Exploring gas thermodynamics around galaxies from the Sunyaev-Zel'dovich effects: impact of galaxy-halo connection, 2D projection and velocity field

This paper utilizes hydrodynamical simulations to demonstrate that galaxy-halo connection parameters, 2D projection effects, and velocity field treatments significantly bias the interpretation of Sunyaev-Zel'dovich signals around galaxies, thereby necessitating careful modeling to accurately constrain gas thermodynamics and feedback processes.

The Gist

Imagine the universe as a giant, invisible ocean. In this ocean, galaxies are like islands, and surrounding them is a vast, hot, invisible fog made of gas. This fog is so hot and energetic that it interacts with the "afterglow" of the Big Bang (the Cosmic Microwave Background, or CMB), leaving tiny fingerprints on it.

Scientists want to study this fog to understand how galaxies grow and how they push gas around (a process called "feedback"). However, looking at this fog is tricky. It's like trying to understand the shape of a 3D cloud just by looking at its shadow on a wall.

This paper is a "stress test" for the tools scientists use to study this fog. The authors used a super-computer simulation (a digital universe) to see how different factors might trick their measurements. Here is the breakdown of their findings using simple analogies:

1. The "Shadow" Problem (2D vs. 3D)

The Analogy: Imagine you are trying to guess how much water is in a swimming pool by looking at a flat photo of the surface. If you just look at the photo, you might think the pool is deeper or has more water than it actually does because you are seeing the water from the front and the back all squashed together.
The Finding: When scientists look at the gas around galaxies, they see a 2D "shadow" on the sky. The paper shows that if you just measure this shadow, you might overestimate how much gas is actually there. The "shadow" includes gas that is far away in the background, making the galaxy look like it has a bigger, denser atmosphere than it really does.

2. The "VIP" and the "Regulars" (Centrals vs. Satellites)

The Analogy: Think of a galaxy cluster as a party.

• Central Galaxies are the hosts living in the main house.
• Satellite Galaxies are the guests living in the guest houses or nearby cottages.
  The paper found that the "guests" (satellites) often live in much bigger, more crowded neighborhoods (massive halos) than the "hosts." Because they live in these bigger neighborhoods, they are surrounded by more gas.
  The Finding: If you don't know exactly how many "guests" are at the party, your measurement of the gas will be wrong. Even a tiny mistake in counting the guests (a 1% error) can lead to a 2–5% error in your calculation of the gas pressure. It's like trying to calculate the total noise level of a party; if you miscount the loud guests, your estimate of the noise is way off.

3. The "Rock Stars" (Massive Halos)

The Analogy: Imagine a concert where 98% of the audience is sitting quietly, but 2% are rock stars screaming at the top of their lungs. If you measure the "average volume" of the room, those 2% rock stars will dominate the sound.
The Finding: The gas around the most massive galaxies (the "rock stars") is so intense that it completely dominates the signal. If you remove just the top 2% of the most massive galaxies from your data, the signal for certain types of gas measurements (like the relativistic effect) drops by 75%. This means different types of gas measurements are actually looking at different groups of galaxies, even if you think you are looking at the same sample.

4. The "Wind" vs. The "Wave" (The Doppler Effect)

The Analogy: Imagine you are trying to hear a specific drum beat (the gas moving around a galaxy). But there is a giant wind blowing through the whole stadium (the large-scale motion of the universe). This wind creates a "whoosh" sound that drowns out the drum.
The Finding: In small computer simulations, this "wind" (the Doppler term) is so strong it looks like part of the drum beat. It creates a fake signal. However, the paper shows that if you use a special mathematical filter (called a "Compensated Aperture" filter), it's like putting on noise-canceling headphones. The filter cancels out the constant "wind" noise, leaving you with a clear picture of the actual drum beat (the gas around the galaxy).

Why Does This Matter?

