Galaxies are only the “tip of the iceberg” of the matter distribution in the universe. On the left, the gray structures represent a slice through the dark matter distribution in a large cosmological simulation with red points indicating the positions of galaxies. The right panel shows the same galaxies, where the colours represent their density on a coarse grid. This large-scale galaxy density is the data considered for the field-level analysis, and is computed directly from the observed galaxy positions. © MPA
The distribution of galaxies on large, cosmological scales holds important clues on the nature of dark
matter, the properties of dark energy and the origin of our Universe.
Yet, optimally retrieving this information from observations is
challenging. MPA researchers are developing a novel analysis approach,
where they follow the evolution of cosmic structures through their
entire formation history. Enabling a very detailed comparison between
theoretical models and observational data, this approach will allow
measuring key parameters of dark matter and dark energy very precisely.
The spatial pattern of galaxies in the Universe exhibits a complex, web-like structure with large galaxy clusters at the nodes that is interspersed by fast voids. Cosmologists have developed a detailed understanding how this cosmic large-scale structure emerged. In a nutshell, an early inflationary period of rapid expansion created very small (of order 1 in 100,000) fluctuations in the cosmic energy density, which subsequently evolved under their self-gravity as the Universe expanded. Yet, in this picture, there remain several open questions:
The spatial pattern of galaxies in the Universe exhibits a complex, web-like structure with large galaxy clusters at the nodes that is interspersed by fast voids. Cosmologists have developed a detailed understanding how this cosmic large-scale structure emerged. In a nutshell, an early inflationary period of rapid expansion created very small (of order 1 in 100,000) fluctuations in the cosmic energy density, which subsequently evolved under their self-gravity as the Universe expanded. Yet, in this picture, there remain several open questions:
- What is the nature of dark energy that drives the accelerated expansion of the Universe?
- What is the nature of dark matter, whose presence is required by gravitational dynamics?
- What is the physical mechanism that drives inflation?
Dark matter is about five times more abundant than ordinary, “visible”
matter. General Relativity requires visible and dark matter to
experience the same gravitational forces, so the evolution of dark
matter fluctuations can be predicted very precisely. ;It depends
sensitively on the interplay between gravitational attraction and the
cosmological expansion. Therefore, the cosmic large-scale structure
holds important clues on the fundamental questions about our Universe.
Large cosmological surveys map the distribution of galaxies out to very
large distances. Yet, these galaxies comprise only the “tip of the
iceberg” while the majority of the cosmic large-scale structure resides
in dark matter and remains invisible.
Is there a way to deduce the distribution of dark matter in our Universe from the observed positions of galaxies? Can we understand how the structures we see today have formed? And can this help us to better answer the fundamental questions we have about our cosmos? The answer is yes. However, as we will see, we first need to overcome some challenges.
First, galaxies do not follow the distribution of matter one-to-one, we only observe a limited number of them, and measurement uncertainties affect the observations. Thus, the matter distribution that explains the observations is not unique – there can be multiple possibilities. A statistical Bayesian analysis can capture this uncertainty: we generate several plausible matter distributions, evaluate how consistent they are with the observations, and only keep those that show the highest level of agreement.
The relation between galaxies and the underlying matter density field is modelled with a theoretical framework called the Effective Field Theory (EFT) of Large Scale Structure. This effective theory has a number of free parameters designed to make our results insensitive to the details of galaxy formation on small scales, which we cosmologists do not yet understand completely. Moreover, the theory gives us a “likelihood” that quantifies the level of agreement between a proposed matter distribution and the observed galaxies. Since this comparison is done voxel by voxel (3D pixels), this is also known as a field-level analysis.
The second challenge is how to propose plausible matter distributions in the first place. The matter distribution in the universe today is the result of a complex dynamical evolution. In contrast, the distribution of primordial perturbations, which provide the starting point to this evolution, have simple statistical properties. We can take a guess on the primordial density, forward model their evolution in time, and compare that prediction to the observation. We repeat this over and over to discover the configurations best explaining the data and explore all possible solutions.
The field–level analysis starts from guessing a possible matter distribution in the very early Universe (left) and evolves it through cosmic time to predict the expected large-scale galaxy distribution (middle) that we can observe with cosmological surveys (right). Shown here are two-dimensional slices; in reality, each panel corresponds to a three-dimensional cube. Each frame in the animation shows a separate realization of the large-scale structure that is compatible with the observation. By optimizing the agreement between model prediction and observation, we can infer the distribution of dark matter in the Universe and measure properties of dark matter and dark energy very precisely.
Authors:
Julia Stadler
Postdoc
tel:2205
jstadler@mpa-garching.mpg.de
Fabian Schmidt
Scientific Staff Member of the works council,
Representative of the Scientific Coworkers
tel:2274
fschmidt@mpa-garching.mpg.de
Beatriz Tucci Schiewaldt
PhD student
tel:2358
tucci@mpa-garching.mpg.de
Original publications:
1. Fabian Schmidt
An n-th order Lagrangian Forward Model for Large-Scale Structure
JCAP 04 (2021), 033
DOI
2. Andrija Kostić, Nhat-Minh Nguyen, Fabian Schmidt, Martin Reinecke
Consistency tests of field level inference with the EFT likelihood
JCAP 07 (2023), 063
DOI
3. Julia Stadler, Fabian Schmidt, Martin Reinecke
Cosmology inference at the field level from biased tracers in redshift-space
JCAP 10 (2023), 069
DOI
Is there a way to deduce the distribution of dark matter in our Universe from the observed positions of galaxies? Can we understand how the structures we see today have formed? And can this help us to better answer the fundamental questions we have about our cosmos? The answer is yes. However, as we will see, we first need to overcome some challenges.
