46 research outputs found
Parametrized cosmological mass maps dataset
No full textParametrized cosmological mass maps dataset
This dataset consists of the non-tomographic training and testing set without noise and intrinsic alignments.
It was introduced in the following paper
Fluri, Janis, et al. "Cosmological constraints with deep learning from KiDS-450 weak lensing maps." Physical Review D 100.6 (2019): 063514.
Furthermore, this dataset is released with the following paper:
Perraudin, Nathanaël, et al. "Emulation of cosmological mass maps with conditional generative adversarial networks." arXiv preprint arXiv:2004.08139 (2020).
Code related to this dataset can be found in https://renkulab.io/projects/nathanael.perraudin/darkmattergan
Description
The simulation grid consists of different cosmologies assuming a flat LambdaCDM universe.
Each of these 57 configurations was run with different values of Omega_m and sigma_8, resulting in the following parameter grid.| Omega_m, sigma_8
0.101, 1.304
0.102, 1.125
0.103, 0.947
0.120, 1.178
0.123, 1.006
0.127, 0.836
0.137, 1.230
0.142, 1.063
0.148, 0.900
0.154, 1.281
0.156, 0.741
0.161, 1.119
0.169, 0.961
0.171, 1.331
0.178, 0.807
0.179, 1.173
0.188, 1.019
0.189, 0.659
0.196, 1.225
0.199, 0.870
0.207, 1.075
0.212, 0.727
0.219, 0.930
0.225, 1.129
0.227, 0.591
0.233, 0.791
0.238, 0.988
0.250, 0.658
0.254, 0.852
0.257, 1.043
0.269, 0.534
0.271, 0.723
0.273, 0.910
0.291, 0.601
0.291, 0.783
0.292, 0.966
0.311, 0.842
0.312, 0.664
0.314, 0.487
0.330, 0.898
0.332, 0.724
0.335, 0.552
0.352, 0.782
0.356, 0.614
0.370, 0.838
0.376, 0.673
0.382, 0.510
0.395, 0.730
0.402, 0.570
0.413, 0.784
0.421, 0.628
0.431, 0.475
0.440, 0.683
0.450, 0.533
0.458, 0.737
0.469, 0.589
0.487, 0.643
Each zip file in the dataset corresponds to 1 of these combinations and contains 12 files containing 1000 images.
The source galaxy redshift distribution corresponding to these maps is the full, non-tomographic redshift distribution n(z) from Fluri et. al.
The projected matter distribution was pixelised into images of size 128px x 128px, which correspond to 5deg x 5deg of the sky.
Eventually, the resulting dataset consists of 57 sets of 12'000 sky convergence maps for a total of samples.
Citations
If you use this dataset, please cite:
@article{perraudin2020emulation,
title={Emulation of cosmological mass maps with conditional generative adversarial networks},
author={Perraudin, Nathana{\"e}l and Marcon, Sandro and Lucchi, Aurelien and Kacprzak, Tomasz},
journal={arXiv preprint arXiv:2004.08139},
year={2020}
}
and
@article{fluri2019cosmological,
title={Cosmological constraints with deep learning from KiDS-450 weak lensing maps},
author={Fluri, Janis and Kacprzak, Tomasz and Lucchi, Aurelien and Refregier, Alexandre and Amara, Adam and Hofmann, Thomas and Schneider, Aurel},
journal={Physical Review D},
volume={100},
number={6},
pages={063514},
year={2019},
publisher={APS}
Nbody 3D Histograms dataset
No full text
# This is the N-body simulations 3D images dataset used in the following paper:
* Scalable Generative Adversarial Networks for Multi-dimensional Images
Ankit Srivastava, Nathanaël Perraudin, Aurelien Lucchi, Tomasz Kacprzak, Thomas Hofmann, Alexandre Refregier, Adam Amara
The dataset does not contain the Nbody simulations as they have a very large size. Instead, we sliced the space into 256 x 256 x 256 cubical areas and counted the number of particules in each area. The result are 3D histograms, where the number of particles is a proxy for matter density.
Note that a the same Nbody simulation were used in this paper, but with a different way of building the histogram.
* Fast Cosmic Web Simulations with Generative Adversarial Networks
Andres C Rodriguez, Tomasz Kacprzak, Aurelien Lucchi, Adam Amara, Raphael Sgier, Janis Fluri, Thomas Hofmann, Alexandre Réfrégier
https://arxiv.org/abs/1801.09070v1
N-body simulation evolves a cosmological matter distribution over time, starting from soon after the big bang.
