Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Niall Jeffrey"'
Autor:
Niall Jeffrey, Benjamin D Wandelt
Publikováno v:
Machine Learning: Science and Technology, Vol 5, Iss 1, p 015008 (2024)
Evidence Networks can enable Bayesian model comparison when state-of-the-art methods (e.g. nested sampling) fail and even when likelihoods or priors are intractable or unknown. Bayesian model comparison, i.e. the computation of Bayes factors or evide
Externí odkaz:
https://doaj.org/article/ccdb09302dea48f99a8b5ab82128fd50
Autor:
Nicolas Tessore, Arthur Loureiro, Benjamin Joachimi, Maximilian von Wietersheim-Kramsta, Niall Jeffrey
Publikováno v:
The Open Journal of Astrophysics, Vol 6 (2023)
We present GLASS, the Generator for Large Scale Structure, a new code for the simulation of galaxy surveys for cosmology, which iteratively builds a light cone with matter, galaxies, and weak gravitational lensing signals as a sequence of nested shel
Externí odkaz:
https://doaj.org/article/93513343c8104fe38cbb23b611378ffe
Publikováno v:
Machine Learning: Science and Technology, Vol 4, Iss 4, p 045002 (2023)
We present an approach for using machine learning to automatically discover the governing equations and unknown properties (in this case, masses) of real physical systems from observations. We train a ‘graph neural network’ to simulate the dynami
Externí odkaz:
https://doaj.org/article/8420d649e4c64526bdb4a41d329e0c18
Autor:
Bruno Regaldo-Saint Blancard, François Levrier, Erwan Allys, Niall Jeffrey, François Boulanger, Benjamin D. Wandelt
Publikováno v:
Monthly Notices of the Royal Astronomical Society: Letters
Monthly Notices of the Royal Astronomical Society: Letters, Oxford Journals, In press, 510 (1), pp.L1-L6. ⟨10.1093/mnrasl/slab120⟩
Monthly Notices of the Royal Astronomical Society: Letters, Oxford Journals, In press, 510 (1), pp.L1-L6. ⟨10.1093/mnrasl/slab120⟩
With a single training image and using wavelet phase harmonic augmentation, we present polarized Cosmic Microwave Background (CMB) foreground marginalization in a high-dimensional likelihood-free (Bayesian) framework. We demonstrate robust foreground
Autor:
M. Smith, Daniel Gruen, D. W. Gerdes, F. Paz-Chinchón, D. J. James, J. Carretero, G. Tarle, Ian Harrison, J. Gschwend, Shantanu Desai, Marcos Lima, Kyler Kuehn, M. Carrasco Kind, Ramon Miquel, E. Suchyta, Juan Garcia-Bellido, August E. Evrard, Felipe Menanteau, Daniel Thomas, E. Buckley-Geer, Jennifer L. Marshall, L. Whiteway, Peter Doel, Juan Estrada, M. March, J. DeRose, L. F. Secco, Josh Frieman, V. Scarpine, Oliver Friedrich, Michael Schubnell, H. T. Diehl, Enrique Gaztanaga, Michael Troxel, T. M. C. Abbott, David Bacon, Martin Crocce, Pablo Fosalba, A. Carnero Rosell, Marcelle Soares-Santos, Niall MacCrann, S. Everett, Peter Melchior, David J. Brooks, R. Cawthon, Elisabeth Krause, E. J. Sanchez, Antonella Palmese, M. A. G. Maia, M. E. C. Swanson, A. A. Plazas, D. L. Burke, S. Santiago, J. Annis, I. Sevilla-Noarbe, T. McClintock, S. Allam, Robert A. Gruendl, J. De Vicente, L. N. da Costa, Santiago Avila, Carlos Solans Sanchez, Chihway Chang, Bhuvnesh Jain, M. D. Johnson, Joe Zuntz, Niall Jeffrey, I. Ferrero, Tim Eifler, G. Gutierrez, M. Gatti
Publikováno v:
Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual)
Universidade de São Paulo (USP)
instacron:USP
Universidade de São Paulo (USP)
instacron:USP
