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Contaminating Electromagnetic Transients in LISA Gravitational Wave Localization Volumes. I: The Intrinsic Rates

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Pith's one-line read This paper quantifies how many unrelated electromagnetic transients will contaminate LISA localization volumes and shows that redshift information is the decisive requirement for identifying true counterparts.

desk verdict Useful first estimate of LISA follow-up contamination; the factor-of-100 redshift penalty is robust, but the quoted z thresholds depend on Fisher-matrix volumes that the paper never stress-tests. read the letter →

arxiv 2502.02839 v1 pith:F3LFZT2P submitted 2025-02-05 astro-ph.GA astro-ph.HE

Weixiang Yu , John J. Ruan , Michael Eracleous , Jessie Runnoe , Daryl Haggard , Tamara Bogdanovic , Aaron Stemo , Kaitlyn Szekerczes
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Carolyn L. Drake Kate E. Futrowsky Steinn Sigurdsson
This is my paper · ORCID
classification astro-ph.GAastro-ph.HE
keywords gravitationalwavesLISAmassiveblackholemergerselectromagneticcounterpartstransientastronomysupernovaetidaldisruptioneventslocalizationvolumes
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

LISA will detect massive black hole mergers in advance, and telescopes will search those sky regions for an electromagnetic flash from the merger itself. This paper asks a prior question: how many unrelated transients—supernovae, tidal disruption events, gamma-ray bursts, fast radio bursts—will happen to lie in the same localization volume and masquerade as the counterpart. Using simulated merger populations and localization volumes, it finds that when every detected transient can be assigned a redshift, the expected contaminant count falls to about one or fewer for mergers at $z \lesssim 0.8$ in the final hour before coalescence, and after coalescence the one-contaminant region extends to $z \lesssim 1.5$. Without redshifts, the count jumps by an average factor of roughly 100 and never drops below unity, so the paper's practical conclusion is that follow-up strategy should treat redshift acquisition for candidate transients as a primary requirement.

What carries the argument

The counting machinery is a volume integral over the LISA localization volume: $N_{\rm trans} = (\Delta\Omega/4\pi) \int_{z_{\min}}^{z_{\max}} R(z)\, (dV_c/dz)\,\Delta t\, dz$, where $\Delta\Omega$ and $\Delta t$ are the sky localization region and transient lifetime, and $R(z)$ is the volumetric rate of each transient class. The localization volumes themselves come from parametric Fisher-matrix fits that give the fractional distance error and sky-area error as functions of merger total mass, redshift, and time to coalescence; these volumes shrink as coalescence approaches and grow with mass and redshift. Available redshift information enters by restricting the integration to the redshift shell spanned by the localization volume, whereas unavailable redshifts force integration over $z\in[0,3]$, which is what produces the factor-of-100 increase.

What would settle it

Run full Bayesian LISA parameter estimation on a grid of massive-black-hole merger waveforms spanning $10^5$–$10^7\,M_\odot$ and $z=0.2$–$3.2$, and compare the resulting localization volumes with the parametric fits; if the volumes differ by more than a factor of two, the reported unity-contaminant thresholds at $z\simeq0.8$ pre-merger and $z\simeq1.5$ post-merger are not reliable.

Watch

Extended reading notes

Core claim

The central discovery is that the expected number of unrelated electromagnetic transients inside a LISA localization volume is controlled mainly by the size of that volume, not by the redshift dependence of transient rates, and that redshift information is what separates a tractable search from an intractable one. In the idealized scenario where a redshift is available for every transient detected in the LISA sky region, the expected contaminant count drops to unity at $z \lesssim 0.8$ for mergers one hour before coalescence, and after coalescence it stays below unity for total masses $10^{5.5}\,M_\odot \lesssim M_{\rm tot} \lesssim 10^{6.5}\,M_\odot$ out to $z \lesssim 1.5$. In the opposite scenario, counting everything from $z=0$ to $z=3$ in the same sky region, the expected count rises by an average factor of about 100 and never falls below one, even for the closest mergers. Core-collapse supernovae make up roughly 80 percent of the contaminants at all redshifts considered.

Load-bearing premise

The calculation inherits the size of every LISA localization volume from approximate fitting formulas for LISA's distance and sky-position measurement errors, averaged over parameters like mass ratio and spin; if those volumes are off by even a factor of a few, all contaminant counts and redshift thresholds shift by a comparable factor.

