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Automated per-instance algorithm selection and configuration have shown promising performances for a number of classic optimization problems, including satisfiability, AI planning, and TSP. The techniques often rely on a set of features…

Neural and Evolutionary Computing · Computer Science 2020-10-01 Tome Eftimov , Gorjan Popovski , Quentin Renau , Peter Korosec , Carola Doerr

CMB lensing from current and upcoming wide-field CMB experiments such as AdvACT, SPT-3G and Simons Observatory relies heavily on temperature (vs. polarization). In this regime, foreground contamination to the temperature map produces…

Cosmology and Nongalactic Astrophysics · Physics 2019-05-20 Emmanuel Schaan , Simone Ferraro

We introduce an exact Bayesian approach to search for non-Gaussianity of local type in Cosmic Microwave Background (CMB) radiation data. Using simulated CMB temperature maps, the newly developed technique is compared against the…

Cosmology and Nongalactic Astrophysics · Physics 2010-11-15 Franz Elsner , Benjamin D. Wandelt

Clustering is essential in data analysis and machine learning, but traditional algorithms like $k$-means and Gaussian Mixture Models (GMM) often fail with nonconvex clusters. To address the challenge, we introduce the Flexible Bivariate…

Machine Learning · Computer Science 2025-02-28 Yung-Peng Hsu , Hung-Hsuan Chen

We propose a computationally feasible estimator for the needlet trispectrum, which develops earlier work on the bispectrum by Donzelli et al. (2012). Our proposal seems to enjoy a number of useful properties, in particular a) the…

Cosmology and Nongalactic Astrophysics · Physics 2014-07-03 Antonino Troja , Simona Donzelli , Davide Maino , Domenico Marinucci

We present a Gibbs sampling solution to the map-making problem for CMB measurements, building on existing destriping methodology. Gibbs sampling breaks the computationally heavy destriping problem into two separate steps; noise filtering…

The Fisher information matrix of the cosmic microwave background (CMB) radiation power spectrum coefficients is a fundamental quantity that specifies the information content of a CMB experiment. In the most general case, its exact…

Cosmology and Nongalactic Astrophysics · Physics 2012-03-30 Franz Elsner , Benjamin D. Wandelt

Randomized iterative algorithms, such as the randomized Kaczmarz method, have gained considerable popularity due to their efficacy in solving matrix-vector and matrix-matrix regression problems. Our present work leverages the insights…

We describe a fast and accurate method for estimation of the cosmic microwave background (CMB) anisotropy angular power spectrum --- Monte Carlo Apodised Spherical Transform EstimatoR. Originally devised for use in the interpretation of the…

Astrophysics · Physics 2011-08-11 E. Hivon , K. M. Gorski , C. B. Netterfield , B. P. Crill , S. Prunet , F. Hansen

High precision measurements of the Cosmic Microwave Background (CMB) anisotropies, as can be expected from the Planck satellite, will require high-accuracy theoretical predictions as well. One possible source of theoretical uncertainty is…

Cosmology and Nongalactic Astrophysics · Physics 2009-11-13 Jan Hamann , Amedeo Balbi , Julien Lesgourgues , Claudia Quercellini

The next generation of CMB experiments should get a better handle on cosmological parameters by mapping the weak lensing deflection field, which is separable from primary anisotropies thanks to the non-Gaussianity induced by lensing.…

Astrophysics · Physics 2009-11-10 Julien Lesgourgues , Michele Liguori , Sabino Matarrese , Antonio Riotto

Hyperspectral remote sensing is a prominent research topic in data processing. Most of the spectral unmixing algorithms are developed by adopting the linear mixing models. Nonnegative matrix factorization (NMF) and its developments are used…

Computer Vision and Pattern Recognition · Computer Science 2018-12-31 Sara Khoshsokhan , Roozbeh Rajabi , Hadi Zayyani

An improved estimator for the amplitude fnl of local-type non-Gaussianity from the cosmic microwave background (CMB) bispectrum is discussed. The standard estimator is constructed to be optimal in the zero-signal (i.e., Gaussian) limit.…

Cosmology and Nongalactic Astrophysics · Physics 2015-06-12 Tristan L. Smith , Daniel Grin , Marc Kamionkowski

Estimation of the angular power spectrum of the Cosmic Microwave Background (CMB) on a small patch of sky is usually plagued by serious spectral leakage, specially when the map has a hard edge. Even on a full sky map, point source masks can…

Astrophysics · Physics 2009-11-06 Sudeep Das , Amir Hajian , David N. Spergel

Matrix factorization methods are important tools in data mining and analysis. They can be used for many tasks, ranging from dimensionality reduction to visualization. In this paper we concentrate on the use of matrix factorizations for…

Machine Learning · Computer Science 2017-07-28 Sanjar Karaev , Pauli Miettinen

Future experiments will produce high-resolution temperature maps of the cosmic microwave background (CMB) and are expected to reveal the signature of gravitational lensing by intervening large-scale structures. We construct all-sky…

Astrophysics · Physics 2009-11-07 Christopher M. Hirata , Uros Seljak

We explore the potential of the B-mode anisotropies of the Cosmic Microwave Background radiation (CMB) to constrain the shape of the primordial tensor power spectrum in a model-independent way. We expand possible perturbations to the…

Cosmology and Nongalactic Astrophysics · Physics 2019-04-09 M. Farhang , A. Vafaei Sadr

