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Cosmic-web quenching with DESI DR1: T-Web environments and mass-dependent red/blue classification

Using DESI DR1 data and T-Web formalism, this study demonstrates that while stellar mass is the primary driver of galaxy quenching, cosmic web environment provides a systematic secondary modulation that is most pronounced in dense knots and at lower stellar masses, with distinct redshift-dependent trends observed across BGS, LRG, and ELG populations.

Original authors: Hafiz Inam Ullah, Muhammad Awais, Tonatiuh Matosb, John F. Suárez-Pérez

Published 2026-04-06

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CC BY 4.0

Original authors: Hafiz Inam Ullah, Muhammad Awais, Tonatiuh Matosb, John F. Suárez-Pérez

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 random scattering of stars, but as a giant, three-dimensional spiderweb. This is the Cosmic Web. It's made of invisible dark matter and gas, and it looks like a giant sponge: it has empty holes (voids), thin walls (sheets), long strings (filaments), and dense, clumpy knots where everything crashes together.

This paper is like a massive census of the universe's "residents" (galaxies), using data from a powerful new telescope survey called DESI. The researchers wanted to answer a simple question: Does where a galaxy lives affect how "old" or "young" it is?

Here is the breakdown of their findings, using some everyday analogies:

1. The Setup: The Great Cosmic Neighborhoods

The researchers looked at over 2.5 million galaxies and sorted them into four types of neighborhoods based on the Cosmic Web:

Voids: The empty suburbs. Very few houses, lots of open space.
Sheets: The flat plains or open fields.
Filaments: The long highways or strings of pearls connecting the universe.
Knots: The bustling city centers. This is where the density is highest, like a crowded downtown.

They also sorted the galaxies into two "ages" or "moods":

Blue Galaxies: These are the "teenagers." They are young, energetic, and still forming new stars (like a lively party).
Red Galaxies: These are the "retirees." They have stopped making new stars and are just coasting along (like a quiet retirement home).

2. The Big Discovery: It's About Your Wallet, Not Your Zip Code

The most important finding is a bit of a twist. You might think that living in a crowded city (a Knot) forces you to stop partying (quench star formation) faster than living in a quiet suburb (a Void).

The paper says: Mostly, no.

The main factor determining if a galaxy is "Blue" (young) or "Red" (old) is its mass (how heavy it is).

The Analogy: Think of a galaxy's mass like its bank account.
- If a galaxy has a huge bank account (it's very massive), it eventually runs out of gas to make new stars and turns Red, no matter where it lives. It's like a rich person who decides to retire regardless of whether they live in a city or a farm.
- Mass is the boss. Heavy galaxies turn red first and everywhere.

3. The Secondary Effect: The Neighborhood Still Matters

While mass is the boss, the neighborhood (the Cosmic Web) does have a subtle influence, like a gentle breeze.

The Analogy: If two galaxies have the exact same bank account (mass), the one living in the Knot (City) is slightly more likely to be Red (retired) than the one in the Void (Suburb).
Living in a crowded city puts more pressure on you (through collisions, gas stripping, etc.), which can speed up the retirement process. But this effect is much weaker than the effect of your bank account.

4. Where Do They Live? (The Population Map)

The researchers also asked: "Where do the Blue and Red galaxies actually hang out?"

Surprise: Most galaxies, whether Blue or Red, don't live in the "Knots" (cities). They live in the Filaments (highways) and Sheets (plains).
Why? Because the Knots are tiny and rare. The Filaments and Sheets make up most of the volume of the universe.
The Metaphor: Even though the "City" (Knot) is where the most intense activity happens, most people in the universe actually live in the suburbs and along the highways. So, even though the City is efficient at turning galaxies "Red," there simply aren't enough galaxies there to dominate the total count.

5. The Time Travel Aspect

The team looked at galaxies at different distances, which means looking back in time.

In the past (High Redshift): The universe was younger, and galaxies were mostly Blue (forming stars).
In the present (Low Redshift): More galaxies have turned Red.
The "retirement" process happens fastest in the densest places (Knots) and slowest in the emptiest places (Voids). It's like a city where everyone retires by age 50, while in a quiet village, people might keep working until 70.

