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Intrinsic galaxy alignments in CAMELS simulations and the significant impact of baryon model

This study utilizes the CAMELS hydrodynamic simulations to demonstrate that intrinsic galaxy alignments are strongly influenced by cosmological parameters and supernova feedback, with quiescent galaxies exhibiting significantly higher alignment amplitudes than star-forming ones and distinct feedback mechanisms operating across different galaxy populations.

Original authors: Daniel Bilsborrow, Niall Jeffrey

Published 2026-02-03

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CC BY 4.0

Original authors: Daniel Bilsborrow, Niall Jeffrey

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, invisible ocean of dark matter. Floating in this ocean are galaxies, which are like islands of stars. For a long time, scientists have wondered: do these islands just float randomly, or do they line up with the currents of the dark matter ocean? This lining-up is called intrinsic alignment.

This paper is like a massive, controlled experiment in a digital laboratory. The researchers didn't look at real galaxies in the sky (which can be messy and hard to control). Instead, they used a super-computer suite called CAMELS to create 1,000 different "universes."

Here is the simple breakdown of what they did and what they found:

1. The Digital Laboratory (The Simulation)

Think of the CAMELS simulations as 1,000 different video game worlds. In each world, the scientists tweaked the "settings" (the parameters) to see how the game changed.

Cosmological Settings: They changed the amount of matter in the universe and how clumpy that matter is (like changing the gravity or the density of the fog).
Astrophysical Settings: They changed how stars explode (supernovae) and how black holes behave (AGN feedback). Think of this as changing how much "wind" or "explosions" happen in the game, which pushes gas and stars around.

2. The Main Discovery: It's All About the "Explosions"

The researchers looked at how the shapes of the galaxies lined up with the dark matter. They found that the alignment isn't just about gravity; it's heavily influenced by supernova feedback.

The Analogy: Imagine a calm pond (the dark matter). If you drop a stone (gravity), ripples form. But if you have a giant fan blowing across the water (supernova explosions), the ripples get messed up or changed.
The Finding: The strength of the galaxy alignment depended heavily on how strong the "fan" (supernova explosions) was. Surprisingly, the "black hole jets" (AGN feedback) didn't seem to matter much. The authors suggest this is because their digital universe was a bit too small to let the black holes show their full power, much like trying to study a hurricane in a bathtub.

3. The Quiet vs. The Active

The team split the galaxies into two groups:

Quiescent (Quiet) Galaxies: These are old, red galaxies that aren't making many new stars.
Star-Forming Galaxies: These are young, blue, active galaxies.

The Result: The "Quiet" galaxies were like soldiers marching in perfect lockstep with the dark matter currents. Their alignment was 10 times stronger than the "Star-Forming" galaxies. The active galaxies were more chaotic and didn't line up as well.

4. The "Shape" vs. The "Direction"

Galaxies have two main features:

Direction: Which way they are pointing.
Shape: How stretched out or squashed they look (their ellipticity).

The researchers did a clever trick: they took the "stretchiness" out of the equation and only looked at the direction.

The Finding: Even when they ignored how stretched the galaxies were, the direction still depended on the supernova explosions. This means supernovae don't just change how squashed a galaxy looks; they actually twist the galaxy to point in a different direction relative to the dark matter.

5. The "Simpson's Paradox" Twist

Here is the most interesting part. When they looked at all galaxies mixed together, the alignment seemed to depend on how "clumpy" the universe was (a parameter called $\sigma_8$ ).

The Twist: When they looked at the "Quiet" and "Active" groups separately, the "Active" group didn't care about clumpiness at all!
Why? It turns out that in "clumpier" universes, there are simply more "Quiet" galaxies. Since Quiet galaxies align so strongly, they dragged the average up. It was a statistical illusion: the universe didn't change the alignment of the active galaxies; it just changed the mix of galaxies, making the strong aligners more common.

Summary

This paper is a "control group" study for the universe. By running 1,000 different simulations, the authors proved that:

Supernova explosions are a major driver of how galaxies line up, not just gravity.
Old, quiet galaxies line up much better than young, active ones.
Black holes (in these specific small simulations) didn't seem to change the alignment much.
The way we measure these alignments can be tricky; sometimes it looks like one thing is causing an effect, but it's actually just a change in the types of galaxies present.

This helps scientists understand that to read the universe correctly (especially for future telescopes), they need to understand not just the gravity, but also the "weather" of supernovae that shapes the galaxies.

Technical Summary: Intrinsic Galaxy Alignments in CAMELS Simulations and the Significant Impact of Baryon Model

Problem Statement
Intrinsic alignments (IA) of galaxies serve as both a probe of galaxy formation and a major source of systematic error in weak gravitational lensing analyses. Current cosmological inference from photometric surveys is bottlenecked by uncertainties in IA modeling, particularly regarding how baryonic feedback processes (supernovae and active galactic nuclei) and cosmological parameters influence alignment signals. While theoretical frameworks exist (e.g., tidal alignment for early-type galaxies and tidal torquing for late-type), the specific dependence of IA on a wide range of astrophysical and cosmological parameters remains poorly constrained, especially when distinguishing between different galaxy populations (star-forming vs. quiescent).