Editorial extensions

If this is right

  • Before coalescence, the contaminant count for low-redshift mergers ($z \lesssim 0.2$) reaches unity as early as six hours before merger when redshifts are known, so pre-merger electromagnetic monitoring with redshift filtering is feasible only for nearby events.
  • After coalescence, mergers with total mass near $10^6\,M_\odot$ at $z \lesssim 1.5$ have at most one contaminant, making them the cleanest targets for counterpart identification.
  • Without redshifts, every LISA follow-up field will contain roughly 100 unrelated transients, so 'find the new source in the error box' strategies cannot work by themselves.
  • Because roughly 80% of contaminants are core-collapse supernovae, real-time transient classification can remove most false candidates, but classification alone does not remove the need for redshifts.
  • The paper's three recommendations—host-galaxy redshift catalogs, real-time classification, and coordinated follow-up spectroscopy—follow directly from the factor-of-100 gap between the two scenarios.

Reading between the lines

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

  • Beyond the paper, the same counting integral can be applied to other future gravitational-wave detectors and to stellar-mass sources; the qualitative conclusion that redshift filtering dominates over localization-volume size should transfer, though the specific thresholds will change.
  • A partial-redshift scenario, not treated in detail here, would place the true contaminant count between the two extremes; even coarse photometric redshifts might remove most of the factor of 100, relaxing the need for spectroscopy of every candidate.
  • The paper's assumption that transient rates are known from $z=0$ to $3$ could be tested with the first years of wide-field time-domain surveys, which will measure the supernova rate at the redshifts LISA will probe.
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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

3 major / 4 minor

Summary. The paper estimates the number of unrelated electromagnetic transients expected to fall inside LISA gravitational-wave localization volumes for massive black hole mergers. The calculation combines localization volumes from the parametric Fisher-matrix fits of Mangiagli et al. (2020) with literature volumetric rates for several transient classes (SN Ia, SN Iax, CCSN, short and long GRBs, TDEs, FRBs) via the steady-state integral in Eq. (8). Two scenarios are compared: one in which redshifts are available for all detected transients, so only transients within the localization volume contribute, and one in which redshifts are unavailable, so all transients in the sky patch out to z=3 are counted. Results are presented as average contamination numbers for a merger catalog following the NewHorizon redshift distribution and as a grid in total mass and redshift at several times before and after coalescence. The headline findings are that with redshift information the expected number of contaminants drops to unity around z~0.8 one hour before coalescence and at z~1.5 after coalescence, while without redshifts the number increases by an average factor of ~100 and never drops below unity.

Significance. If the quantitative thresholds hold, the paper provides useful guidance for LISA follow-up strategy, particularly the need for transient redshifts. The calculation is transparent and internally consistent: Eq. (8) is a standard steady-state estimate, all inputs come from external catalogues and literature rate measurements, and no parameter is fitted to the target contamination counts. The qualitative conclusion that redshift information is critical is likely robust to moderate variations in the inputs. However, the exact z values at which the contamination count crosses unity rest on the adopted localization volumes and on point-value rate estimates, and the current manuscript does not quantify how those uncertainties propagate into the headline numbers.

major comments (3)
  1. [Section 2.2, Eq. (1); Figures 4-5] The localization volumes that control Ntrans through Eq. (8) are taken exclusively from the parametric Fisher-matrix fits of Mangiagli et al. (2020), with no validation for the parameter space or time ranges used in this paper. Because Ntrans is approximately proportional to Vloc, a factor-of-few bias in Vloc translates directly into a shift of the headline thresholds, e.g., the z~0.8 pre-merger and z~1.5 post-merger unity crossing quoted in the abstract and Section 3.2. The 'After Merger' panels require evaluating the fits at or beyond the calibration regime, and the separable form of Eq. (1) discards correlations among luminosity distance, sky position, and other waveform parameters that are typically strong for LISA massive-black-hole signals. Section 4.2 acknowledges that the adopted method ignores mass ratio and that rerunning the Mangiagli et al. experiment would be worthwhile, but no quantitative test is supplied. I request a validation against full parameter estimation for a few representative (Mtot, z, tc) points, or at minimum a sensitivity study showing how the Ntrans=1 contours shift when Vloc is rescaled by a factor of 2-3.
  2. [Section 2.3-2.4, Eq. (8); Figure 3] The volumetric rates R(z) are used as point values without propagating their uncertainties. The shaded regions in Figure 3 show the 1-sigma scatter across simulated mergers, not the uncertainty in the adopted rates, so they understate the uncertainty in Ntrans. Since Ntrans is linear in each R(z), published rate uncertainties (typically factors of ~1.5-3 for supernovae, and larger for TDEs and FRBs, including the FRB beaming-fraction assumption) can move the z thresholds at which Ntrans crosses unity by a comparable amount. Please propagate rate uncertainties, for example by Monte Carlo sampling the rate normalizations and redshift-evolution parameters, and display the resulting uncertainty bands on the Ntrans=1 contours in Figures 4 and 5.
  3. [Section 2.4 and Section 3.2 (no-redshift scenario)] The 'without redshift' scenario integrates Eq. (8) from z=0 to zmax=3, and the abstract's factor-of-~100 increase and the statement that the count 'never drops below unity' rest on this choice. The cutoff is justified only by the qualitative statement that beyond z=3 transients are too faint for the largest telescopes; no detection or limiting-magnitude model is applied, so the integrated count is an upper limit whose numerical value depends on zmax. Because the factor ~100 is a headline result, please show the sensitivity of the factor and of the unity crossing to zmax (e.g., zmax=2 and zmax=4) and to a simple flux-limited detectability model.
minor comments (4)
  1. [Section 4.2] There is a typo in 'Howeber' in the first sentence of the third paragraph.
  2. [References] In the Harris et al. reference, the journal name is written as 'Natur'; it should be 'Nature'.
  3. [Figures 4-5] The color bar labels and contour annotations in Figures 4 and 5 are dense and difficult to read at the current size; consider enlarging the panels or adding explicit contour values.
  4. [Section 2.1] The first investigation uses 141,834 simulated mergers while the NewHorizon simulation predicts roughly five LISA-observable mergers over the mission; please clarify how the catalog is sampled from the rate distribution and why the large sample is appropriate for computing the average.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: every input to the contamination-rate calculation is external and no equation reduces to its own output.