On the arcminute angular scales probed by Planck, the CMB anisotropies are gently perturbed by gravitational lensing. Here we present a detailed study of this effect, detecting lensing independently in the 100, 143, and 217GHz frequency…

Cosmology and Nongalactic Astrophysics · Physics 2015-06-15 Planck Collaboration , P. A. R. Ade , N. Aghanim , C. Armitage-Caplan , M. Arnaud , M. Ashdown , F. Atrio-Barandela , J. Aumont , C. Baccigalupi , A. J. Banday , R. B. Barreiro , J. G. Bartlett , S. Basak , E. Battaner , K. Benabed , A. Benoît , A. Benoit-Lévy , J. -P. Bernard , M. Bersanelli , P. Bielewicz , J. Bobin , J. J. Bock , A. Bonaldi , L. Bonavera , J. R. Bond , J. Borrill , F. R. Bouchet , M. Bridges , M. Bucher , C. Burigana , R. C. Butler , J. -F. Cardoso , A. Catalano , A. Challinor , A. Chamballu , L. -Y Chiang , H. C. Chiang , P. R. Christensen , S. Church , D. L. Clements , S. Colombi , L. P. L. Colombo , F. Couchot , A. Coulais , B. P. Crill , A. Curto , F. Cuttaia , L. Danese , R. D. Davies , R. J. Davis , P. de Bernardis , A. de Rosa , G. de Zotti , T. Déchelette , J. Delabrouille , J. -M. Delouis , F. -X. Désert , C. Dickinson , J. M. Diego , H. Dole , S. Donzelli , O. Doré , M. Douspis , J. Dunkley , X. Dupac , G. Efstathiou , T. A. Enßlin , H. K. Eriksen , F. Finelli , O. Forni , M. Frailis , E. Franceschi , S. Galeotta , K. Ganga , M. Giard , G. Giardino , Y. Giraud-Héraud , J. González-Nuevo , K. M. Górski , S. Gratton , A. Gregorio , A. Gruppuso , J. E. Gudmundsson , F. K. Hansen , D. Hanson , D. Harrison , S. Henrot-Versillé , C. Hernández-Monteagudo , D. Herranz , S. R. Hildebrandt , E. Hivon , S. Ho , M. Hobson , W. A. Holmes , A. Hornstrup , W. Hovest , K. M. Huffenberger , T. R. Jaffe , A. H. Jaffe , W. C. Jones , M. Juvela , E. Keihänen , R. Keskitalo , T. S. Kisner , R. Kneissl , J. Knoche , L. Knox , M. Kunz , H. Kurki-Suonio , G. Lagache , A. Lähteenmäki , J. -M. Lamarre , A. Lasenby , R. J. Laureijs , A. Lavabre , C. R. Lawrence , J. P. Leahy , R. Leonardi , J. León-Tavares , J. Lesgourgues , A. Lewis , M. Liguori , P. B. Lilje , M. Linden-Vørnle , M. López-Caniego , P. M. Lubin , J. F. Macías-Pérez , B. Maffei , D. Maino , N. Mandolesi , A. Mangilli , M. Maris , D. J. Marshall , P. G. Martin , E. Martínez-González , S. Masi , S. Matarrese , F. Matthai , P. Mazzotta , A. Melchiorri , L. Mendes , A. Mennella , M. Migliaccio , S. Mitra , M. -A. Miville-Deschênes , A. Moneti , L. Montier , G. Morgante , D. Mortlock , A. Moss , D. Munshi , P. Naselsky , F. Nati , P. Natoli , C. B. Netterfield , H. U. Nørgaard-Nielsen , F. Noviello , D. Novikov , I. Novikov , S. Osborne , C. A. Oxborrow , F. Paci , L. Pagano , F. Pajot , D. Paoletti , B. Partridge , F. Pasian , G. Patanchon , O. Perdereau , L. Perotto , F. Perrotta , F. Piacentini , M. Piat , E. Pierpaoli , D. Pietrobon , S. Plaszczynski , E. Pointecouteau , G. Polenta , N. Ponthieu , L. Popa , T. Poutanen , G. W. Pratt , G. Prézeau , S. Prunet , J. -L. Puget , A. R. Pullen , J. P. Rachen , R. Rebolo , M. Reinecke , M. Remazeilles , C. Renault , S. Ricciardi , T. Riller , I. Ristorcelli , G. Rocha , C. Rosset , G. Roudier , M. Rowan-Robinson , J. A. Rubiño-Martín , B. Rusholme , M. Sandri , D. Santos , G. Savini , D. Scott , M. D. Seiffert , E. P. S. Shellard , L. D. Spencer , J. -L. Starck , V. Stolyarov , R. Stompor , R. Sudiwala , R. Sunyaev , F. Sureau , D. Sutton , A. -S. Suur-Uski , J. -F. Sygnet , J. A. Tauber , D. Tavagnacco , L. Terenzi , L. Toffolatti , M. Tomasi , M. Tristram , M. Tucci , J. Tuovinen , G. Umana , L. Valenziano , J. Valiviita , B. Van Tent , P. Vielva , F. Villa , N. Vittorio , L. A. Wade , B. D. Wandelt , M. White , S. D. M. White , D. Yvon , A. Zacchei , A. Zonca

We study the problem of likelihood maximization when the likelihood function is intractable but model simulations are readily available. We propose a sequential, gradient-based optimization method that directly models the Fisher score based…

Machine Learning · Statistics 2025-06-10 Sherman Khoo , Yakun Wang , Song Liu , Mark Beaumont