Methodology
The authors utilize the CAMELS (Cosmology and Astrophysics with MachinE Learning Simulations) suite, specifically the Latin-Hypercube (LH) set of 1000 hydrodynamic simulations using the IllustrisTNG code. These simulations vary six key parameters: cosmological parameters ( $\Omega_m$ , $\sigma_8$ ) and astrophysical feedback parameters ( $A_{SN1}$ , $A_{SN2}$ for supernovae; $A_{AGN1}$ , $A_{AGN2}$ for AGN).

Data Construction: Galaxy shapes are measured at $z=0$ from subhaloes identified by the SUBFIND algorithm, selected based on stellar mass ( $M_{2r} > 10^8 M_\odot$ ) and particle count ( $N_s > 10$ ). Ellipticities are calculated from the second-moment tensor of stellar density in three Cartesian projections, yielding 3,000 catalogues.
Statistical Measures: The study measures the projected cross-correlation function between galaxy ellipticities and the underlying dark matter density field, denoted as $w_{m+}(r_\perp)$ . A null test is performed using the cross-component correlation $w_{m\times}$ .
Subsamples: Galaxies are split into "star-forming" and "quiescent" populations based on a specific star formation rate (sSFR) threshold of $10^{-10.5} \text{ yr}^{-1}$ .
Orientation-Only Analysis: To isolate the effect of orientation from ellipticity magnitude, the authors construct "orientation-only" catalogues where all ellipticity magnitudes are normalized to the mean value $\langle|\varepsilon|\rangle$ , calculating a corresponding correlation function $\tilde{w}_{m+}$ .
Parameter Dependence: Alignment amplitudes are defined as weighted averages of $w_{m+}$ across transverse separation bins. Pearson correlation coefficients and p-values are computed to test linear dependencies between these amplitudes and the simulation parameters. The ratio $w_{m+}/w_{mm}$ is also analyzed to test consistency with linear alignment models (NLA/TATT).

Key Results

Detection and Population Differences: A significant intrinsic alignment signal is detected in the CAMELS simulations. Quiescent galaxies exhibit alignment amplitudes approximately an order of magnitude larger than star-forming galaxies.
Parameter Dependencies:
- Cosmology: The alignment amplitude $w_{m+}$ shows statistically significant correlations with $\Omega_m$ and $\sigma_8$ . However, when the signal is normalized by the matter density auto-correlation ( $w_{mm}$ ), the dependence on $\sigma_8$ vanishes, consistent with linear alignment model predictions.
- Feedback: Significant correlations are found between alignment amplitudes and supernova feedback parameters ( $A_{SN1}$ and $A_{SN2}$ ). No significant dependence on AGN feedback parameters is observed, which the authors attribute to the limited simulation volume ( $(25 h^{-1} \text{ Mpc})^3$ ) restricting the range of large-scale modes.
Star-Forming vs. Quiescent Mechanisms:
- The correlation between alignment amplitude and $\sigma_8$ is insignificant for star-forming galaxies but strong for quiescent and full samples. The authors suggest the dependence in the full sample is driven by the increasing fraction of highly-aligned quiescent galaxies at higher $\sigma_8$ values.
- Supernova feedback ( $A_{SN1}$ ) shows a stronger correlation with alignment in star-forming galaxies than in quiescent ones. Conversely, in quiescent galaxies, $A_{SN1}$ primarily regulates specific star formation rates rather than alignment.
Ellipticity vs. Orientation: Normalizing ellipticity magnitudes (orientation-only analysis) retains the sensitivity to supernova feedback parameters. This indicates that supernova feedback influences not just the magnitude of galaxy ellipticity but also the orientation of galaxy shapes relative to the dark matter distribution.
Ratio Analysis: The ratio $w_{m+}/w_{mm}$ removes the $\sigma_8$ dependence but retains significant correlations with $\Omega_m$ and supernova feedback parameters. The authors note a surprising negative correlation with $\Omega_m$ in this ratio, which contradicts simple NLA/TATT predictions and warrants further investigation.

Significance and Claims
The paper claims to provide the first direct measurement of the dependence of intrinsic galaxy alignments on cosmological and astrophysical parameters over a wide, realistic range using a large suite of hydrodynamic simulations. The results offer key insights for:

Modeling: Demonstrating that baryonic feedback (specifically supernovae) significantly impacts alignment signals, suggesting that models must account for these processes to avoid biases in cosmological parameter estimation.
Galaxy Evolution: Highlighting distinct alignment mechanisms operating across galaxy types, where supernova feedback drives orientation changes in star-forming galaxies while regulating star formation rates in quiescent ones.
Future Surveys: Providing necessary constraints for pipeline validation and simulation-based inference for next-generation weak lensing surveys, emphasizing that mismatches between assumed baryonic feedback models and IA prescriptions can introduce significant biases.

The authors remain modest regarding the AGN feedback results, explicitly stating that the lack of detected impact is likely due to the small simulation volume, and they do not claim to have resolved the specific mechanism behind the unexpected $\Omega_m$ dependence in the normalized ratio.