Publications

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Paper highlights

May 20, 2026

Massquerade: Impacts of Mass Ratio Reversals on Binary Black Hole Merger Rates and Mass Distributions

Tyler B. Smith, Floor Broekgaarden, Sasha Levina, Amedeo Romagnolo, Manasvini Komandur, Melanie Santiago, Kyle A. Rocha
arXiv:2605.21580

When two massive stars are born together in a binary system, the heavier star is expected to also produce the heavier black hole when it dies. But binary evolution can flip this expectation. Through a process called mass ratio reversal (MRR), the initially less massive star can end up forming the more massive black hole. This happens when mass is transferred between the stars during their lives — the lighter star receives material from its companion, grows a more massive core, and ultimately collapses into a heavier black hole than the one formed by the originally dominant star. The initially less massive star is then "massquerading" as the primary.

This paper investigates how common MRR is, and — crucially — how it shapes the black hole merger rate and mass distributions that gravitational-wave observatories like LIGO, Virgo, and KAGRA actually measure. The team uses two independent binary population synthesis codes, COMPAS and SEVN, to simulate large populations of binary stars from birth through to black hole merger, and compares the resulting distributions against current LVK observations.

A key finding is that the two codes make qualitatively different predictions. In COMPAS, MRR systems dominate the high-mass end of the distribution (primary masses above ~20 M☉, secondary masses above ~12 M☉), while in SEVN the MRR contribution is more diffuse and remains subdominant across the full mass range. Despite this difference, both codes agree that MRR systems preferentially populate the high mass-ratio regime (q ≳ 0.6, meaning the two black holes have similar masses). The upshot is that the observed mass distribution cannot simply be read as a direct map of the original stellar masses — MRR blurs that connection.

The paper also identifies three distinct evolutionary channels that produce MRR (Figure 6 below): (1) core growth, where stable mass transfer fattens the secondary's helium core until it collapses into the heavier black hole; (2) PPISN shrinkage, where the primary loses mass through violent pulsational pair-instability episodes and ends up lighter than the secondary; and (3) asymmetric core-collapse supernovae, where differential stripping leaves the secondary with a heavier remnant.

Figure 2: BBH merger rate density broken down by MRR and non-MRR contributions for COMPAS and SEVN — **Figure 2.** Intrinsic binary black hole merger rate density as a function of primary mass M₁ (top), secondary mass M₂ (middle), and mass ratio q (bottom) at redshift z ≈ 0.2, for COMPAS (left) and SEVN (right). The total population (black) is split into MRR (purple) and non-MRR (green) contributions and compared to LVK observational constraints (gray shaded regions). COMPAS predicts MRR systems dominate at high masses, while SEVN shows a more diffuse, subdominant contribution. Both models agree that MRR preferentially populates the high mass-ratio regime (q ≳ 0.6).

Figure 6: Representative evolutionary pathways leading to MRR — **Figure 6.** Representative evolutionary histories for each of the three MRR channels. Each panel shows the total stellar masses (black and red) and core masses (magenta and green) of both stars as a function of time, with vertical dotted lines marking the two supernova events. From left to right: the core-growth channel in COMPAS, the core-growth channel in SEVN (illustrating how the two codes treat this process differently), the PPISN-shrinkage channel, and the asymmetric core-collapse supernova channel.

January 28, 2026

From cosmological simulations to binary black hole mergers: The impact of using analytical star formation history models on gravitational-wave source populations

Sasha Levina, Floor Broekgaarden, Lieke van Son, Emanuele Berti, Amedeo Romagnolo, Ruediger Pakmor, Ana Lam
arXiv:2601.20202

To predict how many binary black hole mergers gravitational-wave detectors like LIGO and Virgo should see, theorists must combine a model of how stars form across cosmic time — the star formation history (SFH) — with a model of how binary stars evolve into merging black holes. In practice, most studies use simple analytical SFH models (e.g., power laws or fits to galaxy survey data), but these are approximations of a far more complex underlying reality. This paper asks: how much does the choice of analytical SFH model actually matter?

The team uses the IllustrisTNG cosmological simulation as a ground truth for the star formation history and metallicity evolution of the universe, and compares it against four widely-used analytical SFH prescriptions. By running binary population synthesis models with COMPAS on top of each SFH, they quantify how the choice of SFH propagates through to predictions for BBH merger rates and mass distributions detectable by current and future gravitational-wave observatories.

A key finding (Figure 4 below) is that the predicted merger rate can vary by up to a factor of ~2–3 depending on which analytical SFH model is used, with the differences being most pronounced at high redshifts. This has direct implications for next-generation detectors like the Einstein Telescope and Cosmic Explorer, which will be sensitive to mergers across cosmic history. The metallicity evolution built into each SFH model is the primary driver of the spread: models that assign lower metallicities to star-forming gas at high redshift predict more BBH mergers because low-metallicity stars retain more mass and more readily form heavy black holes.

Figure 6 shows how the mass distributions of detectable BBH mergers shift depending on the SFH choice, with the IllustrisTNG-based prediction bracketed by the analytical models. The results underscore that SFH uncertainty is a non-negligible systematic in population-level gravitational-wave analyses — and that using cosmological simulations as a calibration anchor can help identify and bound this uncertainty.

Figure 4: BBH merger rate as a function of redshift for different SFH models — **Figure 4.** Binary black hole merger rate density as a function of redshift for five star formation history models: IllustrisTNG (used as ground truth), and four analytical prescriptions. The choice of SFH leads to rate differences of up to a factor of ~2–3, with the spread growing at higher redshifts where metallicity evolution assumptions diverge most strongly.

Figure 6: Detectable BBH mass distributions for different SFH models — **Figure 6.** Distributions of primary mass M₁ for detectable binary black hole mergers under each SFH model, for current LIGO-Virgo sensitivity (left) and next-generation detector sensitivity (right). The IllustrisTNG prediction (black) is bracketed by the analytical models, illustrating how SFH choice shifts the relative contribution of high-mass mergers — an effect that will become increasingly important as detector sensitivity improves.