full rationale

The paper's central quantity, Ntrans, is computed by Equation 8 as a direct product of three independently sourced inputs: the LISA localization volume Vloc (Equation 1, using parametric fits from Mangiagli et al. 2020), the volumetric transient rates R(z) (adopted from the external observational literature: Dilday et al. 2008, Hounsell et al. 2018, Foley et al. 2013, Strolger et al. 2015, Fong et al. 2015, Wanderman & Piran 2010, van Velzen 2018, Kochanek 2016, Law et al. 2017), and observation-time windows Delta-t (from Villar et al. 2017 and Yao et al. 2023). None of these inputs is fitted to the number of contaminants, and the quoted z thresholds are obtained by inverting this product, not by construction. The localization-volume fits of Mangiagli et al. (2020) are external to this paper and are used with their assumptions clearly stated, including the uniform draws over mass ratio and spin; the paper explicitly notes in Section 4.2 that it does not vary mass ratio because the adopted method does not take it as input. This is a transparent propagation of an external approximation, not a circular derivation. Citations to prior work by co-authors appear only for predicted EM transient emission mechanisms (e.g., Bode et al. 2010, 2012; Bogdanovic et al. 2011) and are not load-bearing for the contamination-rate calculation. The acknowledged weakness that the Fisher-matrix volumes may be biased by a factor of a few is a correctness and robustness concern, not a circularity, because the volumes are not derived from the paper's target answer. The derivation is therefore self-contained against external benchmarks and no fitted parameter is renamed as a prediction.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

The calculation rests entirely on external inputs: localization volumes from Mangiagli et al. (2020) and transient rates from the literature. The paper's own hand-chosen parameters are the mass distribution slope, the FRB integration time, and the redshift cutoff. No new physical entities or self-derived constants appear.

free parameters (3)
  • Mass distribution slope alpha for simulated merger catalog = 0 (flat in log M)
    Chosen by hand in Section 2.1; the authors test that results are insensitive for alpha between -0.4 and 0.2.
  • FRB integration time T_obs = 3 minutes (with a 1 hour variation)
    Adopted in Section 2.4; the FRB count scales linearly with T_obs, but FRBs remain a negligible contaminant class.
  • High-redshift cutoff z_max for the no-redshift scenario = 3
    Chosen in Section 2.4 to exclude transients too faint to detect; this cutoff directly affects the absolute count in the no-redshift scenario.
assumptions (5)
  • domain assumption Flat Lambda-CDM cosmology with H0=70 km/s/Mpc, Omega_m=0.3, Omega_Lambda=0.7
    Stated in Section 1; used to convert luminosity distance to comoving volume in Eqs. 1 and 8.
  • domain assumption LISA localization volumes are accurately represented by the Fisher-matrix parametric functions of Mangiagli et al. (2020)
    Adopted in Section 2.2; all contaminant counts are proportional to these volumes, and the fits assume uniform distributions for mass ratio and spin.
  • domain assumption Extragalactic transient volumetric rates from the cited literature are correct and mutually independent
    Adopted in Section 2.3; no uncertainties are propagated, and the exact thresholds where the contaminant count equals unity depend on the absolute rate normalizations.
  • domain assumption The expected number of contaminants equals the product of rate, comoving volume, and transient lifetime (steady-state snapshot)
    Equation 8 in Section 2.4 assumes a single observation covering the localization region and ignores light curve detectability, as stated in the introduction.
  • domain assumption Redshifts for all detected transients can be obtained in the 'with redshift' scenario, and none can be obtained in the 'without redshift' scenario
    Section 2.4 defines these two extreme scenarios; real follow-up will lie between them.

