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  • 1
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2020
    In:  Monthly Notices of the Royal Astronomical Society Vol. 492, No. 4 ( 2020-03-11), p. 5023-5029
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP), Vol. 492, No. 4 ( 2020-03-11), p. 5023-5029
    Abstract: We present the first reconstruction of dark matter maps from weak lensing observational data using deep learning. We train a convolution neural network with a U-Net-based architecture on over 3.6 × 105 simulated data realizations with non-Gaussian shape noise and with cosmological parameters varying over a broad prior distribution. We interpret our newly created dark energy survey science verification (DES SV) map as an approximation of the posterior mean P(κ|γ) of the convergence given observed shear. Our DeepMass1 method is substantially more accurate than existing mass-mapping methods. With a validation set of 8000 simulated DES SV data realizations, compared to Wiener filtering with a fixed power spectrum, the DeepMass method improved the mean square error (MSE) by 11 per cent. With N-body simulated MICE mock data, we show that Wiener filtering, with the optimal known power spectrum, still gives a worse MSE than our generalized method with no input cosmological parameters; we show that the improvement is driven by the non-linear structures in the convergence. With higher galaxy density in future weak lensing data unveiling more non-linear scales, it is likely that deep learning will be a leading approach for mass mapping with Euclid and LSST.
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2020
    detail.hit.zdb_id: 2016084-7
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  • 2
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2015
    In:  Monthly Notices of the Royal Astronomical Society Vol. 450, No. 3 ( 2015-07-01), p. 3030-3031
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP), Vol. 450, No. 3 ( 2015-07-01), p. 3030-3031
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2015
    detail.hit.zdb_id: 2016084-7
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  • 3
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2018
    In:  Monthly Notices of the Royal Astronomical Society Vol. 473, No. 3 ( 2018-01-21), p. 3895-3906
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP), Vol. 473, No. 3 ( 2018-01-21), p. 3895-3906
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2018
    detail.hit.zdb_id: 2016084-7
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  • 4
    Online Resource
    Online Resource
    Elsevier BV ; 2019
    In:  IFAC-PapersOnLine Vol. 52, No. 5 ( 2019), p. 399-404
    In: IFAC-PapersOnLine, Elsevier BV, Vol. 52, No. 5 ( 2019), p. 399-404
    Type of Medium: Online Resource
    ISSN: 2405-8963
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2019
    detail.hit.zdb_id: 2839185-8
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  • 5
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2021
    In:  Monthly Notices of the Royal Astronomical Society Vol. 508, No. 2 ( 2021-10-18), p. 2946-2963
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP), Vol. 508, No. 2 ( 2021-10-18), p. 2946-2963
    Abstract: The problem of anomaly detection in astronomical surveys is becoming increasingly important as data sets grow in size. We present the results of an unsupervised anomaly detection method using a Wasserstein generative adversarial network (WGAN) on nearly one million optical galaxy images in the Hyper Suprime-Cam (HSC) survey. The WGAN learns to generate realistic HSC-like galaxies that follow the distribution of the data set; anomalous images are defined based on a poor reconstruction by the generator and outlying features learned by the discriminator. We find that the discriminator is more attuned to potentially interesting anomalies compared to the generator, and compared to a simpler autoencoder-based anomaly detection approach, so we use the discriminator-selected images to construct a high-anomaly sample of ∼13 000 objects. We propose a new approach to further characterize these anomalous images: we use a convolutional autoencoder to reduce the dimensionality of the residual differences between the real and WGAN-reconstructed images and perform UMAP clustering on these. We report detected anomalies of interest including galaxy mergers, tidal features, and extreme star-forming galaxies. A follow-up spectroscopic analysis of one of these anomalies is detailed in the Appendix; we find that it is an unusual system most likely to be a metal-poor dwarf galaxy with an extremely blue, higher-metallicity H ii region. We have released a catalogue with the WGAN anomaly scores; the code and catalogue are available at https://github.com/kstoreyf/anomalies-GAN-HSC; and our interactive visualization tool for exploring the clustered data is at https://weirdgalaxi.es.
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2021
    detail.hit.zdb_id: 2016084-7
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  • 6
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2014
    In:  Monthly Notices of the Royal Astronomical Society Vol. 440, No. 2 ( 2014-05-11), p. 1281-1294
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP), Vol. 440, No. 2 ( 2014-05-11), p. 1281-1294
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2014
    detail.hit.zdb_id: 2016084-7
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  • 7
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2024
    In:  Monthly Notices of the Royal Astronomical Society
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP)
    Abstract: In this work, we demonstrate how differentiable stochastic sampling techniques developed in the context of deep Reinforcement Learning can be used to perform efficient parameter inference over stochastic, simulation-based, forward models. As a particular example, we focus on the problem of estimating parameters of Halo Occupation Distribution (HOD) models which are used to connect galaxies with their dark matter halos. Using a combination of continuous relaxation and gradient re-parameterisation techniques, we can obtain well-defined gradients with respect to HOD parameters through discrete galaxy catalogs realisations. Having access to these gradients allows us to leverage efficient sampling schemes, such as Hamiltonian Monte-Carlo, and greatly speed up parameter inference. We demonstrate our technique on a mock galaxy catalog generated from the Bolshoi simulation using a standard HOD model and find near identical posteriors as standard Markov Chain Monte Carlo techniques with an increase of ∼8x in convergence efficiency. Our differentiable HOD model also has broad applications in full forward model approaches to cosmic structure and cosmological analysis.
