Multifield Cosmology with Artificial Intelligence

• URL: http://arxiv.org/abs/2109.09747v1
• Date: Mon, 20 Sep 2021 18:00:01 GMT
• Title: Multifield Cosmology with Artificial Intelligence
• Authors: Francisco Villaescusa-Navarro, Daniel Angl\'es-Alc\'azar, Shy Genel, David N. Spergel, Yin Li, Benjamin Wandelt, Andrina Nicola, Leander Thiele, Sultan Hassan, Jose Manuel Zorrilla Matilla, Desika Narayanan, Romeel Dave, Mark Vogelsberger
• Abstract summary: Astrophysical processes modify the properties of dark matter, gas, and galaxies in a poorly understood way. We generate hundreds of thousands of 2-dimensional maps for 13 different fields. We use these maps to train convolutional neural networks to extract the maximum amount of cosmological information.
• Score: 13.031414468952313
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Astrophysical processes such as feedback from supernovae and active galactic nuclei modify the properties and spatial distribution of dark matter, gas, and galaxies in a poorly understood way. This uncertainty is one of the main theoretical obstacles to extract information from cosmological surveys. We use 2,000 state-of-the-art hydrodynamic simulations from the CAMELS project spanning a wide variety of cosmological and astrophysical models and generate hundreds of thousands of 2-dimensional maps for 13 different fields: from dark matter to gas and stellar properties. We use these maps to train convolutional neural networks to extract the maximum amount of cosmological information while marginalizing over astrophysical effects at the field level. Although our maps only cover a small area of $(25~h^{-1}{\rm Mpc})^2$, and the different fields are contaminated by astrophysical effects in very different ways, our networks can infer the values of $\Omega_{\rm m}$ and $\sigma_8$ with a few percent level precision for most of the fields. We find that the marginalization performed by the network retains a wealth of cosmological information compared to a model trained on maps from gravity-only N-body simulations that are not contaminated by astrophysical effects. Finally, we train our networks on multifields -- 2D maps that contain several fields as different colors or channels -- and find that not only they can infer the value of all parameters with higher accuracy than networks trained on individual fields, but they can constrain the value of $\Omega_{\rm m}$ with higher accuracy than the maps from the N-body simulations.

Related papers

• PAC-NeRF: Physics Augmented Continuum Neural Radiance Fields for Geometry-Agnostic System Identification [64.61198351207752]
  Existing approaches to system identification (estimating the physical parameters of an object) from videos assume known object geometries. In this work, we aim to identify parameters characterizing a physical system from a set of multi-view videos without any assumption on object geometry or topology. We propose "Physics Augmented Continuum Neural Radiance Fields" (PAC-NeRF), to estimate both the unknown geometry and physical parameters of highly dynamic objects from multi-view videos.
  arXiv  Detail & Related papers  (2023-03-09T18:59:50Z)
• Semi-Supervised Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection [57.85347204640585]
  We develop a Universal Domain Adaptation method DeepAstroUDA. It can be applied to datasets with different types of class overlap. For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets.
  arXiv  Detail & Related papers  (2022-11-01T18:07:21Z)
• $\texttt{Mangrove}$: Learning Galaxy Properties from Merger Trees [1.2116854758481395]
  Graph Neural Networks (GNNs) have proven to be the natural choice for learning physical relations. $texttMangrove$ emulates the galactic stellar mass, cold gas mass and metallicity, instantaneous and time-averaged star formation rate, and black hole mass. root mean squared error up to two times lower than other methods across a $(75 Mpc/h)3$ simulation box in 40 seconds, 4 orders of magnitude faster than the SAM.
  arXiv  Detail & Related papers  (2022-10-24T18:00:00Z)
• Unsupervised Spectral Unmixing For Telluric Correction Using A Neural Network Autoencoder [58.720142291102135]
  We present a neural network autoencoder approach for extracting a telluric transmission spectrum from a large set of high-precision observed solar spectra from the HARPS-N radial velocity spectrograph.
  arXiv  Detail & Related papers  (2021-11-17T12:54:48Z)
• Satellite galaxy abundance dependency on cosmology in Magneticum simulations [101.18253437732933]
  We build an emulator of satellite abundance based on cosmological parameters. We find that $A$ and $beta$ depend on cosmological parameters, even if weakly. We also show that satellite abundance cosmology dependency differs between full-physics (FP) simulations, dark-matter only (DMO) and non-radiative simulations.
  arXiv  Detail & Related papers  (2021-10-11T18:00:02Z)
• The CAMELS Multifield Dataset: Learning the Universe's Fundamental Parameters with Artificial Intelligence [13.72500304639404]
  We present the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) Multifield dataset, CMD. CMD is a collection of hundreds of thousands of 2D maps and 3D grids containing many different properties of cosmic gas, dark matter, and stars from 2,000 distinct simulated universes at several cosmic times. The 2D maps and 3D grids represent cosmic regions that span $sim$100 million light years and have been generated from thousands of state-of-the-art hydrodynamic and gravity-only N-body simulations from the CAMELS project.
  arXiv  Detail & Related papers  (2021-09-22T18:00:01Z)
• Robust marginalization of baryonic effects for cosmological inference at the field level [12.768056235837427]
  We train neural networks to perform likelihood-free inference from $(25,h-1rm Mpc)2$ 2D maps containing the total mass surface density. We show that the networks can extract information beyond one-point functions and power spectra from all resolved scales.
  arXiv  Detail & Related papers  (2021-09-21T18:00:01Z)
• DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning [70.80563014913676]
  We investigate the use of convolutional neural networks (CNNs) for the problem of separating low-surface-brightness galaxies from artifacts in survey images. We show that CNNs offer a very promising path in the quest to study the low-surface-brightness universe.
  arXiv  Detail & Related papers  (2020-11-24T22:51:08Z)
• Emulation of cosmological mass maps with conditional generative adversarial networks [0.0]
  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 $sigma_8$. Our results show that our conditional GAN can interpolate efficiently within the space of simulated cosmologies. This contribution is a step towards building emulators of mass maps directly, capturing both the cosmological signal and its variability.
  arXiv  Detail & Related papers  (2020-04-17T09:34:34Z)
• Probing chiral edge dynamics and bulk topology of a synthetic Hall system [52.77024349608834]
  Quantum Hall systems are characterized by the quantization of the Hall conductance -- a bulk property rooted in the topological structure of the underlying quantum states. Here, we realize a quantum Hall system using ultracold dysprosium atoms, in a two-dimensional geometry formed by one spatial dimension. We demonstrate that the large number of magnetic sublevels leads to distinct bulk and edge behaviors.
  arXiv  Detail & Related papers  (2020-01-06T16:59:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.