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  1. Article ; Online: Emulation of Cosmological Mass Maps with Conditional Generative Adversarial Networks.

    Perraudin, Nathanaël / Marcon, Sandro / Lucchi, Aurelien / Kacprzak, Tomasz

    Frontiers in artificial intelligence

    2021  Volume 4, Page(s) 673062

    Abstract: Weak gravitational lensing mass maps play a crucial role in understanding the evolution of structures in the Universe and our ability to constrain cosmological models. The prediction of these mass maps is based on expensive N-body simulations, which can ... ...

    Abstract Weak gravitational lensing mass maps play a crucial role in understanding the evolution of structures in the Universe and our ability to constrain cosmological models. The prediction of these mass maps is based on expensive N-body simulations, which can create a computational bottleneck for cosmological analyses. Simulation-based emulators of map summary statistics, such as the matter power spectrum and its covariance, are starting to play increasingly important role, as the analytical predictions are expected to reach their precision limits for upcoming experiments. Creating an emulator of the cosmological mass maps themselves, rather than their summary statistics, is a more challenging task. Modern deep generative models, such as Generative Adversarial Networks (GAN), have demonstrated their potential to achieve this goal. Most existing GAN approaches produce simulations for a fixed value of the cosmological parameters, which limits their practical applicability. We propose a novel conditional GAN model that is able to generate mass maps for any pair of matter density Ω
    Language English
    Publishing date 2021-06-04
    Publishing country Switzerland
    Document type Journal Article
    ISSN 2624-8212
    ISSN (online) 2624-8212
    DOI 10.3389/frai.2021.673062
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  2. Book ; Online: Emulation of cosmological mass maps with conditional generative adversarial networks

    Perraudin, Nathanaël / Marcon, Sandro / Lucchi, Aurelien / Kacprzak, Tomasz

    2020  

    Abstract: Weak gravitational lensing mass maps play a crucial role in understanding the evolution of structures in the universe and our ability to constrain cosmological models. The prediction of these mass maps is based on expensive N-body simulations, which can ... ...

    Abstract Weak gravitational lensing mass maps play a crucial role in understanding the evolution of structures in the universe and our ability to constrain cosmological models. The prediction of these mass maps is based on expensive N-body simulations, which can create a computational bottleneck for cosmological analyses. Modern deep generative models, such as Generative Adversarial Networks (GAN), have demonstrated their potential to achieve this goal. Most existing GAN approaches produce simulations for a fixed value of the cosmological parameters, which limits their practical applicability. We propose a novel conditional GAN model that is able to generate mass maps for any pair of matter density $\Omega_m$ and matter clustering strength $\sigma_8$, parameters which have the largest impact on the evolution of structures in the universe. Our results show that our conditional GAN can interpolate efficiently within the space of simulated cosmologies, and generate maps anywhere inside this space with good visual quality high statistical accuracy. We perform an extensive quantitative comparison of the N-body and GAN -generated maps using a range of metrics: the pixel histograms, peak counts, power spectra, bispectra, Minkowski functionals, correlation matrices of the power spectra, the Multi-Scale Structural Similarity Index (MS-SSIM) and our equivalent of the Fr\'echet Inception Distance (FID). We find a very good agreement on these metrics, with typical differences are <5% at the centre of the simulation grid, and slightly worse for cosmologies at the grid edges. The agreement for the bispectrum is slightly worse, on the <20% level. This contribution is a step towards building emulators of mass maps directly, capturing both the cosmological signal and its variability. We make the code and the data publicly available: https://renkulab.io/gitlab/nathanael.perraudin/darkmattergan

    Comment: Accepted at the Workshop at the 33rd Conference on Neural Information Processing Systems (NeurIPS), December 14, 2019, https://ml4physicalsciences.github.io/files/NeurIPS_ML4PS_2019_97.pdf Accepted in Frontiers in Artificial Intelligence in May 2021
    Keywords Astrophysics - Cosmology and Nongalactic Astrophysics ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Image and Video Processing
    Subject code 520
    Publishing date 2020-04-17
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Book ; Online: DeepSphere

    Defferrard, Michaël / Perraudin, Nathanaël / Kacprzak, Tomasz / Sgier, Raphael

    towards an equivariant graph-based spherical CNN

    2019  

    Abstract: Spherical data is found in many applications. By modeling the discretized sphere as a graph, we can accommodate non-uniformly distributed, partial, and changing samplings. Moreover, graph convolutions are computationally more efficient than spherical ... ...