Summary in a Nutshell

The Main Driver: A galaxy's weight (mass) decides if it stops making stars. Heavy galaxies retire early.
The Side Effect: Living in a crowded neighborhood (Knot) makes retirement happen a little bit faster, but it's not the main reason.
The Location: Most galaxies live in the highways and plains of the Cosmic Web, not the dense city centers, even though the city centers are the most efficient places for galaxies to "age."

The paper confirms that our computer simulations (like the "IllustrisTNG" project) are doing a pretty good job of predicting how the universe works, giving us confidence that our understanding of galaxy evolution is on the right track.

1. Problem Statement

The formation and evolution of galaxies are governed by a complex interplay between internal processes (primarily stellar mass) and external environmental factors (the large-scale cosmic web). While it is established that stellar mass is the primary driver of galaxy quenching (the cessation of star formation), the specific role of the cosmic web environment (voids, sheets, filaments, and knots) remains a subject of active investigation.

Previous studies, particularly those using hydrodynamical simulations like IllustrisTNG, have predicted specific trends regarding how environmental quenching efficiency varies with redshift and stellar mass. However, these predictions require rigorous validation against large-scale observational data. The challenge lies in mapping the 3D cosmic web from observational data and applying a robust, mass-dependent classification to separate red (quenched) and blue (star-forming) galaxies to test these simulation-driven hypotheses.

2. Methodology

The authors utilize the Dark Energy Spectroscopic Instrument (DESI) Data Release 1 (DR1), which provides over 13 million extragalactic redshifts. The study employs a multi-tracer approach to cover different stellar mass regimes and redshift intervals:

Bright Galaxy Survey (BGS): $0.15 \le z < 0.55$ (Lower mass, local universe).
Luminous Red Galaxies (LRG): $0.6 \le z < 0.9$ (High mass, intermediate redshift).
Emission-Line Galaxies (ELG): $0.6 \le z \le 1.6$ (Star-forming, high redshift).

Key Technical Steps:

Cosmic Web Reconstruction (T-Web):
- The authors reconstruct the 3D density field on an $800 \text{ Mpc}^3$ grid ( $256^3$ cells) using the Cloud-In-Cell (CIC) mass assignment scheme.
- They solve the Poisson equation in Fourier space to obtain the gravitational potential and compute the tidal tensor (Hessian of the potential).
- The environment is classified based on the eigenvalues of the tidal tensor ( $\lambda_1 > \lambda_2 > \lambda_3$ $λ_{1} > λ_{2} > λ_{3}$ ) relative to a threshold ( $\lambda_{th}=0$ $λ_{t h} = 0$ ):
  - Voids: All $\lambda < 0$ .
  - Sheets: One $\lambda > 0$ .
  - Filaments: Two $\lambda > 0$ .
  - Knots (Clusters): All $\lambda > 0$ .
- Smoothing scales ( $R_s$ ) are tracer-dependent (e.g., $10 \text{ Mpc}$ for BGS/ELG, $7 \text{ Mpc}$ for LRG) to optimize the detection of web structures.
Red/Blue Classification (Mass-Dependent Otsu):
- Instead of a global color cut, the authors implement a mass-dependent Otsu thresholding method.
- In each redshift bin, galaxies are binned by stellar mass ( $\log M_*$ ). An optimal color threshold ( $g-r$ ) is calculated for each mass bin to maximize the separation between the blue and red populations.
- These discrete thresholds are interpolated to create a smooth divider in the $(g-r)$ vs. $\log M_*$ plane.
Metrics:
- Red/Blue Fractions (RF/BF): The fraction of red/blue galaxies within a specific environment.
- Relative Red/Blue Fractions (RRF/RBF): The fraction of the total red/blue population that resides in a specific environment (partitioning the global population).

