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Euclid: Measuring the intrinsic alignment of galaxies around cosmic voids in the \Euclid Flagship simulation\

This paper presents a methodology using the Euclid Flagship simulation to measure and model the intrinsic alignment of red and blue galaxies around cosmic voids, finding that their alignment evolution matches the general population and providing estimates of void linear bias to mitigate systematic effects in future cosmological analyses.

Original authors: P. Vielzeuf, M. -C. Cousinou, A. Pisani, S. Escoffier, E. J. Gonzalez, W. d'Assignies, E. Jullo, M. Ghodsi Yengejeh, A. Kovács, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, S. Bardelli, P. Batt

Published 2026-07-07

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CC BY 4.0

Original authors: P. Vielzeuf, M. -C. Cousinou, A. Pisani, S. Escoffier, E. J. Gonzalez, W. d'Assignies, E. Jullo, M. Ghodsi Yengejeh, A. Kovács, S. Andreon, N. Auricchio, C. Baccigalupi, M. Baldi, S. Bardelli, P. Battaglia, A. Biviano, E. Branchini, M. Brescia, S. Camera, V. Capobianco, C. Carbone, V. F. Cardone, J. Carretero, M. Castellano, G. Castignani, S. Cavuoti, A. Cimatti, C. Colodro-Conde, G. Congedo, L. Conversi, Y. Copin, F. Courbin, H. M. Courtois, H. Degaudenzi, S. de la Torre, G. De Lucia, H. Dole, F. Dubath, X. Dupac, M. Farina, R. Farinelli, S. Farrens, F. Faustini, S. Ferriol, F. Finelli, P. Fosalba, S. Fotopoulou, N. Fourmanoit, M. Frailis, E. Franceschi, M. Fumana, S. Galeotta, K. George, W. Gillard, B. Gillis, C. Giocoli, P. Gómez-Alvarez, J. Gracia-Carpio, A. Grazian, F. Grupp, S. V. H. Haugan, H. Hoekstra, W. Holmes, F. Hormuth, A. Hornstrup, K. Jahnke, M. Jhabvala, B. Joachimi, S. Kermiche, A. Kiessling, M. Kilbinger, B. Kubik, M. Kümmel, M. Kunz, H. Kurki-Suonio, A. M. C. Le Brun, S. Ligori, P. B. Lilje, V. Lindholm, I. Lloro, G. Mainetti, O. Mansutti, O. Marggraf, M. Martinelli, N. Martinet, F. Marulli, R. J. Massey, S. Maurogordato, E. Medinaceli, S. Mei, M. Meneghetti, E. Merlin, G. Meylan, A. Mora, M. Moresco, L. Moscardini, C. Neissner, S. -M. Niemi, J. W. Nightingale, C. Padilla, S. Paltani, F. Pasian, K. Pedersen, V. Pettorino, A. Pezzotta, S. Pires, G. Polenta, L. A. Popa, L. Pozzetti, F. Raison, A. Renzi, J. Rhodes, G. Riccio, E. Romelli, M. Roncarelli, C. Rosset, R. Saglia, Z. Sakr, A. G. Sánchez, D. Sapone, B. Sartoris, P. Schneider, T. Schrabback, A. Secroun, E. Sihvola, P. Simon, C. Sirignano, G. Sirri, A. Spurio Mancini, L. Stanco, P. Tallada-Crespí, A. N. Taylor, H. I. Teplitz, I. Tereno, N. Tessore, S. Toft, R. Toledo-Moreo, F. Torradeflot, I. Tutusaus, J. Valiviita, T. Vassallo, G. Verdoes Kleijn, A. Veropalumbo, Y. Wang, J. Weller, G. Zamorani, F. M. Zerbi, E. Zucca, T. Castro, M. Sereno, M. Viel, D. Navarro-Gironés

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 not as a static picture, but as a giant, three-dimensional sponge. Most of the sponge is made of "stuff" (galaxies and dark matter), but there are also huge, empty holes running through it. Astronomers call these holes cosmic voids.

This paper is like a detective story about what happens to the "stuff" (galaxies) that lives right next to these empty holes. Specifically, the authors wanted to see if the shapes of galaxies get stretched or twisted by the empty space around them, and if this stretching changes as we look further back in time.

