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arxiv: 2606.04740 · v1 · pith:U73YDRXBnew · submitted 2026-06-03 · 🌌 astro-ph.HE

TDEs on FIRE: Illuminating the Cosmic Evolution of Tidal Disruption Rates

Rudrani Kar Chowdhury , Lixin Dai , Janet N.Y. Chang , Tsang Keung Chan This is my paper

Pith reviewed 2026-06-28 05:25 UTC · model grok-4.3

classification 🌌 astro-ph.HE
keywords tidal disruption eventscosmological simulationsblack hole growthgalaxy evolutionstar formation ratecentral stellar densitysatellite galaxiesredshift evolution
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The pith

Simulations find the average tidal disruption rate per galaxy peaks near redshift 2.5 then falls sharply by redshift 1.

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

This paper applies cosmological zoom-in simulations to track how often stars are torn apart by black holes inside galaxies from redshift 10 down to redshift 1. The calculation shows the typical rate per galaxy climbs from early times, reaches roughly 4 times 10 to the minus 4 events per year near redshift 2.5, and drops to about 10 to the minus 5 by redshift 1. The rates track closely with each galaxy's star-formation activity and the density of stars near its center at every epoch examined. The same mass trends seen locally also appear at high redshift, while satellite galaxies supply a rising fraction of the events as redshift increases.

Core claim

Using the FIRE-2 cosmological zoom-in simulations, the per-galaxy tidal disruption rate is computed over redshifts 1 to 10 for black holes ranging from intermediate-mass to supermassive. The averaged rate rises from the early universe, peaks at approximately 4 times 10 to the minus 4 per year near redshift 2.5, and declines to about 10 to the minus 5 per year at redshift 1. This rate correlates strongly with host-galaxy star-formation rate and central stellar density at all redshifts. The dependence on black-hole mass and galaxy mass remains qualitatively similar from high redshift to the local universe, and satellite galaxies show comparably high rates whose fractional contribution grows at

What carries the argument

FIRE-2 cosmological zoom-in simulations that model central stellar densities, black-hole populations, and dynamical conditions to compute per-galaxy tidal disruption rates across redshift.

If this is right

  • Tidal disruption rates track star-formation rate and central density, so galaxies with elevated star formation should produce more events at any redshift.
  • Satellite galaxies maintain high rates whose share increases at high redshift, making them useful targets for finding intermediate-mass black holes.
  • The black-hole to galaxy mass trends stay consistent from high redshift to today, supporting similar scaling relations across cosmic time.
  • Cosmological simulations can now supply predictions for the cosmic evolution of tidal disruption rates that future surveys can test directly.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Confirmation of the redshift-2.5 peak would link the era of maximum star formation directly to the era of maximum central stellar densities that drive disruptions.
  • Higher rates at earlier times could alter estimates of how much black-hole growth occurs through stellar capture rather than gas accretion.
  • If satellite contributions are as large as modeled, wide-field high-redshift transient searches could preferentially detect events in merging or assembling galaxies.

Load-bearing premise

The FIRE-2 simulations accurately capture the central stellar densities, black-hole populations, and dynamical conditions needed to compute tidal disruption rates at redshifts above 1.

What would settle it

A direct measurement or statistical sample of tidal disruption events at redshift approximately 2.5 that yields an average per-galaxy rate far below or far above 4 times 10 to the minus 4 per year.

Figures

Figures reproduced from arXiv: 2606.04740 by Janet N.Y. Chang, Lixin Dai, Rudrani Kar Chowdhury, Tsang Keung Chan.

