You need to log in to edit.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

1 code implementation • 2 Feb 2024 • Matthew A. Price, Alicja Polanska, Jessica Whitney, Jason D. McEwen

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.

1 code implementation • 30 Nov 2023 • Tobías I. Liaudat, Matthijs Mars, Matthew A. Price, Marcelo Pereyra, Marta M. Betcke, Jason D. McEwen

This work proposes a method coined QuantifAI to address UQ in radio-interferometric imaging with data-driven (learned) priors for high-dimensional settings.

1 code implementation • 24 Nov 2023 • Matthew A. Price, Jason D. McEwen

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.

1 code implementation • 30 Jun 2023 • Jason D. McEwen, Tobías I. Liaudat, Matthew A. Price, Xiaohao Cai, Marcelo Pereyra

Proximal nested sampling was introduced recently to open up Bayesian model selection for high-dimensional problems such as computational imaging.

1 code implementation • 30 Jun 2023 • Alicja Polanska, Matthew A. Price, Alessio Spurio Mancini, Jason D. McEwen

The learned harmonic mean estimator solves the exploding variance problem of the original harmonic mean estimation of the marginal likelihood.

1 code implementation • 15 Mar 2023 • Tarek Allam Jr., Julien Peloton, Jason D. McEwen

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.

1 code implementation • 24 Jan 2023 • Matthijs Mars, Marta M. Betcke, Jason D. McEwen

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.

no code implementations • 27 Sep 2022 • Jeremy Ocampo, Matthew A. Price, Jason D. McEwen

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.

2 code implementations • 13 May 2021 • Tarek Allam Jr., Jason D. McEwen

Recent efforts have sought to leverage machine learning methods to tackle the challenge of astronomical transient classification, with ever improving success.

no code implementations • ICLR 2022 • Jason D. McEwen, Christopher G. R. Wallis, Augustine N. Mavor-Parker

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.

no code implementations • 15 Oct 2020 • Adeem Aslam, Zubair Khalid, Jason D. McEwen

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.

1 code implementation • ICLR 2021 • Oliver J. Cobb, Christopher G. R. Wallis, Augustine N. Mavor-Parker, Augustin Marignier, Matthew A. Price, Mayeul d'Avezac, Jason D. McEwen

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.

no code implementations • 22 Nov 2018 • William D. Jennings, Catherine A. Watkinson, Filipe B. Abdalla, Jason D. McEwen

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

3 code implementations • 28 Sep 2018 • The PLAsTiCC team, Tarek Allam Jr., Anita Bahmanyar, Rahul Biswas, Mi Dai, Lluís Galbany, Renée Hložek, Emille E. O. Ishida, Saurabh W. Jha, David O. Jones, Richard Kessler, Michelle Lochner, Ashish A. Mahabal, Alex I. Malz, Kaisey S. Mandel, Juan Rafael Martínez-Galarza, Jason D. McEwen, Daniel Muthukrishna, Gautham Narayan, Hiranya Peiris, Christina M. Peters, Kara Ponder, Christian N. Setzer, The LSST Dark Energy Science Collaboration, The LSST Transients, Variable Stars Science Collaboration

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

1 code implementation • 24 Jul 2018 • Luke Pratley, Melanie Johnston-Hollitt, Jason D. McEwen

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

1 code implementation • 14 Aug 2017 • LSST Science Collaboration, Phil Marshall, Timo Anguita, Federica B. Bianco, Eric C. Bellm, Niel Brandt, Will Clarkson, Andy Connolly, Eric Gawiser, Zeljko Ivezic, Lynne Jones, Michelle Lochner, Michael B. Lund, Ashish Mahabal, David Nidever, Knut Olsen, Stephen Ridgway, Jason Rhodes, Ohad Shemmer, David Trilling, Kathy Vivas, Lucianne Walkowicz, Beth Willman, Peter Yoachim, Scott Anderson, Pierre Antilogus, Ruth Angus, Iair Arcavi, Humna Awan, Rahul Biswas, Keaton J. Bell, David Bennett, Chris Britt, Derek Buzasi, Dana I. Casetti-Dinescu, Laura Chomiuk, Chuck Claver, Kem Cook, James Davenport, Victor Debattista, Seth Digel, Zoheyr Doctor, R. E. Firth, Ryan Foley, Wen-fai Fong, Lluis Galbany, Mark Giampapa, John E. Gizis, Melissa L. Graham, Carl Grillmair, Phillipe Gris, Zoltan Haiman, Patrick Hartigan, Suzanne Hawley, Renee Hlozek, Saurabh W. Jha, C. Johns-Krull, Shashi Kanbur, Vassiliki Kalogera, Vinay Kashyap, Vishal Kasliwal, Richard Kessler, Alex Kim, Peter Kurczynski, Ofer Lahav, Michael C. Liu, Alex Malz, Raffaella Margutti, Tom Matheson, Jason D. McEwen, Peregrine McGehee, Soren Meibom, Josh Meyers, Dave Monet, Eric Neilsen, Jeffrey Newman, Matt O'Dowd, Hiranya V. Peiris, Matthew T. Penny, Christina Peters, Radoslaw Poleski, Kara Ponder, Gordon Richards, Jeonghee Rho, David Rubin, Samuel Schmidt, Robert L. Schuhmann, Avi Shporer, Colin Slater, Nathan Smith, Marcelles Soares-Santos, Keivan Stassun, Jay Strader, Michael Strauss, Rachel Street, Christopher Stubbs, Mark Sullivan, Paula Szkody, Virginia Trimble, Tony Tyson, Miguel de Val-Borro, Stefano Valenti, Robert Wagoner, W. Michael Wood-Vasey, Bevin Ashley Zauderer

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

no code implementations • 20 Apr 2017 • Alice P. Bates, Zubair Khalid, Jason D. McEwen, Rodney A. Kennedy

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.

1 code implementation • 7 Oct 2016 • Luke Pratley, Jason D. McEwen, Mayeul d'Avezac, Rafael E. Carrillo, Alexandru Onose, Yves Wiaux

However, they produce reconstructed inter\-ferometric images that are limited in quality and scalability for big data.

Instrumentation and Methods for Astrophysics

no code implementations • 21 Sep 2016 • Xiaohao Cai, Christopher G. R. Wallis, Jennifer Y. H. Chan, Jason D. McEwen

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.

no code implementations • 17 Nov 2015 • Jennifer Y. H. Chan, Boris Leistedt, Thomas D. Kitching, Jason D. McEwen

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

no code implementations • 6 Oct 2015 • Jason D. McEwen, Matthew A. Price

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

no code implementations • 22 Sep 2015 • Jason D. McEwen, Boris Leistedt, Martin Büttner, Hiranya V. Peiris, Yves Wiaux

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

no code implementations • 11 Feb 2014 • Rafael E. Carrillo, Jason D. McEwen, Yves Wiaux

We propose a novel regularization method for compressive imaging in the context of the compressed sensing (CS) theory with coherent and redundant dictionaries.

2 code implementations • 16 Jul 2013 • Rafael E. Carrillo, Jason D. McEwen, Yves Wiaux

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

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.