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Delayed and Displaced: The Impact of Binary Interactions on Core-collapse SN Feedback

T0 review · 4 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read Most massive stars have binary partners, which this paper shows delays about 25% of core-collapse supernovae and moves 13% more than 100 parsecs from their birth clusters.

desk verdict A thorough, well-released simulation study showing binaries delay and displace core-collapse SNe, but the headline displacement fraction rests on an unconstrained cluster velocity dispersion; the qualitative result is robust, the specific 13% is not. read the letter →

arxiv 2504.17903 v1 pith:ISSY3KUP submitted 2025-04-24 astro-ph.SR

Tom Wagg , Julianne J. Dalcanton , Mathieu Renzo , Katelyn Breivik , Matthew E. Orr , Adrian M. Price-Whelan , Akaxia Cruz , Alyson Brooks
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Ulrich P. Steinwandel Eric C. Bellm
This is my paper · ORCID
classification astro-ph.SR
keywords core-collapsesupernovaebinarystellarevolutionsupernovafeedbackpopulationsynthesisrunawaystarsgalacticdynamicsdwarfgalaxiesmergers
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Most massive stars form in binaries or higher-order multiples, yet galaxy-formation simulations routinely assume every star evolves alone when deciding where and when supernova feedback strikes the gas. This paper argues that assumption materially mispredicts the feedback: in its fiducial model of a dwarf galaxy, binary interactions delay about 25% of core-collapse supernovae past the 44-million-year cutoff used in current feedback prescriptions, and displace about 13% of them more than 100 parsecs from their birth clusters, against essentially zero and 1% for single stars. The delayed explosions are almost entirely products of stellar mergers; the displaced ones are mostly companion stars ejected as runaways when their partner exploded. Because the timing and location of each explosion control how efficiently supernovae regulate star formation and drive outflows, getting the distribution wrong changes what simulations predict about galaxies, most strongly at low metallicity and high redshift. The paper's practical deliverable is a metallicity-dependent analytic model that reproduces the simulated joint distribution and can be substituted for single-star subgrid feedback prescriptions.

What carries the argument

The central machine is cogsworth, the paper's population-synthesis-plus-galactic-dynamics framework, which replaces young star particles in a hydrodynamical dwarf-galaxy simulation with clusters of binary stars, evolves each binary with the COSMIC rapid population-synthesis code, and simultaneously integrates every star's galactic orbit through a potential fitted to the simulated galaxy, so that the time and position of each core-collapse supernova are recorded self-consistently. The argument runs on three binary mechanisms: mass transfer, which lengthens the donor's nuclear timescale and delays its explosion; stellar mergers, which combine two stars below the single-star core-collapse threshold into one star that explodes later, producing the long tail; and binary disruption at the first supernova, which launches the surviving secondary as a runaway at roughly its orbital velocity. The analytic deliverable is a metallicity-dependent piecewise power-law supernova rate with an exponential tail, paired with a four-component mixture model for progenitor ejection velocities that distinguishes unejected stars and ejections after no mass transfer, case A, case B/C, or common-envelope evolution.

What would settle it

Measure the internal velocity dispersions of young embedded clusters near $10^4\,M_\odot$: if reliable measurements cluster near 0.5 km/s rather than 1.7 km/s, the displacement fraction drops toward 5%, while values near 5 km/s would push it to 45%, settling whether the 13% headline holds. Independently, count core-collapse supernovae in regions of galaxies that lack young massive stars: the model predicts roughly a quarter of all core-collapse explosions occur more than 44 Myr after the birth burst, so an observed late fraction well below a quarter would rule out the timing tail's size.

Watch

Extended reading notes

Core claim

On its own terms, the central discovery is that binary interactions reshape the joint time-distance distribution of core-collapse supernovae in a way that single-star prescriptions cannot capture. In the fiducial simulation, the median supernova occurs 22 Myr after a star-formation event and 35 pc from its parent cluster, compared with 17 Myr and 23 pc for an equivalent single-star population, and binaries produce about 11% more supernovae overall because mergers and accretion let stars below the single-star core-collapse threshold still explode. The two headline features are a long late tail, with 25% of supernovae exploding after the 44 Myr at which the last single star explodes, almost all of them merger products, and a long-distance tail, with 13% of supernovae more than 100 pc from their cluster, dominated by secondary stars ejected at their orbital velocity when the primary exploded. The paper further claims these distributions are surprisingly stable across wide variations in binary physics, initial conditions, and host galaxy, with medians typically moving by less than 15%, while both tails strengthen at low metallicity, reaching about 34% late and 21% beyond 100 pc at one-tenth solar metallicity. It concludes that this stability justifies an analytic fit, and presents one along with a sampling routine for use in hydrodynamical simulations.