We present a simulated cosmology analysis using the second and third moments of the weak lensing mass (convergence) maps. The second moment, or variances, of the convergence as a function of smoothing scale contains information similar to standard sh
Autor:
Filipe B. Abdalla, Niall Jeffrey
Publikováno v:
Monthly Notices of the Royal Astronomical Society. 490:5749-5756
When inferring unknown parameters or comparing different models, data must be compared to underlying theory. Even if a model has no closed-form solution to derive summary statistics, it is often still possible to simulate mock data in order to genera
Publikováno v:
Physical Review D
Physical Review D, American Physical Society, 2021, 103 (2), pp.023009. ⟨10.1103/PhysRevD.103.023009⟩
Physical Review D, American Physical Society, 2021, 103 (2), pp.023009. ⟨10.1103/PhysRevD.103.023009⟩
We use Density Estimation Likelihood-Free Inference, $\Lambda$ Cold Dark Matter simulations of $\sim 2M$ galaxy pairs, and data from Gaia and the Hubble Space Telescope to infer the sum of the masses of the Milky Way and Andromeda (M31) galaxies, the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::42a3f4f823678fa17209327907714aa1
https://hal.archives-ouvertes.fr/hal-02999468/file/hal-02999468.pdf
https://hal.archives-ouvertes.fr/hal-02999468/file/hal-02999468.pdf
Publikováno v:
Astronomy and Astrophysics-A&A
Astronomy and Astrophysics-A&A, EDP Sciences, 2021, 649, pp.A99. ⟨10.1051/0004-6361/202039451⟩
Astronomy and Astrophysics-A&A, 2021, 649, pp.A99. ⟨10.1051/0004-6361/202039451⟩
Astronomy and Astrophysics-A&A, EDP Sciences, 2021, 649, pp.A99. ⟨10.1051/0004-6361/202039451⟩
Astronomy and Astrophysics-A&A, 2021, 649, pp.A99. ⟨10.1051/0004-6361/202039451⟩
We introduce a novel approach to reconstruct dark matter mass maps from weak gravitational lensing measurements. The cornerstone of the proposed method lies in a new modelling of the matter density field in the Universe as a mixture of two components
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fee65f69486835ffd02b3d79d8ffb06b
Publikováno v:
Monthly Notices of the Royal Astronomical Society
Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP): Policy P-Oxford Open Option A, 2021, 501 (1), pp.954-969. ⟨10.1093/mnras/staa3594⟩
Monthly Notices of the Royal Astronomical Society, 2021, 501 (1), pp.954-969. ⟨10.1093/mnras/staa3594⟩
Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP): Policy P-Oxford Open Option A, 2021, 501 (1), pp.954-969. ⟨10.1093/mnras/staa3594⟩
Monthly Notices of the Royal Astronomical Society, 2021, 501 (1), pp.954-969. ⟨10.1093/mnras/staa3594⟩
In many cosmological inference problems, the likelihood (the probability of the observed data as a function of the unknown parameters) is unknown or intractable. This necessitates approximations and assumptions, which can lead to incorrect inference
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a7009f56d88a787a7f5031ad5f1c9e42
https://hal.archives-ouvertes.fr/hal-02959520
https://hal.archives-ouvertes.fr/hal-02959520
Autor:
