and redundant dictionaries.
PURIFY: a new approach to radio-interferometric imaging
This approach was shown, in theory and through simulations in a simple discrete visibility setting, to have the potential to outperform significantly CLEAN and its evolutions.
Instrumentation and Methods for Astrophysics