Load-bearing premise

The load-bearing premise is the assumed initial velocity dispersion of young stellar clusters, set to 1.7 km/s in the fiducial model with no strong observational constraint; the fraction of supernovae beyond 100 pc swings from about 5% at 0.5 km/s to about 45% at 5 km/s, so the paper's 13% displacement headline rides on this one unconstrained input.

Editorial extensions

If this is right

  • Simulations that keep single-star supernova prescriptions omit roughly a quarter of core-collapse explosions and place about 13% of the feedback energy more than 100 pc away from where the simple model puts it, so adopting binary-aware feedback should change the predicted efficiency of star formation regulation and outflow driving.
  • The paper's analytic fits reproduce the simulated timing distribution to within 0.5% and the ejection-velocity distribution to within a few percent, so they can be installed into existing hydrodynamical codes at negligible computational cost.
  • Because both the late and the distant tails grow at low metallicity (roughly 34% late and 21% beyond 100 pc at $Z = 0.1\,Z_\odot$), the error in single-star prescriptions is largest in exactly the regime occupied by high-redshift galaxies.
  • A longer, smoother energy-release history turns supernova feedback from an impulsive burst into a gradual push, which the paper argues could reduce the burstiness of star formation and change how the interstellar medium responds to successive explosions.
  • In dwarf galaxies with effective radii below about a kiloparsec, the displaced supernovae traverse a substantial fraction of the galaxy, so binary-driven feedback automatically becomes a galaxy-wide process and a plausible contributor to dwarf outflows.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A testable observational corollary: surveys of nearby core-collapse supernovae should find a population of 'orphan' explosions with no young massive stars nearby, and those orphans should be systematically old; the paper's joint time-distance distribution predicts exactly this correlation and could be read off existing supernova remnant catalogs.
  • Because the displacement numbers follow from the cluster velocity dispersion, the portability of the analytic model hinges on matching its velocity-dispersion input to each simulation's own cluster dissolution treatment, and the paper's choice of fitting velocities rather than distances is what makes such matching possible.
  • The same population-synthesis physics that generates the delayed merger-product tail also sets the merger rates of compact-object binaries, so the predicted roughly 25% late-supernova fraction is a consistency check for gravitational-wave progenitor models built on the same binary physics.
  • The paper implies a redshift-dependent feedback geometry: in compact, low-metallicity high-redshift galaxies the energy is deposited later and farther from dense gas, so simulations adopting the low-metallicity fits should see systematically different gas retention and star-formation histories than those using solar-metallicity single-star prescriptions.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. The paper uses the cogsworth population-synthesis and orbital-integration framework to replace the star particles formed in the past 150 Myr of the FIRE-2 m11h dwarf galaxy simulation with clusters of binary stars, evolving them with COSMIC and integrating their orbits in a gala potential fitted to the hydrodynamical simulation. It records the time and position of every core-collapse supernova relative to the parent cluster, for a fiducial binary model and for a broad set of variations of initial conditions, binary physics, metallicity, cluster dissolution assumptions, and galaxy potential. The central results are that binary interactions produce a long tail of delayed SNe (about 25% after the 44 Myr single-star cutoff, predominantly merger products) and displaced SNe (about 13% beyond 100 pc, predominantly ejected secondaries), and that these distributions are robust in their qualitative form across most variations. The paper also presents a metallicity-dependent analytic model for the SN rate and progenitor velocity distribution, intended as a subgrid replacement for single-star feedback prescriptions.

Significance. If the results hold, the paper provides the most complete joint time-distance distribution of core-collapse SN feedback from binaries in a realistic galactic potential to date, and a practical analytic prescription that can be incorporated into hydrodynamical simulations. The forward-modeling pipeline is clearly specified, and the code and simulation data are released on GitHub and Zenodo, which makes the experiment reproducible. The extensive parameter study, including extreme variations in common-envelope efficiency, mass-transfer stability, kicks, IMF, orbital period, mass ratio, metallicity, velocity dispersion, and galaxy code, is a genuine strength, as are the quantitative comparisons to De Donder & Vanbeveren (2003), Zapartas et al. (2017), Eldridge et al. (2011), and Renzo et al. (2019). The qualitative existence of delayed and displaced SNe is robust; less robust is the specific 13% displacement fraction, as discussed in the major comments.

