🔭 astrophysics

Tracing the dynamical states and mass accretion histories of galaxy clusters in IllustrisTNG

Using IllustrisTNG simulations, this study demonstrates that observable structural parameters, such as the magnitude gap between brightest galaxies and stellar mass asymmetry, effectively correlate with and can be used to trace the assembly histories of galaxy clusters, suggesting that structural classification should be explicitly incorporated into analyses of cluster scaling relations and completeness.

Original authors: Rashaad Reid, Syeda Lammim Ahad, Roan Haggar, Charlie T. Mpetha, James E. Taylor

Published 2026-06-01

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CC BY 4.0

Original authors: Rashaad Reid, Syeda Lammim Ahad, Roan Haggar, Charlie T. Mpetha, James E. Taylor

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine the universe as a giant, cosmic construction site. For billions of years, it has been building structures out of dark matter and gas, starting with small bricks and stacking them up to form massive skyscrapers called galaxy clusters. These clusters are the biggest things in the universe held together by gravity.

The paper you're asking about is like a detective story. The authors want to figure out how these cosmic skyscrapers were built. Did they grow slowly and peacefully over time? Or were they the result of violent, recent crashes where smaller buildings smashed into the big one?

Here is the breakdown of their investigation, explained simply:

The Problem: The "Look" Can Be Deceiving

In the real world, if you see a house with a messy yard and a crooked fence, you might guess it was just built or recently renovated. If you see a perfectly manicured lawn, you guess it's been settled for a long time.

Astronomers have long tried to do the same with galaxy clusters. They look at how "relaxed" (calm and orderly) or "unrelaxed" (messy and chaotic) a cluster looks. They assume:

Relaxed clusters = Old, finished construction.
Unrelaxed clusters = Young, still under construction or recently crashed.

However, the authors realized that just looking at a cluster isn't always enough. There are many ways to measure "messiness," and they wanted to know: Which specific measurement is the best detective for figuring out how recently a cluster was built?

The Tool: A Cosmic Time Machine (Simulations)

Since we can't travel back in time to watch a cluster being built, the authors used a super-powerful computer simulation called IllustrisTNG. Think of this as a massive, ultra-realistic video game of the universe.

In this game, they created thousands of galaxy clusters. Because it's a computer game, they knew the "secret history" of every single cluster: exactly when it formed, how fast it grew, and when its last big crash happened. This is their "ground truth."

The Experiment: The "Split Sample" Test

The authors played a sorting game. They took their simulated clusters and sorted them into two piles based on different "messiness" rules:

The "Center of Gravity" Test: Did the center of the cluster's mass match up with its brightest star? (If they are far apart, it's messy).
The "Shape" Test: Is the cluster a perfect sphere, or is it stretched out like a rugby ball?
The "Density" Test: Is the matter packed tightly in the middle, or spread out?
The "Asymmetry" Test: Is the cluster lopsided? (Like a pizza with all the toppings on one side).
The "Galaxy Gap" Test: Is the biggest galaxy in the cluster much bigger than the second biggest? (If yes, the big one has eaten all the others and is "relaxed." If no, they are still fighting).

For each test, they took the top 20% (the messiest) and the bottom 20% (the calmest) and compared their secret histories.

The Findings: Who Wins the Detective Award?

The authors found that some "messiness" tests were much better detectives than others.

The Losers (Hard to use in real life):
- Measuring the exact 3D shape or the total mass distribution is great for finding the history, but we can't do this easily in the real universe because we can't see "through" the cluster to get the full 3D picture. It's like trying to guess the shape of a cake just by looking at a shadow.
The Winners (Easy to use with telescopes):
- Stellar Asymmetry: This measures if the stars are spread out evenly or if they are clumped on one side. This was a huge winner. Even just looking at the light from the stars (which is easy to do), they could tell if a cluster had recently crashed.
- Magnitude Gap: This measures the difference in brightness between the #1 galaxy and the #2 galaxy. If the #1 galaxy is a giant superstar and the #2 is a tiny dwarf, the cluster is likely old and calm. If they are close in size, the cluster is likely young and still chaotic. This was also a very strong detective.

The "So What?"

The paper concludes that we don't need to know the full, complex 3D map of a galaxy cluster to know its history. We can just look at how lopsided the stars are or how much bigger the biggest galaxy is compared to the second biggest.

If a cluster looks lopsided or has two similarly sized giants, it's likely a "new" cluster that just had a big crash. If it's symmetrical and has one clear boss galaxy, it's an "old" cluster that has settled down.

Why Does This Matter?

The authors say that if we want to use galaxy clusters to measure the expansion of the universe (cosmology), we need to be careful. Mixing up "new, messy" clusters with "old, calm" clusters is like mixing fresh concrete with old bricks and trying to build a wall; the results will be inaccurate.

By using these simple "messiness" checks (Asymmetry and Magnitude Gap), astronomers can sort their lists of clusters into "calm" and "chaotic" groups. This makes their measurements of the universe much more precise, ensuring they aren't getting fooled by the construction noise of the universe's recent past.

