68 research outputs found
AUTHOR CORRECTION - ERS International Congress 2019:highlights from Best Abstract awardees
No full textLorna E. Latimer, Marieke Duiverman, Mahmoud I. Abdel-Aziz, Gulser Caliskan, Sara M. Mensink-Bout, Alberto Mendoza-Valderrey, Aurelien Justet, Junichi Omura, Karthi Srikanthan, Jana De Brandt. Breathe 2019; 15: e143–e149. This article from the December 2019 issue of Breathe was published with an error in the name of one of the authors. The corrected author list is shown above. The article has been corrected and republished online.</p
Physics in the multiverse: an introductory review
No full text6 pagesThis brief note, written for non-specialists, aims at drawing an introductive overview of the multiverse issue
World-making with extended gravity black holes for cosmic natural selection in the multiverse scenario
No full text5 pages, 1 figurePhysics is facing contingency. Not only in facts but also in laws (the frontier becoming extremely narrow). Cosmic natural selection is a tantalizing idea to explain the apparently highly improbable structure of our Universe. In this brief note I will study the creation of Universes by black holes in -string inspired- higher order curvature gravity
Loop quantum gravity and the CMB: toward pre-big bounce cosmology
No full textISBN 9789814374514, 9789814458030https://doi.org/10.1142/8316International audienceThe loop approach to quantum gravity is the most promising way to derive a background-independent quantization of general relativity. Interestingly, it was shown in the last years that cosmological observations -in particular with tensor modes in the primordial spectrum- could open a new window to observationally probe quantum gravity. I will briefly review the current situation and, assuming a standard evolution for the background, underline the missing links to build a fully consistent framework
Restrictions on curved cosmologies in modified gravity from metric considerations
No full text7 pages, 2 figuresInternational audienceThis study uses very simple symmetry and consistency considerations to put constraints on possible Friedmann equations for modified gravity models in curved spaces. As an example, it is applied to loop quantum cosmology
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}
Cosmological N-body simulations: a challenge for scalable generative models: Tensorflow checkpoints
No full text<p><strong>Tensorflow checkpoints: Cosmological N-body simulations: a challenge for scalable generative models</strong></p>
<p>This corresponds to the Tensorflow checkpoints for the experiments in the paper <strong>Cosmological N-body simulations: a challenge for scalable generative models</strong> by Nathanaël Perraudin, Ankit Srivastava, Aurelien Lucchi, Tomasz Kacprzak, Thomas Hofmann, Alexandre Refregier, Adam Amara.</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>Please check the assotiated github page <a href="https://github.com/nperraud/3DcosmoGAN">https://github.com/nperraud/3DcosmoGAN</a> for additional information.</p>
<p>This corresponds to the Tensorflow checkpoints for the experiments in the paper<br>
**Cosmological N-body simulations: a challenge for scalable generative models** by<br>
Nathanaël Perraudin, Ankit Srivastava, Aurelien Lucchi, Tomasz Kacprzak, Thomas Hofmann, Alexandre Refregier, Adam Amara.</p>
<p>Please check the assotiated github page <a href="https://github.com/nperraud/3DcosmoGAN">https://github.com/nperraud/3DcosmoGAN</a> for additional information.</p>
Author Correction: QUAREP-LiMi: a community endeavor to advance quality assessment and reproducibility in light microscopy
No full text- …