Figure 1
Figure 1. Figure 1: The radial profile of stellar density at the centres of the primary host galaxies at z = 1 in the A1, A2, A4, and A8 runs, respectively. Double power-law fits (Equation 11) are shown as solid black lines. The primary galaxies of the A2 and A8 do not contribute to the TDE rates as their BH masses are > 108M⊙. using the fitted parameters of the double power law pro￾file (ρ0, r0, α, δ and γ). phaseflow can al… view at source ↗
Figure 2
Figure 2. Figure 2: The correlation between the TDR and MBH at individual redshifts. Gray dots show the full galaxy sample across all redshifts, while colored points in each panel highlight galaxies at the specific redshift indicated [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Correlation between the TDR and Mgal at individual redshifts. Colour scheme is same as [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: The redshift evolution of the averaged TDR (black triangles) and the averaged SFR (red dots) at each redshift. Black solid lines show the best-fit relations at z > 2.5, while the thin dotted line marks the redshift be￾low which both rates moderately decline. within a central radius that contains 90% of the stellar mass of host galaxy. As noted from the [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: The TDR versus SFR for individual galaxies in our sample, with the colors indicating redshift from z = 1 to 10. The TDR generally traces the redshift evolution of SFR. The gray shaded regions show the averaged TDR in each SFR bin, fitted with a power-law function (Equation 13). on samples at z ≥ 1. Hence, differences in the scal￾ing relations are expected. However, qualitative agree￾ment on the overall tre… view at source ↗
Figure 6
Figure 6. Figure 6: The correlation of the TDR with the black hole mass (MBH) (left panel) and host galaxy mass (Mgal) (right panel) for individual galaxies in our sample. Colors indicate different redshifts, as shown in the legend. In both panels, star symbols denote primary host galaxies, while circles represent the remaining smaller galaxies. The grey shaded regions indicate the average TDR within individual mass bins, and… view at source ↗
Figure 7
Figure 7. Figure 7: The projected stellar densities around the BHs at the centers of two example galaxies, where the TDR is found to be highest in [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: The correlated properties of the TDR vs the fitted parameters of stellar profiles at different redshifts. Left panel: Correlation between TDR and stellar densities at the scale radius (ρ0). Right panel: Correlation of TDR with the inner slope of the stellar profiles (γ). The color scheme is the same as in [PITH_FULL_IMAGE:figures/full_fig_p011_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: The correlation of the radius of influence (rinf) with the BH mass (left panel) and the TDR (right panel) at z = 1−10. The symbols and the color schemes are the same as in [PITH_FULL_IMAGE:figures/full_fig_p011_9.png] view at source ↗
Figure 12
Figure 12. Figure 12: The TDR of satellite galaxies versus their off￾set distances from the primary hosts at each redshift. Black lines mark the angular resolution limits of Roman (dash– dotted), Rubin (dashed), and ULTRASAT (dotted), respec￾tively. While there is a spread in TDR among satellite galax￾ies, the majority of TDEs are located at angular separations well within the resolution limits of all three telescopes. Col￾ors… view at source ↗
Figure 11
Figure 11. Figure 11: The ratio of the total TDR from all satel￾lite galaxies to that from primary galaxies at each redshift. The TDE contribution from satellite galaxies is significantly higher in the early universe. 5. Finally, we examine the detectability of TDEs in satellite galaxies. The fraction of TDEs originat￾ing from satellite galaxies increases significantly at high redshifts, underscoring their potential as probes … view at source ↗
read the original abstract

Tidal disruption events have been extensively studied in the local universe, but their prevalence at high redshifts remains largely unexplored. Using the FIRE-2 cosmological zoom-in simulations, we compute the per-galaxy tidal disruption rate (TDR) over $z=1-10$, covering black holes from IMBHs to SMBHs. The averaged TDR rises from the early universe, peaks at $\sim 4 \times 10^{-4} \, \text{yr}^{-1}$ near $z \sim 2.5$, and declines to $\sim 10^{-5} \, \text{yr}^{-1}$ at $z=1$. The TDR correlates strongly with host galaxy star formation rate and central stellar density at all redshifts. Qualitatively, the TDR trends with the $M_{\rm BH}$ and $M_{\rm gal}$ persist from high redshift to the local universe, suggesting similar BH-galaxy scaling across cosmic time. Satellite galaxies exhibit comparably high TDRs, with their fractional contribution increasing significantly at high redshifts, highlighting their potential for probing IMBHs and early galaxy assembly. This work demonstrates that cosmological simulations offer a promising avenue for constraining the cosmic evolution of the TDR, paving the way for future comparisons with next-generation observations.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper uses FIRE-2 cosmological zoom-in simulations to compute per-galaxy tidal disruption rates (TDR) from z=1 to 10 across IMBH to SMBH masses. It reports that the averaged TDR rises from early times, peaks at ~4×10^{-4} yr^{-1} near z~2.5, and falls to ~10^{-5} yr^{-1} at z=1. The TDR is stated to correlate strongly with host SFR and central stellar density at all redshifts; trends with M_BH and M_gal are qualitatively similar to the local universe; satellite galaxies contribute an increasing fraction of the TDR at high z.