major comments (4)
  1. [Section 4.6.1 / Table C1] The abstract and Section 3.2 quote about 13% of SNe beyond 100 pc as a headline result. Section 4.6.1 states that there are currently no strong observational constraints on the initial cluster velocity dispersion, and Table C1 shows that fD>100pc changes from 5.1% (vdisp = 0.5 km/s) to 45.3% (vdisp = 5 km/s). Since the unejected-component velocity distribution in Eq. (6) is set by vdisp, the quantitative displacement claim is a one-point draw from a largely unconstrained parameter rather than a robust prediction. I recommend presenting the displacement result as a conditional range and explicitly labeling the fiducial 13% as such throughout the abstract, Section 6, and the conclusions.
  2. [Section 6.2.2] The statement that "in all of our models, at least 12-15% of all SNe occur more than 0.1 kpc from the centre of the clustered star formation" is contradicted by the vdisp = 0.5 km/s variation in Table C1, for which fD>100pc = 5.1%. This overstatement appears in the discussion that motivates galaxy-evolution implications and should be corrected, along with the related framing in Section 6.1.2 that the low velocity dispersion is the only case below 10%.
  3. [Section 5.1 / Figures 9-10] The analytic model in Section 5 is calibrated with the fiducial simulation and the same metallicity variations used for the comparisons; the reported 0.5% and 1% agreements are therefore in-sample fit qualities, not validation against independent data. The adequacy of the model as a subgrid replacement for hydrodynamical simulations would be much better supported by a holdout test, for example fitting on m11h and predicting the ChaNGa r442 run, or leaving out one metallicity variation and predicting it. If the authors instead intend the numbers as fit residuals, that should be stated explicitly.
  4. [Section 6.1.2 / Figure 13] The claim that the distributions are "surprisingly insensitive" is based mainly on medians, but the tails that are most relevant for feedback vary substantially across Table C1: fD>100pc ranges from 5.1% to 45.3%, fD>500pc from 0.0% to 3.1%, and ft>44Myr from 12.3% to 34.6%. The robustness summary should explicitly separate median stability from tail sensitivity, and the abstract's phrase "surprisingly insensitive to most of these variations" should be qualified accordingly.
minor comments (5)
  1. [Abstract / Section 8] The >100 pc fraction is given as about 13% in the abstract and about 14% in conclusion item 2, while Table C1 lists 13.2% for the fiducial model; please standardize the quoted value.
  2. [Equation (18)] The second parameter of the beta distribution is labelled beta_B/C, but in the preceding line it is defined as beta_CE; the label should be made consistent.
  3. [Section 6.1.1 / Table C1] Section 6.1.1 states ft>44Myr = 0% for the single-star model, whereas the Singles row of Table C1 gives 1.3%; either round explicitly or quote the tabulated value.
  4. [Section 5.2] The values of feject in Eq. (5) and the mixture fractions in Eqs. (8)-(11) are presented without uncertainties or sample sizes; reporting these would help users of the analytic model gauge its precision.
  5. [Figure 13] The markers for ft>44Myr, fD>100pc and fD>500pc may be hard to distinguish in grayscale print; consider different marker shapes or a table callout for the key values.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the headline timing and displacement percentages are forward simulation outputs, and the analytic model is explicitly fitted rather than disguised as an independent prediction.

full rationale

The paper's central claims—that binary interactions delay core-collapse SNe (ft>44 Myr ≈ 25%) and displace them (fD>100 pc ≈ 13%)—are produced by cogsworth simulations that evolve binary populations and integrate their orbits in FIRE galaxy potentials. These are forward outputs of population synthesis plus orbital dynamics; they are not defined in terms of the claimed results. The single-star comparison is constructed consistently by widening binary orbits to be non-interacting, and the robustness suite varies binary physics, initial conditions, metallicity, and galaxy settings independently. The analytic model in Section 5 is transparently introduced as a fit: the text says 'We fit both the rate of core-collapse SNe over time, and the velocities of SN progenitors' and 'we outline our model for each of these distributions and assess their goodness-of-fit to our simulations.' Its quoted 0.5% and 4% agreements are therefore in-sample goodness-of-fit statistics, not independent predictions, and the paper does not use this agreement to justify the headline simulation results. The acknowledged uncertainty in the initial cluster velocity dispersion (Section 4.6.1 states 'There are currently no strong observational constraints on the appropriate value of the initial cluster velocity dispersion') is a parameter-sensitivity limitation, with Table C1 bracketing fD>100 pc between 5.1% and 45.3%; this affects robustness but is not circular because the dispersion is an input, not an output of the derivation. Self-citations to cogsworth (Wagg et al. 2025a,b) and 'Wagg et al. in prep.' support software and secondary mechanism explanations, but the load-bearing robustness claims are demonstrated by the paper's own variation runs. Overall, the derivation chain is self-contained and no circular step is present.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The central results rest on standard population synthesis assumptions plus two hand-chosen cluster parameters (radius 3 pc, velocity dispersion 1.7 km/s) and on the analytic model coefficients, all of which are fitted to the simulations. No new physical entities are introduced.