It represents matter density distribution as a finite set of massive particles, typically order of trillions.
The positions of these particles are modified due to gravitational forces and expansion of the cosmological volume due to cosmic acceleration.
N-body simulations use periodic boundary condition, where particles leaving the volume on one face enter it back from the opposite side.
## Short description of the data generation from Rordiguez et al. 2018:
We created N-body simulations of cosmic structures in boxes of size 100 Mpc and 500 Mpc with 512^3 and 1,024^3 particles respectively.
We used L-PICOLA [21] to create 10 and 30 independent simulation boxes for both box sizes.
The cosmological model used was ΛCDM (Cold Dark Matter) with Hubble constant H0 = 100, h = 70 km s−1 Mpc−1,
dark energy density Omega_Lambda = 0.72 and matter density Omega_m = 0.28.
We used the particle distribution at redshift z = 0.
For additional information, please check the README.md</p
Nbody 3D Histograms dataset
No full text<p><strong>3Dcosmo: a benchmark dataset for large 3-dimensional generative models (and 2-dimensional as well)</strong></p>
<p>This is the N-body simulations 3D images dataset used in the paper <em>Cosmological N-body simulations: a challenge for scalable generative models</em>, Nathanaël Perraudin, Ankit Srivastava, Aurelien Lucchi, Tomasz Kacprzak, Thomas Hofmann, Alexandre Refregier</p>
<p>The dataset does not contain the Nbody simulations as they have a very large size. Instead, we sliced the space into 256 x 256 x 256 cubical areas and counted the number of particules in each area. The result are 3D histograms, where the number of particles is a proxy for matter density.</p>
<p><strong>3DCosmo benchmark</strong></p>
<p>This dataset can be used to evaluate 2D and 3D generative model. It is particularly suitable for large scale 3D images. Please check <a href="https://github.com/nperraud/3DcosmoGAN">https://github.com/nperraud/3DcosmoGAN</a> for more information.</p>
<p>Please consider citing our paper if you use it.</p>
<pre><code>@inproceedings{perraudin2019cosmological,
title = {Cosmological N-body simulations: a challenge for scalable generative models},
author = {Nathana\"el, Perraudin and Ankit, Srivastava and Kacprzak, Tomasz and Lucchi, Aurelien and Hofmann, Thomas and R{\'e}fr{\'e}gier, Alexandre},
year = {2019},
archivePrefix = {arXiv},
eprint = {1908.05519},
url = {https://arxiv.org/abs/1908.05519},
}
</code></pre>
<p>While this data is associated to the paper <em>Cosmological N-body simulations: a challenge for scalable generative models</em>, note that a the same Nbody simulation were used in the paper <em>Fast Cosmic Web Simulations with Generative Adversarial Networks</em> (<a href="https://arxiv.org/abs/1801.09070v1">https://arxiv.org/abs/1801.09070v1</a>), but with a different way of building the histogram. You may want to cite this work as well.</p>
<pre><code>@article{rodriguez2018fast,
title={Fast cosmic web simulations with generative adversarial networks},
author={Rodr{\'\i}guez, Andres C and Kacprzak, Tomasz and Lucchi, Aurelien and Amara, Adam and Sgier, Rapha{\"e}l and Fluri, Janis and Hofmann, Thomas and R{\'e}fr{\'e}gier, Alexandre},
journal={Computational Astrophysics and Cosmology},
volume={5},
number={1},
pages={4},
year={2018},
publisher={Springer}
}
</code></pre>
<p>N-body simulation evolves a cosmological matter distribution over time, starting from soon after the big bang. It represents matter density distribution as a finite set of massive particles, typically order of trillions. The positions of these particles are modified due to gravitational forces and expansion of the cosmological volume due to cosmic acceleration. N-body simulations use periodic boundary condition, where particles leaving the volume on one face enter it back from the opposite side.</p>
<p><strong>Short description of the data generation:</strong></p>
<p>We created N-body simulations of cosmic structures in boxes of size 100 Mpc and 500 Mpc with 512^3 and 1,024^3 particles respectively. We used L-PICOLA [21] to create 10 and 30 independent simulation boxes for both box sizes. The cosmological model used was ΛCDM (Cold Dark Matter) with Hubble constant H0 = 500, h = 350 km s−1 Mpc−1, dark energy density Omega_Lambda = 0.72 and matter density Omega_m = 0.28. We used the particle distribution at redshift z = 0.</p>
<p><br>
For additional information, please check the README.md</p>
Response by Clive Barnett. Book review forum discussion: The Priority of Injustice: Locating Democracy in Critical Theory, by Michael Samers, Joshua Barkan, Kirsi Pauliina Kallio, Jennifer L. Fluri and Clive Barnett