The universe is trying to tell us a story about how galaxies form and how they interact with the gas around them. But if we don't account for:

• The "shadow" effect (looking at 2D instead of 3D),
• The mix of "hosts" and "guests" (satellite fractions),
• The loud "rock stars" (massive outliers), and
• The background "wind" (Doppler noise),

...we might tell the wrong story.

The Bottom Line: This paper is a manual for future scientists. It says, "Hey, when you look at the gas around galaxies, make sure you correct for these specific tricks, or your measurements will be biased." By understanding these pitfalls, future telescopes (like the ones mentioned in the paper) can give us a much clearer, more accurate picture of the invisible universe.

Technical Summary

Here is a detailed technical summary of the paper "Exploring gas thermodynamics around galaxies from the Sunyaev-Zel'dovich effects: impact of galaxy-halo connection, 2D projection and velocity field."

1. Problem Statement

Recent measurements of the Kinematic Sunyaev-Zel'dovich (kSZ) effect around galaxies suggest that circumgalactic gas extends to megaparsec scales, implying powerful feedback processes and significant baryonic corrections to weak lensing. However, interpreting these observations requires disentangling complex systematic effects. Key uncertainties include:

• Galaxy-Halo Connection: How do the properties of central vs. satellite galaxies (specifically the satellite fraction) and the presence of high-mass outliers affect the stacked signals?
• 2D Projection Effects: How does projecting 3D gas distributions into 2D profiles bias inferred quantities like gas fractions?
• Velocity Fields: To what extent do large-scale velocity modes (the Doppler term) contaminate kSZ measurements, and are current simulation volumes sufficient to model these effects?
• Signal Decomposition: How do the 1-halo (intra-halo) and 2-halo (inter-halo) terms contribute to the total signal, particularly for different SZ observables (thermal, kinematic, relativistic)?

The authors aim to quantify these systematic effects to ensure future high-precision surveys (e.g., DESI, CMB-S4, Simons Observatory) can accurately recover baryonic feedback constraints.

2. Methodology

The study utilizes a combination of hydrodynamical and N-body simulations to model Sunyaev-Zel'dovich (SZ) signals around Luminous Red Galaxies (LRGs) similar to those in the DESI survey.

• Simulations:
  • IllustrisTNG-300: A magnetohydrodynamical simulation ($205 , \text{cMpc}/h$box) used to generate realistic gas profiles (density, temperature, pressure) and a catalog of DESI-like LRGs. The sample includes$\sim4,600$galaxies with a satellite fraction of$\sim20%$.
  • AbacusSummit: A large N-body simulation ($2000 , \text{cMpc}/h$ box) used specifically to model the large-scale velocity field and the Doppler term, as the IllustrisTNG box is too small to capture full convergence of velocity correlations.
• SZ Stacking Procedure:
  • The authors mimic observational stacking techniques used in recent DESI/ACT analyses.
  • tSZ (Thermal): Simple averaging of temperature maps around galaxy positions.
  • kSZ (Kinematic): Weighted stacking using the line-of-sight (LOS) peculiar velocities of the galaxies.
  • Filtering: Application of the Compensated-Aperture Photometry (CAP) filter to remove primary CMB contamination and constant background fields.
• Decomposition:
  • Signals are decomposed into 1-halo (gas within the host halo, $R < R_{200c}$) and 2-halo (gas from neighboring halos projected along the LOS) terms.
  • Profiles are analyzed in both 3D (intrinsic) and 2D (projected) to isolate projection biases.
• Parameter Variations:
  • The satellite fraction ($f_{sat}$) is varied by $\pm 1\%, \pm 3\%, \pm 7\%$ to test sensitivity.
  • The most massive halos are masked to test the impact of high-mass outliers.

3. Key Contributions and Results

A. 2D Projection Biases and Gas Fractions

• Overestimation of Gas Fractions: The study demonstrates that inferring baryon fractions from 2D projected profiles leads to systematic overestimation. While 3D profiles converge to the cosmological baryon fraction ($\Omega_b/\Omega_m$) at large radii, 2D profiles integrate gas along the entire LOS, artificially enhancing the signal.
• Implication: Directly converting 2D SZ profiles to gas fractions without correcting for projection effects yields biased results.