First, galaxies do not follow the distribution of matter one-to-one, we only observe a limited number of them, and measurement uncertainties affect the observations. Thus, the matter distribution that explains the observations is not unique – there can be multiple possibilities. A statistical Bayesian analysis can capture this uncertainty: we generate several plausible matter distributions, evaluate how consistent they are with the observations, and only keep those that show the highest level of agreement.
The relation between galaxies and the underlying matter density field is modelled with a theoretical framework called the Effective Field Theory (EFT) of Large Scale Structure. This effective theory has a number of free parameters designed to make our results insensitive to the details of galaxy formation on small scales, which we cosmologists do not yet understand completely. Moreover, the theory gives us a “likelihood” that quantifies the level of agreement between a proposed matter distribution and the observed galaxies. Since this comparison is done voxel by voxel (3D pixels), this is also known as a field-level analysis.
The second challenge is how to propose plausible matter distributions in the first place. The matter distribution in the universe today is the result of a complex dynamical evolution. In contrast, the distribution of primordial perturbations, which provide the starting point to this evolution, have simple statistical properties. We can take a guess on the primordial density, forward model their evolution in time, and compare that prediction to the observation. We repeat this over and over to discover the configurations best explaining the data and explore all possible solutions.
The field–level analysis starts from guessing a possible matter distribution in the very early Universe (left) and evolves it through cosmic time to predict the expected large-scale galaxy distribution (middle) that we can observe with cosmological surveys (right). Shown here are two-dimensional slices; in reality, each panel corresponds to a three-dimensional cube. Each frame in the animation shows a separate realization of the large-scale structure that is compatible with the observation. By optimizing the agreement between model prediction and observation, we can infer the distribution of dark matter in the Universe and measure properties of dark matter and dark energy very precisely.
Each possible realization of the three-dimensional primordial density is characterized by up to a million parameters. And for each of these realizations we have the full history of structure formation. To explore this huge range of possibilities efficiently, we have developed our own numerical code LEFTfield, which is heavily parallelized, provides gradient information and runs on computing clusters. It can generate plausible matter distributions and forward evolve them through cosmic time in just a few seconds. Figure 2 illustrates the result of such an analysis on a simulated dataset, where each frame of the animation shows a separate simulated universe that is compatible with the observation. The observations best constrain the distribution of matter on large scales. Correspondingly, the animation shows little variability in the inferred density for larger structures, but more movement in smaller ones.
How does this help to answer the questions posed above? The properties of dark matter and dark energy are built into our numerical forward model for structure formation. We can now modify these parameters and see how the agreement with the data changes. For example, changing the amount of dark energy or how it evolves leads to a different expansion rate of the universe, which in turn affects the growth of structure. This comparison therefore allows us to measure key parameters of dark matter and dark energy.
The field-level analysis extracts all information available in the large-scale galaxy distribution; our ultimate goal is to achieve a much more precise measurement of dark matter and dark energy than current state-of-the-art analyses. The latter only compare theory and data at the level of highly-compressed summary statistics (such as the RMS fluctuations as a function of scale). A further advantage of forward modeling is that we can incorporate observational and instrumental effects present in cosmological surveys. One example are redshift-space distortions from the peculiar motion of galaxies. In fact, the peculiar motions allow us to extract additional cosmological information, as they directly probe the growth rate of cosmic structures.
We are continuing to advance the technique to apply it to state-of-the-art cosmological observations, where they will hopefully allow us to better understand dark matter and dark energy. A comparative analysis that we performed recently shows that field-level analysis indeed yields tighter measurements than the standard approach, so stay tuned for some exciting highlights we hope to share soon.
How does this help to answer the questions posed above? The properties of dark matter and dark energy are built into our numerical forward model for structure formation. We can now modify these parameters and see how the agreement with the data changes. For example, changing the amount of dark energy or how it evolves leads to a different expansion rate of the universe, which in turn affects the growth of structure. This comparison therefore allows us to measure key parameters of dark matter and dark energy.
The field-level analysis extracts all information available in the large-scale galaxy distribution; our ultimate goal is to achieve a much more precise measurement of dark matter and dark energy than current state-of-the-art analyses. The latter only compare theory and data at the level of highly-compressed summary statistics (such as the RMS fluctuations as a function of scale). A further advantage of forward modeling is that we can incorporate observational and instrumental effects present in cosmological surveys. One example are redshift-space distortions from the peculiar motion of galaxies. In fact, the peculiar motions allow us to extract additional cosmological information, as they directly probe the growth rate of cosmic structures.
We are continuing to advance the technique to apply it to state-of-the-art cosmological observations, where they will hopefully allow us to better understand dark matter and dark energy. A comparative analysis that we performed recently shows that field-level analysis indeed yields tighter measurements than the standard approach, so stay tuned for some exciting highlights we hope to share soon.
Authors:
Julia Stadler
Postdoc
tel:2205
jstadler@mpa-garching.mpg.de
Fabian Schmidt
Scientific Staff Member of the works council,
Representative of the Scientific Coworkers
tel:2274
fschmidt@mpa-garching.mpg.de
Beatriz Tucci Schiewaldt
PhD student
tel:2358
tucci@mpa-garching.mpg.de
Original publications:
1. Fabian Schmidt
An n-th order Lagrangian Forward Model for Large-Scale Structure
JCAP 04 (2021), 033
DOI
2. Andrija Kostić, Nhat-Minh Nguyen, Fabian Schmidt, Martin Reinecke
Consistency tests of field level inference with the EFT likelihood
JCAP 07 (2023), 063
DOI
3. Julia Stadler, Fabian Schmidt, Martin Reinecke
Cosmology inference at the field level from biased tracers in redshift-space
JCAP 10 (2023), 069
DOI