No full textThis is the author accepted manuscript. The final version is available from Routledge via the DOI in this recordThis is the response by Clive Barnett within the book review forum discussion "The Priority of Injustice: Locating Democracy in Critical Theory", by Michael Samers, Joshua Barkan, Kirsi Pauliina Kallio, Jennifer L. Fluri and Clive Barnett which constitutes the whole article cited in this record. The response is on pp. 50-53 of the articl
DeepLSS: Breaking Parameter Degeneracies in Large-Scale Structure with Deep-Learning Analysis of Combined Probes
No full textISSN:2160-330
A tomographic spherical mass map emulator of the KiDS-1000 survey using conditional generative adversarial networks
No full textLarge sets of matter density simulations are becoming increasingly important
in large-scale structure cosmology. Matter power spectra emulators, such as the
Euclid Emulator and CosmicEmu, are trained on simulations to correct the
non-linear part of the power spectrum. Map-based analyses retrieve additional
non-Gaussian information from the density field, whether through human-designed
statistics such as peak counts, or via machine learning methods such as
convolutional neural networks. The simulations required for these methods are
very resource-intensive, both in terms of computing time and storage. Map-level
density field emulators, based on deep generative models, have recently been
proposed to address these challenges. In this work, we present a novel mass map
emulator of the KiDS-1000 survey footprint, which generates noise-free
spherical maps in a fraction of a second. It takes a set of cosmological
parameters as input and produces a consistent set of 5
maps, corresponding to the KiDS-1000 tomographic redshift bins. To construct
the emulator, we use a conditional generative adversarial network architecture
and the spherical CNN , and train it on N-body-simulated
mass maps. We compare its performance using an array of quantitative comparison
metrics: angular power spectra , pixel/peaks distributions,
correlation matrices, and Structural Similarity Index. Overall, the average
agreement on these summary statistics is for the cosmologies at the
centre of the simulation grid, and degrades slightly on grid edges. Finally, we
perform a mock cosmological parameter estimation using the emulator and the
original simulation set. We find good agreement in these constraints, for both
likelihood and likelihood-free approaches. The emulator is available at
https://tfhub.dev/cosmo-group-ethz/models/kids-cgan/1.Comment: 38 pages, 17 figures, 2 tables. Link to software:
https://tfhub.dev/cosmo-group-ethz/models/kids-cgan/
Cosmological constraints from noisy convergence maps through deep learning
No full textISSN:1550-7998ISSN:0556-2821ISSN:1550-2368ISSN:0556-2821ISSN:1550-236
Experimental results from a laboratory-scale molten salt thermocline storage
No full textArt. 080025, 10 S.Single-tank storage presents a valid option for cost reduction in thermal energy storage systems. For low-temperature systems with water as storage medium this concept is widely implemented and tested. For high-temperature systems very limited experimental data are publicly available. To improve this situation a molten salt loop for experimental testing of a single-tank storage prototype was designed and built at Fraunhofer ISE. The storage tank has a volume of 0.4 m3 or a maximum capacity of 72 kWhth. The maximum charging and discharging power is 60 kW, however, a bypass flow control system enables to operate the system also at a very low power. The prototype was designed to withstand temperatures up to 550 °C. A cascaded insulation with embedded heating cables can be used to reduce the effect of heat loss on the storage which is susceptible to edge effects due to its small size. During the first tests the operating temperatures were adapted to the conditions in systems with thermal oil as heat transfer fluid and a smaller temperature difference. A good separation between cold and hot fluid was achieved with temperature gradients of 95 K within 16 cm
Evaluation of different operating strategies to integrate storage in a linear Fresnel ORC power plant
No full textArt. 040008, 8 S.An existing linear Fresnel power plant with ORC process located in Ben Guerir, Morocco, is retrofitted with a thermal energy storage system and additional collector loops. Two different plant configurations are investigated in this paper. In the first configuration two separate solar fields are built and only the minor one can charge the storage. In the second configuration, there is only one large solar field which offers more flexibility. Two different control strategies are assessed by comparing simulation results. It shows that the simulations of the systems with two solar fields results in higher energy yields throughout the year, but the power production of the system with one solar field is much more flexible and demand oriented. Also it offers great potential for improvement when it comes to weather forecasting