B. 1-Halo vs. 2-Halo Contributions

• Non-Negligible 2-Halo Term in 2D: In 3D, the 2-halo term is negligible at small radii ($R \lesssim 1 \, \text{cMpc}/h$). However, in 2D projections, the 2-halo term remains significant even where the 1-halo term dominates.
  • For gas density, the 2-halo contribution is $\sim20\%$ of the 1-halo term at $R \approx 0.1 \, \text{cMpc}/h$.
  • For tSZ, it can reach $\sim40\%$ for group-scale halos.
  • For rSZ (relativistic), the 2-halo contribution is small ($\sim1\%$) due to the steep mass dependence ($M^{7/3}$) which suppresses contributions from lower-mass neighbors.
• Satellite Influence: Satellite galaxies reside in more massive halos and exhibit significantly larger 1-halo and 2-halo terms than centrals. However, because satellites are less numerous, their contribution to the total stacked signal is down-weighted.

C. Sensitivity to Satellite Fraction and Mass Outliers

• Satellite Fraction Sensitivity: The SZ signal amplitude is highly sensitive to the satellite fraction ($f_{sat}$). A mere $\pm 1\%$ uncertainty in $f_{sat}$ propagates to:
  • $\sim 2\%$ uncertainty in the kSZ signal.
  • $\sim 3\%$ uncertainty in the tSZ signal.
  • $\sim 5\%$ uncertainty in the rSZ signal.
  • Reason: Quantities with steeper mass dependence (pressure, rSZ) are more strongly affected by the massive halos where satellites preferentially reside.
• Impact of Massive Halos: The most massive halos dominate the stacked signal despite being a small fraction of the population.
  • Masking the top 2% of the most massive halos reduces the rSZ amplitude by 75%.
  • Masking the top 2% reduces the tSZ amplitude by 40%.
  • Masking the top 2% reduces the kSZ amplitude by 10%.
  • Conclusion: Different SZ observables effectively probe different subsets of the halo population.

D. The Doppler Term and Simulation Size

• Doppler Contamination: The kSZ signal contains a "Doppler term" arising from large-scale velocity modes. In small simulation boxes (like IllustrisTNG), this term does not cancel out and can dominate the signal at large radii ($R \gtrsim 1 \, \text{cMpc}/h$).
• Convergence: The velocity correlation function requires simulation boxes of $\sim 2000 \, \text{cMpc}/h$ (like AbacusSummit) to converge. In smaller boxes, the Doppler term appears as a nearly constant offset.
• CAP Filter Solution: Crucially, the Compensated-Aperture Photometry (CAP) filter, used in real observational analyses, automatically suppresses the Doppler term. By subtracting the signal in an outer ring from an inner aperture, the filter removes the constant (flat) Doppler contribution, leaving a clean kSZ profile dominated by the Ostriker-Vishniac term (density fluctuations).

4. Significance and Conclusions

This paper provides a critical "preparatory exercise" for upcoming high-precision cosmological surveys. The findings highlight that:

1. Systematics are dominant: Projection effects, satellite fractions, and the presence of massive outliers introduce biases that can rival the statistical precision of future experiments.
2. Modeling requirements: Accurate interpretation of SZ data requires hydrodynamical simulations large enough to capture the 2-halo term and velocity fields, or analytical corrections for these effects.
3. Filtering efficacy: The CAP filter is robust against the artificial Doppler term caused by finite simulation volumes, validating current observational analysis pipelines.
4. Precision constraints: To achieve $\sim1\%$ precision on gas profiles, the uncertainty in the satellite fraction must be constrained to the sub-percent level, and the impact of assembly bias and high-mass outliers must be carefully modeled.

The study underscores that extracting robust constraints on baryonic feedback and gas physics from SZ measurements requires a holistic understanding of the galaxy-halo connection, projection geometry, and velocity field modeling.