Representation of the hierarchical model of the ingredients that make up the universe on a large scale. Thanks to the computational model, the connections between the amounts of intergalactic gas, dark matter, and neutral hydrogen allow researchers to predict the absorption flow of the Lyman-alpha forest, a pattern of lines in the spectrum of distant galaxies and quasars that occurs. when the light emitted by these objects is absorbed as it passes through the clouds of hydrogen gas. Credit: Francesco Sinigaglia
The Canary Islands Institute of Astrophysics (IAC) led the development of a new numerical procedure to reproduce the intergalactic medium obtained from a 100,000-hour cosmological simulation of computation using big data and machine learning techniques. Thanks to this algorithm, called Hydro-BAM, researchers have been able to exploit the hierarchy in the relationship between the properties of dark matter, ionized gas and intergalactic neutral hydrogen, ingredients that make up the large-scale structure of our universe. .
The research accurately reproduced the so-called Lyman-alpha forests, a pattern of lines in the spectrum of distant galaxies and quasars that occurs when light emitted by these objects is absorbed by clouds of hydrogen gas as they pass. The analysis of these forests is critical to advancing our understanding of the universe as a whole. The study has led to the publication of two articles in Astrophysics Journal.
Current observations seem to indicate that everything in our universe is dominated by dark matter and dark energy, which are much more abundant than conventional or baryonic matter. Baryonic matter represents only 5% of the total mass of the universe. In contrast, dark matter accounts for approximately 27% of the cosmos. The remaining 68% is composed of dark energy, which is not only responsible for the expansion of the universe, but also for its constant acceleration.
The standard cosmological model assumes that the organization of the universe at its largest scales depends on the interaction of these ingredients. In fact, state-of-the-art numerical simulations are beginning to provide a realistic modeling of these processes. However, there are a lot of uncertainties.
To obtain reliable theoretical predictions, scientists must perform large sets of numerical simulations that cover a large cosmological volume and are based on different possible models that include all relevant physical processes. These “virtual universes” serve as test benches for the study of cosmology. However, simulations are computationally expensive, and current computing facilities can only explore small cosmic volumes compared to the volumes covered by current and future observation campaigns.
Big data and AI to decode the universe
A collaboration between a team from the Canary Islands Institute of Astrophysics (IAC), led by Francisco-Shu Kitaura, and another from the University of Osaka, led by Kentaro Nagamine, have developed new strategies to recreate models. fast and detailed computational data on the formation and evolution of the universe.
“We are making a special effort to develop machine learning techniques to speed up the whole process, save computing costs, and run many of these simulations efficiently,” said Francesco Sinigaglia, PhD. student of the University of La Laguna (Tenerife, Spain) and the IAC and the University of Padua (Italy), first author of both publications.
Specifically, the IAC team has developed a new algorithm called Hydro-BAM, which combines advanced concepts of probability theory, machine learning, and cosmology. The algorithm produces accurate predictions in a few tens of seconds that are equivalent to the most expensive hydrodynamic simulations, which take approximately 100,000 hours on a supercomputer. “The algorithm is about 100,000 lines of code written in the IAC as a result of the effort of years of work by a few researchers, about the same number as the first version of Photoshop,” says Francisco- Shu Kitaura, IAC researcher.
“The goal of these studies is to improve our understanding of the large-scale structure of the universe and to infer information about its evolution over cosmic time by modeling and observing baryonic quantities,” says Andrés Balaguera Antolínez, a researcher at the IAC is one of the leading developers of the Hydro-BAM code. “Our methods aim to reproduce the observed universe through a detailed assessment of the different and complex statistical links between the three-dimensional distribution of dark matter and visible matter such as galaxies and intergalactic gas.”
Gas trees revealing the cosmic forest
Through this new computational procedure, the researchers addressed the connection to the observable universe. “We performed a thorough post-processing analysis of our hydrodynamic simulations by using millions of virtual observers to model the Lyman-alpha forest observed in the absorption of quasar lines of sight,” describes Ikkoh Shimuzu, before Osaka University. (see Shikoku). Gakuin University).
This pattern occurs when hydrogen gaseous “trees” scattered throughout the universe absorb light emitted by these distant objects. In this way, scientists can see different absorption lines corresponding to clouds at different distances and thus show different ages of the universe, as well as provide information about the intergalactic environment.
“The breakthrough came when we realized that the connections between the amounts of intergalactic gas, dark matter, and neutral hydrogen we were trying to model are well organized in a hierarchical way,” says Sinigaglia. “The ionized gas has a distribution in space very similar to that of dark matter and neutral hydrogen is determined by the distribution of ionized gas; in addition, the joint distribution of ionized gas and neutral hydrogen gives us information on the thermal state of the gas and allows us to predict the absorption flow of the Lyman-alpha forest “, he concludes.
“Our articles in this field are having a big impact on the scientific community and we have been contacted by world class groups,” says Kitaura. Despite its success, the authors say research is just beginning and they plan to produce thousands of simulated universes, including baryonic physics, which should allow a thorough analysis of galaxy survey data such as DESI, WEAVE- JPAS and the Subaru PFS project. In particular, the result of this research will allow scientists to perform an unprecedented analysis of massive sets of Lyman-alpha forest data, which will allow us to address the possible stresses of cosmological models obtained from different observational probes.
Understanding the “dark” universe and the formation of primordial galaxies More information: Francesco Sinigaglia et al, Mapping the Three-dimensional Lyα Forest Large-Scale Structure in Real and Redshift Space *, Astrophysics Journal (2022). DOI: 10.3847 / 1538-4357 / ac5112
Francesco Sinigaglia et al, The Bias from Hydrodynamic Simulations: Mapping Barion Physics on to Dark Matter Fields, Astrophysics Journal (2021). DOI: 10.3847 / 1538-4357 / ac158b
Provided by Canary Islands Institute of Astrophysics
Citation: algorithm simulates the intergalactic environment of the universe (2022, June 7) retrieved June 8, 2022 from
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