Here is a breakdown of their findings using simple analogies:

1. The Problem: The "Fake" Signal

When astronomers look at distant galaxies, they try to measure how much the universe's gravity bends light (like a lens). This is called gravitational lensing. It helps them map out invisible "dark matter."

However, there is a problem. Galaxies aren't just random blobs; they are like little spinning tops. Sometimes, the local gravity around them naturally stretches them into an oval shape before the light even travels to us. This is called Intrinsic Alignment (IA).

Think of it like this: If you are trying to measure how much a wind (gravity) bends a flag (light), but the flag was already tied into a knot (intrinsic alignment) by the pole it's attached to, you might get the wrong measurement of the wind. The authors wanted to figure out exactly how much these "knots" exist around the cosmic voids so they don't mess up future measurements.

2. The Experiment: A Digital Universe

Since we can't travel back in time to watch the universe evolve, the team used a super-computer simulation called the Euclid Flagship. This is a massive, virtual universe that mimics the real one, complete with billions of fake galaxies and cosmic voids.

They created a "void catalogue" by finding the biggest empty spots in this digital sponge. They then looked at the galaxies living on the edges of these holes to see how they were oriented.

3. The Findings: How Galaxies "Pose"

The team looked at two types of galaxies, which they color-coded for simplicity:

Red Galaxies: These are like old, heavy, elliptical balls of stars.
Blue Galaxies: These are like younger, spiral pinwheels.

What they found:

The "Hula Hoop" Effect: Inside the voids, the red galaxies tended to line up their long axes around the hole, like a hula hoop. They didn't point at the hole; they wrapped around it.
The "Sunbeam" Effect: Just outside the voids, the galaxies tended to point toward the hole, like sunbeams radiating from a center.
Red vs. Blue: The red galaxies showed a much stronger "stretching" effect than the blue ones. The blue galaxies were much more random, like leaves blowing in the wind.

4. Time Travel: Looking Back

The simulation allowed them to look at different "eras" of the universe (different redshifts).

The Trend: They found that the "stretching" effect gets stronger the further back in time they look.
The Analogy: Imagine a rubber band. In the early universe (high redshift), the rubber band was tighter and more elastic, so the galaxies were more easily pulled into alignment. As the universe got older and the structures settled down, the alignment became slightly less intense.

5. The "Bias" of the Void

The authors also calculated something called Void Bias.

The Metaphor: Imagine you are trying to find a quiet neighborhood (a void) in a noisy city. If the quiet neighborhood is surrounded by loud construction sites (overdense regions), it stands out more.
The Result: They found that smaller voids act like "loud" neighborhoods. They are more strongly connected to the surrounding matter than larger, deeper voids. As the universe ages, these small voids seem to become more "connected" to the rest of the cosmic web.

Why This Matters

The authors conclude that if future telescopes (like the real Euclid satellite) want to use cosmic voids to measure the universe's expansion or dark energy, they must account for this "knotting" of galaxies.

If they ignore the fact that galaxies naturally align around voids, they might think the universe is expanding differently than it actually is. By understanding this "intrinsic alignment," they can clean up the data, ensuring that when they measure the cosmic "wind," they aren't accidentally measuring the "knots" in the flag.

In short: The paper maps out how galaxies naturally stretch around the universe's biggest empty holes, proving that this effect is real, changes over time, and must be corrected for to get accurate maps of the cosmos.

Technical Summary: Euclid: Measuring the intrinsic alignment of galaxies around cosmic voids in the Euclid Flagship simulation

Problem Statement
Gravitational lensing is a primary probe for constraining cosmological parameters, yet it is contaminated by the intrinsic alignment (IA) of galaxies, where local tidal fields align galaxy shapes with the surrounding gravitational potential. While IA around overdense structures (clusters, filaments) is well-studied, the IA signal in underdense environments—specifically cosmic voids—remains poorly characterized. This lack of understanding poses a risk for future cosmological analyses, particularly those utilizing void-lensing measurements from upcoming Stage IV surveys like Euclid. If unmodeled, IA in voids could act as a systematic error, mimicking or obscuring the weak lensing signal. Furthermore, characterizing IA in these low-density regions offers a unique opportunity to study galaxy-environment interactions and the linear bias of voids.