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Cite this review

Pith. "Pith review of Contaminating Electromagnetic Transients in LISA Gravitational Wave Localization Volumes. I: The Intrinsic Rates." pith.science (2026). https://pith.science/paper/F3LFZT2P

@misc{pith2026250202839,
  author       = {Pith},
  title        = {Pith review of: Contaminating Electromagnetic Transients in LISA Gravitational Wave Localization Volumes. I: The Intrinsic Rates},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/F3LFZT2P}},
  note         = {Machine review of arXiv:2502.02839}
}
abstract

The Laser Interferometer Space Antenna (LISA) will soon detect gravitational waves (GWs) emitted by massive black hole (MBH) mergers. Some theoretical models have predicted transient electromagnetic (EM) emission from these mergers, enabling the association of LISA GW sources with their EM counterparts via telescope follow-up. However, the number of unrelated EM transients that might contaminate telescope searches for the true transient counterparts of LISA MBH mergers is unknown. We investigate the expected numbers of unrelated EM transients that will coincide with simulated LISA localization volumes of MBH mergers, as a function of the merger total mass and redshift. We find that the number of potential contaminants in LISA localization volumes drops to unity for mergers at $z \lesssim 0.8$ and at 1 hour before coalescence. After coalescence, the parameter space corresponding to a maximum of one potential contaminant expands to $z \lesssim 1.5$. In contrast, if the redshifts for all transients detected in LISA sky localization regions are not available, the number of potential contaminants increases by an average factor of $\sim100$, and never drops below unity. Overall, we expect the average number of contaminating transients in telescope follow-up of LISA MBH mergers to be non-negligible, especially without redshift information for the detected transients. We recommend that endeavors designing follow-up strategies of LISA events should focus on: (1) building large redshift catalogs for host galaxies, (2) developing robust real-time transient classification algorithms, (3) and coordinating telescope resources to obtain redshifts for candidate transient EM counterparts in a timely manner.

Figures

Figures reproduced from arXiv: 2502.02839 by the authors.

Figure 1
Figure 1. LISA localization (comoving) volume as a function of MBH merger total mass (Mtot), redshift (z), and the time to coalescence. The LISA localization volume increases with the merger mass and redshift and decreases with time to coalescence before the merger. After the merger, the localization volume is smallest for mergers with a Mtot slightly greater than 106 M⊙ at z < 1; the Mtot corresponding to the smallest locali… view at source ↗
Figure 2
Figure 2. The adopted volumetric rate as a function of redshift for each EM transient class considered in this work. Solid curves indicate the redshift range where the volumetric rate has been explicitly calibrated using observations, while dash-dotted curves indicate the redshift range where the vol￾umetric rate is derived using indirect correlations or theoret￾ical models. shift range extended by the localization volume to … view at source ↗
Figure 3
Figure 3. Results of the first investigation—the average number of unrelated transients expected in LISA localization volumes at different times before coalescence. The averaging is taken over simulated MBH mergers in our first catalog that fall into the redshift ranges indicated at the top of each panel. The blue curves give the estimated number of unrelated transients assuming redshifts are available for all transients dete… view at source ↗
Figures from the paper (3 more)
Figure 4
Figure 4. Figure 4: The number of unrelated transients coinciding with LISA MBH merger events, as a function of total binary mass (Mtot) and redshift (z), at different times before and after coalescence. Top: The left and center panels show the number of unrelated transients in LISA local…
Figure 5
Figure 5. Figure 5: The number of unrelated transients coinciding with LISA MBH merger events, as a function of total binary mass (Mtot) and redshift (z), at different times before coalescence. The colors and contour lines indicate the expected number of unrelated transients for MBH merge…
Figure 6
Figure 6. Figure 6: The percentage of contaminating transients be￾longing to a particular class as a function of redshift. Core￾collapse supernovae account for most of the contaminating transients. The numbers of expected GRBs and FRBs are negligible in comparison to that of core-collapse…

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