In short: The paper teaches us that to tell the story of a galaxy cluster's past, we don't need a complex 3D scan. We just need to check if the stars are sitting evenly or if the biggest galaxy has clearly won the fight for dominance.

Problem Statement
Galaxy clusters, as the most massive gravitationally bound structures, serve as critical probes for cosmology and the study of structure formation. However, current cluster samples are often treated as homogeneous based solely on mass and redshift. This assumption overlooks the intrinsic variation in cluster properties driven by their assembly histories. Systems with recent accretion (unrelaxed) differ significantly from those with older, quiescent histories (relaxed) in terms of dark matter structure, gas properties, and scaling relations. While X-ray and Sunyaev-Zel'dovich (SZ) observations can distinguish dynamical states, upcoming large optical surveys (e.g., Euclid, LSST) will primarily detect clusters via optical properties with limited spectroscopic follow-up. A practical challenge remains: can clusters be structurally classified based on optical properties alone to form more homogeneous subsamples for stacking and analysis, thereby reducing biases in scaling relations and mass estimates?

Methodology
The authors utilize the IllustrisTNG cosmological simulations, specifically the TNG300-1 run, to investigate the correlation between structural parameters and mass accretion histories (MAH). The study employs a "split sample" approach:

Sample Definition: The authors select halos within specific mass ranges ( $5 \times 10^{13} \le M_{200c}/M_\odot \le 1 \times 10^{14}$ ) at redshift $z=0$ .
Structural Indicators: A suite of structural parameters is measured, categorized by their observability:
- Intrinsic (3D): 3D centre of mass offset, 3D axis ratio, 3D concentration ( $c_{200c}$ ), and total mass asymmetry.
- Projected (Mass-based): Projected centre of mass offset and total mass asymmetry.
- Optical (Stellar/Galaxy-based): Projected stellar offset, projected galaxy offset, stellar mass asymmetry, magnitude gap ( $r_{12}$ ), and stellar mass ratio ( $m_{14}$ ).
Growth History Metric: The MAH is characterized primarily by $z_{75}$ , the redshift at which the main progenitor branch reached 75% of its final mass. The accretion rate ( $\Gamma_{dyn}$ ) is also considered.
Split Sample Analysis: For each structural parameter, the sample is sorted, and the top and bottom 20% (representing the most and least relaxed systems) are isolated. The median MAH and mass accretion rates (MAR) of these subsamples are compared.
Quantification: The effectiveness of each split is quantified using a maximum normalised difference metric ( $D$ ), which measures the separation between the median accretion rates of the relaxed and unrelaxed subsamples relative to their uncertainties.

Key Contributions and Results
The paper establishes that several observable quantities, particularly those derived from stellar distributions, are effective proxies for distinguishing between clusters with recent and ancient accretion histories.

Correlation Strength: The 3D centre of mass offset shows the strongest intrinsic correlation with MAH ( $D=7.2$ ), followed by total mass asymmetry ( $D=5.8$ ). However, these require knowledge of the full mass distribution, which is difficult to obtain for large optical samples.
Optical Proxies: Crucially, the study finds that stellar mass asymmetry and magnitude gaps (specifically $r_{12}$ $r_{12}$ and $m_{14}$ $m_{14}$ ) are highly effective when applied to projected data.
- Stellar Mass Asymmetry: Despite being a projected quantity, it produces a strong separation in MAH ( $D=4.3$ ), nearly matching the performance of total mass asymmetry. It is robust to projection effects and does not require mass modeling.
- Magnitude Gap & Stellar Mass Ratio: These optical metrics also yield significant separation ( $D=4.4$ for projected versions), performing nearly as well as more complex structural measures.
Projection Effects: While 3D measures generally trace MAH with less contamination, projected versions of these measures (e.g., projected centre of mass offset) retain significant discriminatory power ( $D=6.1$ ). However, measures relying on galaxy positions alone (projected galaxy offset) show weaker correlations ( $D=3.7$ ) due to increased scatter.
Scaling Relations: The authors demonstrate a practical application by fitting mass-richness scaling relations to the split samples. They find that relaxed and unrelaxed clusters exhibit consistent, albeit statistically modest in this sample size, differences in the normalization and slope of the scaling relation. Relaxed clusters tend to have lower normalizations and steeper slopes.

Significance and Claims
The paper claims that structural classification should be explicitly included in analyses of catalogue completeness, scaling relations, and mean density profiles. The primary significance lies in identifying stellar mass asymmetry, magnitude gap, and stellar mass ratio as the most practical tools for the next generation of optical surveys. These metrics allow for the creation of more homogeneous subsamples of "relaxed" and "unrelaxed" clusters without requiring ancillary X-ray, SZ, or weak lensing data. By separating clusters based on these optical indicators, researchers can potentially reduce scatter in scaling relations and mitigate biases in mass estimates caused by the varying dynamical states of cluster populations. The authors note that while their results are based on a specific simulation suite (IllustrisTNG), the identified correlations align with previous literature and offer a pathway to refine cosmological tests using large-scale optical data.