Sunayana Bhargava, Jennifer L. Marshall, David J. Brooks, Carlos Solans Sanchez, G. Tarle, Samuel Hinton, G. Gutierrez, Alexandra Amon, Peter Doel, Ami Choi, Ben Hoyle, Marco Raveri, B. Flaugher, F. Andrade-Oliveira, Jochen Weller, R. P. Rollins, H. T. Diehl, D. L. Burke, Felipe Menanteau, Alex Drlica-Wagner, J. DeRose, F. J. Castander, David Bacon, E. Suchyta, Dragan Huterer, Christopher J. Conselice, Brian Yanny, Jack Elvin-Poole, U. Demirbozan, Maria E. S. Pereira, Niall MacCrann, Keith Bechtol, Erin Sheldon, Martin Crocce, V. Scarpine, T. M. C. Abbott, Peter Melchior, D. L. Hollowood, S. Serrano, Agnès Ferté, J. De Vicente, J. Cordero, P. F. Leget, A. Campos, Eli S. Rykoff, C. Doux, Chun-Hao To, E. J. Sanchez, K. Herner, F. Paz-Chinchón, J. Carretero, Antonella Palmese, Marcos Lima, J. Myles, I. Harrison, G. Pollina, A. Roodman, David J. James, Tommaso Giannantonio, D. Zeurcher, M. A. G. Maia, M. Rodriguez-Monroy, Joe Zuntz, A. Alarcon, B. Yin, S. Allam, Robert A. Gruendl, A. Kovács, Matthew R. Becker, Pablo Fosalba, B. Mawdsley, Ashley J. Ross, S. Pandey, J. Muir, Joseph J. Mohr, Michael Troxel, I. Ferrero, Matt J. Jarvis, A. Carnero Rosell, J. McCullough, P. Vielzeuf, Yanxi Zhang, Seshadri Nadathur, M. Carrasco Kind, M. March, S. Everett, M. Smith, M. Costanzi, Jean-Luc Starck, Daniel Gruen, I. Tutusaus, Francois Lanusse, Chihway Chang, Daniel Thomas, J. Prat, L. Whiteway, F. Elsner, S. Desai, I. Sevilla-Noarbe, M. Soares-Santos, G. Giannini, W. G. Hartley, D. W. Gerdes, R. Cawthon, N. Kuropatkin, Ramon Miquel, K. D. Eckert, T. N. Varga, L. N. da Costa, J. P. Dietrich, Juan Garcia-Bellido, E. Bertin, E. M. Huff, R. Chen, Michel Aguena, Enrique Gaztanaga, R. L. C. Ogando, Bhuvnesh Jain, M Gatti, Ofer Lahav, Niall Jeffrey, Robert Morgan, L. F. Secco, Nico Hamaus, A. A. Plazas, A. Navarro-Alsina, T. Shin, T. Kacprzak, C. Davis, J. Gschwend, Gary Bernstein
Publikováno v:
Digital.CSIC. Repositorio Institucional del CSIC
instname
Monthly Notices of the Royal Astronomical Society
Monthly Notices of the Royal Astronomical Society, 2021, 505, pp.4626-4645. ⟨10.1093/mnras/stab1495⟩
Mon.Not.Roy.Astron.Soc.
Mon.Not.Roy.Astron.Soc., 2021, 505, pp.4626-4645. ⟨10.1093/mnras/stab1495⟩
Scopus
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
DES Collaboration, Bacon, D, Mawdsley, B & Thomas, D 2021, ' Dark Energy Survey Year 3 results: curved-sky weak lensing mass map reconstruction ', Monthly Notices of the Royal Astronomical Society, vol. 505, no. 3, pp. 4626-4645 . https://doi.org/10.1093/mnras/stab1495
instname
Monthly Notices of the Royal Astronomical Society
Monthly Notices of the Royal Astronomical Society, 2021, 505, pp.4626-4645. ⟨10.1093/mnras/stab1495⟩
Mon.Not.Roy.Astron.Soc.
Mon.Not.Roy.Astron.Soc., 2021, 505, pp.4626-4645. ⟨10.1093/mnras/stab1495⟩
Scopus
Repositório Institucional da UNESP
Universidade Estadual Paulista (UNESP)
instacron:UNESP
DES Collaboration, Bacon, D, Mawdsley, B & Thomas, D 2021, ' Dark Energy Survey Year 3 results: curved-sky weak lensing mass map reconstruction ', Monthly Notices of the Royal Astronomical Society, vol. 505, no. 3, pp. 4626-4645 . https://doi.org/10.1093/mnras/stab1495
Jeffrey, N., et al. DES Collaboration
We present reconstructed convergence maps, mass maps, from the Dark Energy Survey (DES) third year (Y3) weak gravitational lensing data set. The mass maps are weighted projections of the density field (prima
We present reconstructed convergence maps, mass maps, from the Dark Energy Survey (DES) third year (Y3) weak gravitational lensing data set. The mass maps are weighted projections of the density field (prima
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::321054fdaeb2e1eaef6fa2ccfa76e664
http://hdl.handle.net/10261/254855
http://hdl.handle.net/10261/254855