    Abstract Spherical data is found in many applications. By modeling the discretized sphere as a graph, we can accommodate non-uniformly distributed, partial, and changing samplings. Moreover, graph convolutions are computationally more efficient than spherical convolutions. As equivariance is desired to exploit rotational symmetries, we discuss how to approach rotation equivariance using the graph neural network introduced in Defferrard et al. (2016). Experiments show good performance on rotation-invariant learning problems. Code and examples are available at https://github.com/SwissDataScienceCenter/DeepSphere

    Comment: published at the ICLR 2019 Workshop on Representation Learning on Graphs and Manifolds. arXiv admin note: text overlap with arXiv:1810.12186
    Keywords Computer Science - Machine Learning ; Statistics - Machine Learning
    Publishing date 2019-04-08
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  4. Book ; Online: DeepSphere

    Perraudin, Nathanaël / Defferrard, Michaël / Kacprzak, Tomasz / Sgier, Raphael

    Efficient spherical Convolutional Neural Network with HEALPix sampling for cosmological applications

    2018  

    Abstract: Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning toolbox and have led to many breakthroughs in Artificial Intelligence. These networks have mostly been developed for regular Euclidean domains such as those supporting images, ... ...

    Abstract Convolutional Neural Networks (CNNs) are a cornerstone of the Deep Learning toolbox and have led to many breakthroughs in Artificial Intelligence. These networks have mostly been developed for regular Euclidean domains such as those supporting images, audio, or video. Because of their success, CNN-based methods are becoming increasingly popular in Cosmology. Cosmological data often comes as spherical maps, which make the use of the traditional CNNs more complicated. The commonly used pixelization scheme for spherical maps is the Hierarchical Equal Area isoLatitude Pixelisation (HEALPix). We present a spherical CNN for analysis of full and partial HEALPix maps, which we call DeepSphere. The spherical CNN is constructed by representing the sphere as a graph. Graphs are versatile data structures that can act as a discrete representation of a continuous manifold. Using the graph-based representation, we define many of the standard CNN operations, such as convolution and pooling. With filters restricted to being radial, our convolutions are equivariant to rotation on the sphere, and DeepSphere can be made invariant or equivariant to rotation. This way, DeepSphere is a special case of a graph CNN, tailored to the HEALPix sampling of the sphere. This approach is computationally more efficient than using spherical harmonics to perform convolutions. We demonstrate the method on a classification problem of weak lensing mass maps from two cosmological models and compare the performance of the CNN with that of two baseline classifiers. The results show that the performance of DeepSphere is always superior or equal to both of these baselines. For high noise levels and for data covering only a smaller fraction of the sphere, DeepSphere achieves typically 10% better classification accuracy than those baselines. Finally, we show how learned filters can be visualized to introspect the neural network.

    Comment: arXiv admin note: text overlap with arXiv:astro-ph/0409513 by other authors
    Keywords Astrophysics - Cosmology and Nongalactic Astrophysics ; Astrophysics - Instrumentation and Methods for Astrophysics ; Computer Science - Artificial Intelligence ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning
    Subject code 006
    Publishing date 2018-10-29
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: Constructing Impactful Machine Learning Research for Astronomy

    Huppenkothen, D. / Ntampaka, M. / Ho, M. / Fouesneau, M. / Nord, B. / Peek, J. E. G. / Walmsley, M. / Wu, J. F. / Avestruz, C. / Buck, T. / Brescia, M. / Finkbeiner, D. P. / Goulding, A. D. / Kacprzak, T. / Melchior, P. / Pasquato, M. / Ramachandra, N. / Ting, Y. -S. / van de Ven, G. /
    Villar, S. / Villar, V. A. / Zinger, E.

    Best Practices for Researchers and Reviewers

    2023  

    Abstract: Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and ... ...

    Abstract Machine learning has rapidly become a tool of choice for the astronomical community. It is being applied across a wide range of wavelengths and problems, from the classification of transients to neural network emulators of cosmological simulations, and is shifting paradigms about how we generate and report scientific results. At the same time, this class of method comes with its own set of best practices, challenges, and drawbacks, which, at present, are often reported on incompletely in the astrophysical literature. With this paper, we aim to provide a primer to the astronomical community, including authors, reviewers, and editors, on how to implement machine learning models and report their results in a way that ensures the accuracy of the results, reproducibility of the findings, and usefulness of the method.

    Comment: 14 pages, 3 figures; submitted to the Bulletin of the American Astronomical Society
    Keywords Astrophysics - Instrumentation and Methods for Astrophysics ; Computer Science - Machine Learning
    Publishing date 2023-10-19
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Cosmological N-body simulations

    Perraudin, Nathanaël / Srivastava, Ankit / Lucchi, Aurelien / Kacprzak, Tomasz / Hofmann, Thomas / Réfrégier, Alexandre

    a challenge for scalable generative models

    2019  

    Abstract: Deep generative models, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAs) have been demonstrated to produce images of high visual quality. However, the existing hardware severely limits the size of the images that can be ... ...