free parameters (7)
  • Cluster velocity dispersion vdisp = 1.7 km/s (fiducial)
    Hand-chosen from Orion Nebula Cluster measurements; strongly controls the SN distance distribution, with fD>100pc varying from 5.1% to 45.3% across the explored range (Section 4.6.1, Table C1).
  • Cluster radius = 3 pc
    Assumed radius of the stellar cluster that replaces each star particle, used for the Gaussian position spread (Section 2.1).
  • SN rate fit coefficients a_i = [0.38+0.13[Fe/H], 0.47+0.05[Fe/H], 0.22+0.02[Fe/H], 0.13, 0.1, 0.175+0.05[Fe/H]]
    Fitted to the simulated CCSN rate distribution in Eq. 2-3 to reproduce the fiducial and metallicity-varied simulations.
  • SN rate transition times t_i = [3.5, 6, 23-6.5[Fe/H], 28-6.5[Fe/H], 45.5-16.5[Fe/H], 200] Myr
    Fitted transition points in the piecewise power-law model, chosen to match features in the simulated SN time distribution (Eq. 4).
  • Ejected progenitor fraction feject(t) = 0.24 for 5 <= tSN < t5; 0.1 for t5 <= tSN < 60; 0 otherwise
    Fitted to the simulated fraction of SNe from ejected progenitors as a function of SN time (Eq. 5).
  • Ejection velocity mixture fractions = fnoMT = 0.14 - 0.12[Fe/H], fMT,A = 0.12 + 0.035[Fe/H], fMT,B = 0.67 + 0.12[Fe/H]
    Fitted to the five metallicity variations to partition ejected progenitors by mass-transfer history (Eq. 8-11).
  • Ejection velocity distribution shape parameters = power-law slope -1.8+0.5*sqrt(|[Fe/H]|); Normal(22-8[Fe/H], 6-3[Fe/H]); beta shapes alpha=1.5-1.5[Fe/H]…
    Fitted parameters for the sub-population velocity distributions in Eq. 12-18, matched to simulated ejection velocity histograms.
assumptions (5)
  • domain assumption Kroupa IMF slope and Sana et al. period/eccentricity distributions for initial binaries
    Adopted from prior observational constraints (Section 2.2); varied across plausible ranges in Section 4.4.
  • domain assumption Binary fraction of 100% for massive stars
    Justified by observed high multiplicity of massive stars; effectively single stars are represented by very wide binaries (Section 2.2).
  • domain assumption COSMIC/BSE rapid population synthesis prescriptions (mass transfer, common envelope, kicks)
    Fiducial default settings of COSMIC v3.4.16; the paper varies several parameters but acknowledges that the underlying parametric prescriptions may not capture full physics (Section 6.3).
  • domain assumption All stars with non-zero ejecta mass produce a visible core-collapse SN
    Uses the Fryer et al. (2012) remnant prescription; the paper notes many of these stars may implode instead (Section 6.3, 'Explodability of massive stars').
  • ad hoc to paper Each star particle is treated as a 3 pc radius cluster with a Gaussian position spread and a velocity dispersion of 1.7 km/s
    This choice directly sets the cluster dissolution rate and strongly controls the SN distance distribution (Section 2.1, Section 4.6.1).