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2024
    detail.hit.zdb_id: 2016084-7
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  • 8
    In: IFAC-PapersOnLine, Elsevier BV, Vol. 50, No. 1 ( 2017-07), p. 11331-11336
    Type of Medium: Online Resource
    ISSN: 2405-8963
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2017
    detail.hit.zdb_id: 2839185-8
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  • 9
    In: Astronomy & Astrophysics, EDP Sciences, Vol. 599 ( 2017-3), p. A79-
    Abstract: Peak statistics in weak-lensing maps access the non-Gaussian information contained in the large-scale distribution of matter in the Universe. They are therefore a promising complementary probe to two-point and higher-order statistics to constrain our cosmological models. Next-generation galaxy surveys, with their advanced optics and large areas, will measure the cosmic weak-lensing signal with unprecedented precision. To prepare for these anticipated data sets, we assess the constraining power of peak counts in a simulated Euclid -like survey on the cosmological parameters Ω m , σ 8 , and w 0 de . In particular, we study how C amelus , a fast stochastic model for predicting peaks, can be applied to such large surveys. The algorithm avoids the need for time-costly N -body simulations, and its stochastic approach provides full PDF information of observables. Considering peaks with a signal-to-noise ratio ≥ 1, we measure the abundance histogram in a mock shear catalogue of approximately 5000 deg 2 using a multiscale mass-map filtering technique. We constrain the parameters of the mock survey using C amelus combined with approximate Bayesian computation, a robust likelihood-free inference algorithm. Peak statistics yield a tight but significantly biased constraint in the σ 8 –Ω m plane, as measured by the width ΔΣ 8 of the 1 σ contour. We find Σ 8 = σ 8 (Ω m / 0.27) α = 0.77 -0.05 +0.06 with α = 0.75 for a flat ΛCDM model. The strong bias indicates the need to better understand and control the model systematics before applying it to a real survey of this size or larger. We perform a calibration of the model and compare results to those from the two-point correlation functions ξ ± measured on the same field. We calibrate the ξ ± result as well, since its contours are also biased, although not as severely as for peaks. In this case, we find for peaks Σ 8 = 0.76 -0.03 +0.02 with α = 0.65, while for the combined ξ + and ξ − statistics the values are Σ 8 = 0.76 -0.01 +0.02 and α = 0.70. We conclude that the constraining power can therefore be comparable between the two weak-lensing observables in large-field surveys. Furthermore, the tilt in the σ 8 –Ω m degeneracy direction for peaks with respect to that of ξ ± suggests that a combined analysis would yield tighter constraints than either measure alone. As expected, w 0 de cannot be well constrained without a tomographic analysis, but its degeneracy directions with the other two varied parameters are still clear for both peaks and ξ ± .
    Type of Medium: Online Resource
    ISSN: 0004-6361 , 1432-0746
    RVK:
    RVK:
    Language: English
    Publisher: EDP Sciences
    Publication Date: 2017
    detail.hit.zdb_id: 1458466-9
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  • 10
    Online Resource
    Online Resource
    Oxford University Press (OUP) ; 2022
    In:  Monthly Notices of the Royal Astronomical Society Vol. 516, No. 2 ( 2022-09-10), p. 2406-2419
    In: Monthly Notices of the Royal Astronomical Society, Oxford University Press (OUP), Vol. 516, No. 2 ( 2022-09-10), p. 2406-2419
    Abstract: In order to prepare for the upcoming wide-field cosmological surveys, large simulations of the Universe with realistic galaxy populations are required. In particular, the tendency of galaxies to naturally align towards overdensities, an effect called intrinsic alignments (IA), can be a major source of systematics in the weak lensing analysis. As the details of galaxy formation and evolution relevant to IA cannot be simulated in practice on such volumes, we propose as an alternative a Deep Generative Model. This model is trained on the IllustrisTNG-100 simulation and is capable of sampling the orientations of a population of galaxies so as to recover the correct alignments. In our approach, we model the cosmic web as a set of graphs, where the graphs are constructed for each halo, and galaxy orientations as a signal on those graphs. The generative model is implemented on a Generative Adversarial Network architecture and uses specifically designed Graph-Convolutional Networks sensitive to the relative 3D positions of the vertices. Given (sub)halo masses and tidal fields, the model is able to learn and predict scalar features such as galaxy and dark matter subhalo shapes; and more importantly, vector features such as the 3D orientation of the major axis of the ellipsoid and the complex 2D ellipticities. For correlations of 3D orientations the model is in good quantitative agreement with the measured values from the simulation, except for at very small and transition scales. For correlations of 2D ellipticities, the model is in good quantitative agreement with the measured values from the simulation on all scales. Additionally, the model is able to capture the dependence of IA on mass, morphological type, and central/satellite type.
    Type of Medium: Online Resource
    ISSN: 0035-8711 , 1365-2966
    Language: English
    Publisher: Oxford University Press (OUP)
    Publication Date: 2022
    detail.hit.zdb_id: 2016084-7
    SSG: 16,12
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