    Abstract Deep generative models, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAs) have been demonstrated to produce images of high visual quality. However, the existing hardware severely limits the size of the images that can be generated. The rapid growth of high dimensional data in many fields of science therefore poses a significant challenge for generative models. In cosmology, the large-scale, three-dimensional matter distribution, modeled with N-body simulations, plays a crucial role in understanding the evolution of the universe. As these simulations are computationally very expensive, GANs have recently generated interest as a possible method to emulate these datasets, but they have been, so far, mostly limited to two dimensional data. In this work, we introduce a new benchmark for the generation of three dimensional N-body simulations, in order to stimulate new ideas in the machine learning community and move closer to the practical use of generative models in cosmology. As a first benchmark result, we propose a scalable GAN approach for training a generator of N-body three-dimensional cubes. Our technique relies on two key building blocks, (i) splitting the generation of the high-dimensional data into smaller parts, and (ii) using a multi-scale approach that efficiently captures global image features that might otherwise be lost in the splitting process. We evaluate the performance of our model for the generation of N-body samples using various statistical measures commonly used in cosmology. Our results show that the proposed model produces samples of high visual quality, although the statistical analysis reveals that capturing rare features in the data poses significant problems for the generative models. We make the data, quality evaluation routines, and the proposed GAN architecture publicly available at https://github.com/nperraud/3DcosmoGAN
    Keywords Physics - Computational Physics ; Astrophysics - Cosmology and Nongalactic Astrophysics ; Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Image and Video Processing
    Subject code 006 ; 600
    Publishing date 2019-08-15
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Thesis: Teoria i projektowanie układów przerzutnikowych CMOS w asynchronicznych systemach cyfrowych VLSI

    Kacprzak, Tomasz

    (Rozprawy naukowe / Politechnika Łódzka ; 120 ; Zeszyty naukowe / Politechnika Łódzka ; 571)

    1989  

    Author's details Tomasz Kacprzak
    Series title Rozprawy naukowe / Politechnika Łódzka ; 120
    Zeszyty naukowe / Politechnika Łódzka ; 571
    Size 204 S
    Publishing place Łódź
    Document type Book ; Thesis
    Thesis / German Habilitation thesis Diss
    Database Library catalogue of the German National Library of Science and Technology (TIB), Hannover

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  8. Article ; Online: Wide-Field Lensing Mass Maps from Dark Energy Survey Science Verification Data.

    Chang, C / Vikram, V / Jain, B / Bacon, D / Amara, A / Becker, M R / Bernstein, G / Bonnett, C / Bridle, S / Brout, D / Busha, M / Frieman, J / Gaztanaga, E / Hartley, W / Jarvis, M / Kacprzak, T / Kovács, A / Lahav, O / Lin, H /
    Melchior, P / Peiris, H / Rozo, E / Rykoff, E / Sánchez, C / Sheldon, E / Troxel, M A / Wechsler, R / Zuntz, J / Abbott, T / Abdalla, F B / Allam, S / Annis, J / Bauer, A H / Benoit-Lévy, A / Brooks, D / Buckley-Geer, E / Burke, D L / Capozzi, D / Carnero Rosell, A / Carrasco Kind, M / Castander, F J / Crocce, M / D'Andrea, C B / Desai, S / Diehl, H T / Dietrich, J P / Doel, P / Eifler, T F / Evrard, A E / Fausti Neto, A / Flaugher, B / Fosalba, P / Gruen, D / Gruendl, R A / Gutierrez, G / Honscheid, K / James, D / Kent, S / Kuehn, K / Kuropatkin, N / Maia, M A G / March, M / Martini, P / Merritt, K W / Miller, C J / Miquel, R / Neilsen, E / Nichol, R C / Ogando, R / Plazas, A A / Romer, A K / Roodman, A / Sako, M / Sanchez, E / Sevilla, I / Smith, R C / Soares-Santos, M / Sobreira, F / Suchyta, E / Tarle, G / Thaler, J / Thomas, D / Tucker, D / Walker, A R

    Physical review letters

    2015  Volume 115, Issue 5, Page(s) 51301

    Abstract: We present a mass map reconstructed from weak gravitational lensing shear measurements over 139  deg2 from the Dark Energy Survey science verification data. The mass map probes both luminous and dark matter, thus providing a tool for studying cosmology. ... ...

    Abstract We present a mass map reconstructed from weak gravitational lensing shear measurements over 139  deg2 from the Dark Energy Survey science verification data. The mass map probes both luminous and dark matter, thus providing a tool for studying cosmology. We find good agreement between the mass map and the distribution of massive galaxy clusters identified using a red-sequence cluster finder. Potential candidates for superclusters and voids are identified using these maps. We measure the cross-correlation between the mass map and a magnitude-limited foreground galaxy sample and find a detection at the 6.8σ level with 20 arc min smoothing. These measurements are consistent with simulated galaxy catalogs based on N-body simulations from a cold dark matter model with a cosmological constant. This suggests low systematics uncertainties in the map. We summarize our key findings in this Letter; the detailed methodology and tests for systematics are presented in a companion paper.
    Language English
    Publishing date 2015-07-31
    Publishing country United States
    Document type Journal Article
    ZDB-ID 208853-8
    ISSN 1079-7114 ; 0031-9007
    ISSN (online) 1079-7114
    ISSN 0031-9007
    DOI 10.1103/PhysRevLett.115.051301
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