3. Key Contributions

First Large-Scale Observational Test: This is one of the first studies to apply the T-Web formalism and mass-dependent Otsu classification to the massive DESI DR1 dataset, providing a direct observational counterpart to IllustrisTNG predictions.
Multi-Tracer Analysis: By combining BGS, LRG, and ELG samples, the study covers a broad range of stellar masses ( $\sim 10^{10} - 10^{12} M_\odot$ ) and redshifts ( $z \sim 0.15 - 1.6$ ), allowing for a comprehensive view of quenching evolution.
Refined Classification: The use of a mass-dependent Otsu threshold addresses the systematic shift of the red sequence and blue cloud with stellar mass, providing a more accurate separation of populations than fixed color cuts.

4. Key Results

A. Stellar Mass as the Primary Driver

Universal Trend: Across all tracers and environments, the red fraction increases monotonically with stellar mass.
Transition Mass: For BGS, the red fraction rises steeply above $\log(M_*/M_\odot) \approx 11.2$ , reaching $\sim 80\%$ . This confirms that internal processes (mass quenching) dominate at high masses, regardless of environment.

B. Environmental Modulation (Secondary Effect)

Hierarchy: At fixed mass and redshift, a consistent environmental hierarchy is observed: Knots > Filaments > Sheets > Voids in terms of red fraction.
LRG Specifics: LRGs show strong environment-dependent quenching. At $z \approx 0.66$ , knots have a red fraction of $\sim 66\%$ , while sheets are $\sim 58\%$ . By $z \approx 0.88$ , these converge to $\sim 68-70\%$ .
ELG Evolution: ELGs show strong redshift evolution. The red fraction drops significantly from $z \approx 0.65$ to $z \approx 0.95$ , indicating a rapid transition in the star-forming population at these epochs.

C. Partitioning of Populations (RRF/RBF)

Volume vs. Population: While knots are the most efficient quenching sites, they occupy only $\sim 4-6\%$ of the volume. Consequently, they host a small fraction of the total galaxy population.
Dominance of Sheets/Filaments: Sheets and Filaments host the vast majority ( $\sim 70-90\%$ $\sim 70 - 90%$ ) of both red and blue galaxies across all tracers.
- Contrast with Simulations: This finding partially contradicts some IllustrisTNG predictions which suggested high-mass blue galaxies might be concentrated in knots. In DESI data, high-mass blue galaxies (BGS/ELG) are predominantly found in sheets and filaments.
- LRG Exception: The LRG sample (highly biased) does show a higher concentration of both red and blue galaxies in knots and filaments, consistent with their intrinsic bias toward dense regions.

D. Color Distributions

Bimodality: Clear bimodality in $(g-r)$ color distributions is observed at low redshifts, with knots exhibiting the strongest red peak.
Evolution: As redshift increases, the distributions become broader and less bimodal, particularly for ELGs at $z > 1.0$ .

5. Significance and Comparison with IllustrisTNG

Qualitative Agreement: The study strongly supports the IllustrisTNG framework that stellar mass is the primary quenching driver, with environment acting as a secondary modulator. The observed hierarchy (Knots > Filaments > Sheets > Voids) matches simulation predictions.
Quantitative Differences:
- Amplitude: The dynamic range of environmental effects in DESI appears "compressed" compared to TNG. For example, the difference in red fraction between knots and voids is smaller in DESI LRGs than predicted. This is likely due to observational selection effects (DESI LRGs are pre-selected to be massive and red) and redshift-space distortions smoothing the density field.
- High-Mass Blue Galaxies: Unlike TNG, which predicts high-mass blue galaxies concentrate in knots at low redshift, DESI observations show they remain primarily in sheets and filaments. This suggests that the simulation may overestimate the efficiency of environmental quenching for massive star-forming galaxies or that selection functions differ significantly.
Conclusion: The paper validates the physical mechanisms (AGN feedback, ram-pressure stripping) implemented in modern simulations while highlighting the need to account for observational selection effects when comparing simulations to real data. It establishes a robust, reproducible methodology for future DESI data releases to refine our understanding of galaxy evolution in the cosmic web.