Methodology
The authors utilize the Euclid Flagship simulation (FS2-Wide), a cosmological N-body simulation populated with a galaxy mock catalogue generated via the SciPIC pipeline. The analysis proceeds through the following steps:

Void Identification: A 2D void-finder algorithm (Sánchez et al. 2017) is applied to smoothed galaxy density maps (using HEALPix pixelization) to identify cosmic voids. The study focuses on voids with radii in the range $10 < R_V/(h^{-1}\text{Mpc}) < 15$ , corresponding to the peak of the void radius distribution.
Galaxy Selection: Galaxies are separated into red and blue populations using the color_kind column from the simulation, which classifies them based on a Gaussian fit to the $g-r$ color-magnitude diagram. This allows for a comparison of alignment behaviors between elliptical (red) and spiral (blue) galaxies.
Signal Measurement: The projected correlation function $w^I_{V+}(r_p)$ between void centers and galaxy intrinsic ellipticities is measured using a Davis–Peebles estimator. The signal is integrated along the line of sight ( $\Pi$ ) within $\pm 100 \, h^{-1}\text{Mpc}$ to mitigate redshift-space distortions.
Theoretical Modeling: The analysis employs a Linear Alignment (LA) model, decomposing the signal into two regimes:
- Intermediate-Scale Regime (ISR): Inside the void radius, where the void-matter power spectrum is inferred directly from the measured weak lensing signal ( $\Delta\Sigma$ ) of the voids. This requires reconstructing the 3D matter density profile from the 2D lensing signal using an Abel transform.
- Large-Scale Regime (LSR): Outside the void, where voids are treated as biased tracers of the underlying matter field. The signal is modeled using the linear void bias $b_V(z)$ and the linear matter power spectrum.
Parameter Estimation: The model fits the measured correlation functions to constrain the IA amplitude parameter $A_I$ and the void bias $b_V(z)$ . The redshift evolution of $A_I$ is modeled as a power law, $A_I(z) = A_z [(1+z)/(1+z_p)]^{\eta_1}$ . Covariance matrices are estimated via a jackknife resampling technique.

Key Results

IA Signal Detection: The study detects a significant IA signal around cosmic voids. Within the void interiors (ISR), galaxies exhibit a tangential alignment (negative signal), while in the surrounding regions (LSR), a weaker radial alignment (positive signal) is observed.
Redshift Evolution: The amplitude of the IA signal ( $A_I$ $A_{I}$ ) increases with redshift for both red and blue galaxy samples.
- For red galaxies, the best-fit parameters are $A_z = 2.02 \pm 0.11$ and $\eta_1 = 1.86 \pm 0.18$ .
- For blue galaxies, the parameters are $A_z = 0.63 \pm 0.07$ and $\eta_1 = 3.74 \pm 0.32$ .
- The redshift evolution of the IA amplitude in voids is found to be broadly consistent with measurements of IA in the general galaxy population (specifically galaxy-galaxy correlations) from a companion study (Euclid Collaboration: Paviot et al. 2026).
Void Bias: The analysis estimates the linear bias of voids, $b_V(z)$ , for the selected radius range. The results show a positive trend where the bias increases with redshift, consistent with theoretical predictions for the clustering of underdense regions.
Model Consistency: The two-component model (ISR + LSR) successfully describes the data across different redshift bins, with reduced $\chi^2$ values indicating a good fit.

Significance and Claims
The paper claims to provide one of the first detailed investigations of intrinsic alignments specifically within cosmic void environments. Its primary significance lies in:

Systematic Mitigation: Highlighting that IA in voids constitutes a non-negligible systematic for future void-lensing cosmology. Accurate modeling is essential to prevent biases in cosmological parameter estimation.
Methodological Framework: Demonstrating a self-consistent approach that uses the void-lensing signal itself to reconstruct the matter density profile, thereby avoiding the introduction of additional free parameters when modeling the IA signal.
Cosmological Insight: Confirming that the linear alignment model is appropriate for void environments, where nonlinear structure formation effects are minimal. The results also validate the use of void bias as a probe of large-scale structure evolution, showing that smaller voids (void-in-clouds) exhibit positive bias that evolves with redshift.

The authors conclude that these findings prepare the groundwork for analyzing Euclid observational data, emphasizing the necessity of accounting for IA in void studies to fully exploit the potential of voids as cosmological probes.