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Cite this review

Pith. "Pith review of Delayed and Displaced: The Impact of Binary Interactions on Core-collapse SN Feedback." pith.science (2026). https://pith.science/paper/ISSY3KUP

@misc{pith2026250417903,
  author       = {Pith},
  title        = {Pith review of: Delayed and Displaced: The Impact of Binary Interactions on Core-collapse SN Feedback},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/ISSY3KUP}},
  note         = {Machine review of arXiv:2504.17903}
}
read the original abstract

Core-collapse supernova feedback models in hydrodynamical simulations typically assume that all stars evolve as single stars. However, the majority of massive stars are formed in binaries and multiple systems, where interactions with a companion can affect stars' subsequent evolution and kinematics. We assess the impact of binary interactions on the timing and spatial distribution of core-collapse supernovae, using `cogsworth` simulations to evolve binary star populations, and their subsequent galactic orbits, within state-of-the-art hydrodynamical zoom-in galaxy simulations. We show that binary interactions: (a) displace supernovae, with ~13% of all supernovae occurring more than 0.1 kpc from their parent cluster; and (b) produce delayed supernovae, such that ~25% of all supernovae occur after the final supernova from a single star population. Delays are largest for low-mass merger products, which can explode more than 200 Myr after a star formation event. We characterize our results as a function of: (1) initial binary population distributions, (2) binary physics parameters and evolutionary pathways, (3) birth cluster dissolution assumptions, and (4) galaxy models (which vary metallicity, star formation history, gravitational potential and simulation codes), and show that the overall timing and spatial distributions of supernovae are surprisingly insensitive to most of these variations. We provide metallicity-dependent analytic fits that can be substituted for single-star subgrid feedback prescriptions in hydrodynamical simulations, and discuss some of the possible implications for binary-driven feedback in galaxies, which may become particularly important at high redshift.

Figures

Figures reproduced from arXiv: 2504.17903 by the authors.

Figure 1
Figure 1. Binary interactions can result in delayed SNe and displace SNe far from their parent clusters and molecular clouds. Top panels: Stacked histograms separated by progenitor type. Effectively single stars had no binary interaction prior to SN (no Roche Lobe overflow and less than 5% wind mass accretion). Primary (secondary) stars were the initially more (less) massive star in a binary that did not undergo a merger. Mer… view at source ↗
Figure 2
Figure 2. The type of mass transfer that a primary star first initiates prior to its core collapse is a function of its initial mass. The transition at ∼10 M⊙ leads to the knee in the dis￾tribution in the upper left panel of [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Top: Binary stars produce a significantly dif￾ferent distribution of SNe times and locations than single stars. Main panel shows a 2D kernel density estimation of the distribution of SNe for single star population in blue. The grey hatched contour shows the 98% region for a binary star population. Marginal histograms are shown in each side panel. Bottom: The timing and location of SNe relative to their parent cluste… view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Comparison of the impact of binary physics variations on the timing (upper panel) and location (lower panel) of SNe. Each group of bars in the main panels corresponds to a different choice of binary physics. Coloured bars show the interquartile range for each subpopula…
Figure 5
Figure 5. Figure 5: As [PITH_FULL_IMAGE:figures/full_fig_p014_5.png]
Figure 6
Figure 6. Figure 6: As [PITH_FULL_IMAGE:figures/full_fig_p016_6.png]
Figure 7
Figure 7. Figure 7: As [PITH_FULL_IMAGE:figures/full_fig_p017_7.png]
Figure 8
Figure 8. Figure 8: The distribution of metallicities of stars in our fiducial m11h simulation, compared to those in the ChaNGa r442 simulation with a factor 2 increased metallicity. The boxes at the top indicate the interquartile range, with the median shown with a line in the box. As no…
Figure 9
Figure 9. Figure 9: Top: A comparison of our analytic model (Eq. 2, shown in black) to the SNe delay time distribution to our fiducial simulation (shown in red). Transition points are an￾notated with the physical process driving them. The model reproduces the late SN rate and overall norm…
Figure 10
Figure 10. Figure 10: Top: A comparison of our analytic model for the distribution of ejection velocities (Eq. 7, shown in black) to our fiducial simulation for the subset of SN progenitors that are ejected with vej ≥ 5 km s−1 (shown in red, with the lower velocity region shaded in grey). …
Figure 11
Figure 11. Figure 11: A comparison of the joint distribution of our analytic model to the fiducial simulation, which shows good agreement. Each panel shows a 2D histogram with the super￾nova time on the x-axis and the maximum distance travelled (i.e. the product of the SN time and velocity…
Figure 12
Figure 12. Figure 12: The median time and distance at which SNe oc￾cur for each variation (variations are outlined in Section 4.1). Dotted lines and shaded regions show the fiducial value and a ±15% region around this value. The solid black lines show the median value of a population of si…
Figure 13
Figure 13. Figure 13: The fraction of the total population in the tails of the SNe timing and distance distributions for each variation (variations are outlined in Section 4.1). Scatter points indicate the ft>44 Myr (green), fD>100 pc (light blue), and fD>500 pc (slate blue) fractions. Not…

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