“New developments in AI through study of evolution of the Milky Way
from initial quantum fluctuations to its assembly”
Project Leader
Hitoshi Murayama (Kavli IPMU)
Subleader
Masahiro Takada (Kavli IPMU)
In this project we use an AI-assisted method to understand how cosmic structures,
including our Milky Way Galaxy, have grown from the seed fluctuations, which originatedfrom the quantum fluctuations in the inflationary era, from cosmological data including thosefrom the 8.2m Subaru Telescope.
One of the most powerful cosmologicalmethods is to use the map of theuniverse obtained from the distribution ofgalaxies that are observed by telescopes
including the Subaru Telescope. By
comparing cosmological observables
obtained from the universe map with thepredictions obtained from the standard
model of the universe, we can measure
the physical quantities of the universe
such as the amount of dark matter.
Cosmologicalsimulations

using

supercomputers make it possible toaccurately calculate the time evolution ofthe distribution of dark matter (theinhomogeneities of matter distribution)
starting from the seed fluctuations in theearly universe as the initial conditions.
However, even with a supercomputer, ittakes a few days to run a high-precision
simulation for a single cosmologicalmodel.

Fig.1 Upper panel: The data points are themeasurement of galaxy power spectra measured
from the Sloan Digital Sky Survey data. The solidlines in each panel show the best-fit modelcomputed from the Dark Emulator. Lower panel: The
posterior distribution of cosmological parameters
obtained from the comparison of the SDSS data with
the Dark Emulator predictions.

In this project, we performed high-precision cosmological simulations for each of the 100
cosmological models using the supercomputer. By using a deep learning method to learn
the simulation data, we were able to build "Dark Emulator," a machine that can predict theproperties of structure formation with the same degree of accuracy as a high-precision
simulation, even for an input cosmological model for which no simulation has been run. DarkEmulator can perform calculations in only about 0.1 second, which are equivalent tosimulations that would take a few days to complete on a supercomputer. In other words, wewere able to speed up the computation by a factor of 1 million. By comparing the theoreticalpredictions computed by Dark Emulator with the cosmological statistics observed from theuniverse map data, we were able to measure cosmological parameters such as the total
amount of dark matter (Fig. 1). This method is equivalent to comparing the universe mapwith the universe computed by a supercomputer using different cosmological models.
Recently, we presented the cosmology
results using the data obtained from theHyper Suprime-Cam (HSC) on the Subaru
Telescope in Hawaii. We measured theweak gravitational lensing effect, theprediction of the Einstein’s theory ofgravity, from the Subaru HSC data andthen compared the measurement with thepredictions computed by Dark Emulator tomeasure the cosmological parameters.
Both of our cosmological parametermeasurements mentioned above show
some discrepancies with the independent
results

obtained

microwave

frombackgroundthedata.

cosmicThis

discrepancy is currently one of the hottest

Fig.2 The cosmological parameters measured bycomparing the model predictions with the weaklensing measurements from the Subaru HSC data.
For comparison, we also show the result obtainedfrom the Planck cosmic microwave backgrounddata.

topics in the cosmology field. It may
indicate new physics of the universe, and further research is underway. There is no doubtthat AI/machine learning methods will be used more and more in cosmology, taking
advantage of upcoming big data such as that from the Subaru Telescope.
As a separate project independently from the cosmology project above, we used theSubaru Telescope's large data set to measure the proper motions of stars, which can tell us
how the Milky Way evolved and formed through the assembly history of smaller building
blocks. We also measured the proper motion of the Sextans dwarf galaxy that is one of themost famous classical dwarf galaxies in the outer region of the Milky Way halo.
We were able to advance our understanding of the structure formation in the universe, fromthe Milky Way to large-scale structure. Here we would like to thank the beyond AI for thesupport for the past three years.
Selected Publications
1.

“Hyper Suprime-Cam Year 3 results: Cosmology from galaxy clustering and weaklensing with HSC and SDSS using the minimal bias model”, S. Sugiyama, et al. including
M. Takada and H. Murayama, Phys. Rev. D 12, 123521 (2023)

2.

“Hyper Suprime-Cam Year 3 Results: Cosmology from Cosmic Shear Power Spectra”,
R. Dalal, et al. including M. Takada and H. Murayama, Phys. Rev. D 108, 1235219(2023)

3.

“Hyper Suprime-Cam Year 3 Results: Cosmology from Cosmic Shear Two-point
Correlation Functions1”, X. Li, et al. including M. Takada and H. Murayama, Phys. Rev.
D 108, 1235218 (2023)

4.

“Hyper Suprime-Cam Year 3 results: Cosmology from galaxy clustering and weaklensing with HSC and SDSS using the emulator based halo model”, H. Miyatake, S.
Sugiyama, M. Takada et al. including H. Murayama, Phys. Rev. D 108, 1235217 (2023)

5.

“Study of structural parameters and systemic proper motion of Sextans dwarf
spheroidal galaxy with Subaru Hyper Suprime-Cam data”, A. Tokiwa, M. Takada et al.,MNRAS 526, 1310 (2023)

6.

“Galaxy clustering from the bottom up: a streaming model emulator I”, C. CuestaLazaro et al. including M. Takada, MRAS 523, 3219 (2023)

7.

“Cosmological inference from an emulator based halo model. II. Joint analysis of galaxygalaxy weak lensing and galaxy clustering from HSC-Y1 and SDSS”, H. Miyatake, S.
Sugiyama, M. Takada et al. including H. Murayama, Phys. Rev. D 106, 083520 (2022)8.

“Full-shape cosmology analysis of the SDSS-III BOSS galaxy power spectrum using anemulator-based halo model: A 5% determination of σ8”, Y. Kobayashi, T. Nishimichi, M.
Takada, H. Miyatake, Phys. Rev. D 105, 083517 (2022)

9.

“Proper motion measurements for stars up to 100 kpc with Subaru HSC and SDSS
Stripe 82”, T. Qiu, M. Takada, et al., MNRAS 501, 5149 (2021)