Significance. If robust, the work supplies the first simulation-derived TDR(z) evolution and links it to galaxy properties, offering testable predictions for high-redshift TDE searches. The approach of extracting rates directly from cosmological zoom-ins is novel for this problem. Credit is due for covering a wide redshift range and including satellites. However, the result's significance hinges on whether the unresolved central densities can be reliably mapped to TDR.

major comments (3)
  1. [Abstract / Methods] Abstract and methods (TDR extraction): the headline TDR(z) curve is obtained by applying an estimator to simulated M_BH, M_gal, SFR, and central stellar density. No derivation, functional form, or sub-grid extrapolation for the loss-cone density is provided; the exponential sensitivity of TDR to density within the BH influence radius means any inaccuracy in the unresolved nuclear density shifts both normalization and peak redshift by factors of several.
  2. [Abstract / Results] Abstract and results (validation): no comparison of the computed local (z~0) TDR to observed rates is shown, nor are error bars or resolution convergence tests reported. Without this anchor, the claimed evolution from z=10 to z=1 cannot be assessed for systematic bias.
  3. [Abstract] Abstract (correlations): the reported strong correlation between TDR and central stellar density is expected by construction once the estimator is applied to that density; it therefore does not constitute independent validation of the redshift trend or the underlying density field.
minor comments (2)
  1. [Abstract] Abstract: include a one-sentence description of the TDR estimator and the mass range of black holes considered.
  2. [Figures] Figure clarity: ensure any TDR(z) plots show individual galaxy tracks or scatter in addition to the average to allow assessment of sample variance.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for their detailed and constructive report. We address each major comment below and indicate where revisions will be made to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Abstract / Methods] Abstract and methods (TDR extraction): the headline TDR(z) curve is obtained by applying an estimator to simulated M_BH, M_gal, SFR, and central stellar density. No derivation, functional form, or sub-grid extrapolation for the loss-cone density is provided; the exponential sensitivity of TDR to density within the BH influence radius means any inaccuracy in the unresolved nuclear density shifts both normalization and peak redshift by factors of several.

    Authors: We agree that the methods section would benefit from an explicit derivation of the TDR estimator, including the functional form relating loss-cone refilling to central stellar density. The estimator follows the standard loss-cone formalism (e.g., as in Merritt & Wang 2005 and subsequent works), applied to the resolved central densities in FIRE-2. We will add this derivation, the precise functional form, and a brief discussion of sub-grid assumptions in a revised Methods section. This addresses the concern about transparency while noting that the simulations themselves provide the density evolution. revision: yes

  2. Referee: [Abstract / Results] Abstract and results (validation): no comparison of the computed local (z~0) TDR to observed rates is shown, nor are error bars or resolution convergence tests reported. Without this anchor, the claimed evolution from z=10 to z=1 cannot be assessed for systematic bias.

    Authors: We acknowledge the value of anchoring the results to local observations. In the revised manuscript we will include a direct comparison of our z≈0 TDR values to the observed local TDE rate range (∼10^{-5}–10^{-4} yr^{-1} per galaxy) from the literature, along with sample variance error bars derived from the zoom-in suite. Resolution convergence is limited by the fixed FIRE-2 resolution; we will add a brief discussion of this limitation and note that higher-resolution follow-up simulations would be needed for full convergence tests. revision: partial

  3. Referee: [Abstract] Abstract (correlations): the reported strong correlation between TDR and central stellar density is expected by construction once the estimator is applied to that density; it therefore does not constitute independent validation of the redshift trend or the underlying density field.

    Authors: We agree that the TDR–central-density correlation is expected by construction of the estimator. However, the independent correlation with SFR (which is not an input to the estimator) and the persistence of M_BH and M_gal trends across redshift provide additional support for the physical trends. We will revise the abstract and results text to clarify this distinction and to emphasize that the redshift evolution arises from the simulated evolution of galaxy properties rather than from the estimator alone. revision: yes

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper computes per-galaxy TDR directly from FIRE-2 simulation snapshots of M_BH, M_gal, SFR and central stellar density over z=1-10, then reports the resulting averaged TDR(z) evolution and correlations. No equations, fitted parameters, or self-citations are shown that define the output TDR in terms of itself or rename a fitted input as a prediction. The stated correlations with SFR and central density are expected consequences of applying any standard TDR estimator but do not reduce the headline TDR(z) curve to a tautology by construction; the main result remains an independent computation from the simulation data. The derivation is therefore self-contained against external simulation inputs with no load-bearing circular steps.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No free parameters, axioms, or invented entities are identifiable from the abstract alone.

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discussion (0)

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