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arxiv: 2605.00391 · v1 · submitted 2026-05-01 · 🌌 astro-ph.IM · astro-ph.HE· gr-qc

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Training a neural network to rapidly identify candidate gravitational-wave events in the lower mass gap

Nayyer Raza , Man Leong Chan , Daryl Haggard , Ashish Mahabal , Jess McIver , Audrey Durand , Alexandre Larouche , Hadi Moazen
Authors on Pith no claims yet

Pith reviewed 2026-05-09 19:05 UTC · model grok-4.3

classification 🌌 astro-ph.IM astro-ph.HEgr-qc
keywords gravitational wavesneural networksmass gapneutron starsblack holesmachine learningbinary mergersLIGO-Virgo-KAGRA
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The pith

A neural network trained on gravitational-wave signals can rapidly estimate the chance a candidate merger involves a neutron star or a component in the lower mass gap.

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

The paper trains a model called GWSkyNet-MassGap to output two probabilities for any candidate event: the chance one component lies in the uncertain 2-5 solar-mass range and the chance the event contains a neutron star. These outputs matter because they can guide whether to trigger expensive electromagnetic follow-up observations while the signal is still fresh. The network achieves this by learning patterns tied to the chirp mass of the source, which lets it classify high-mass mergers accurately but leaves it weaker on lower-mass systems where the mass ratio is also needed. Tests on real O4a candidates show average errors of 9 percent on the mass-gap probability and 6 percent on the neutron-star probability. The work positions the model as a fast filter that could be extended to directly estimate chirp mass in future runs.

Core claim

The neural network GWSkyNet-MassGap simultaneously predicts the probability that a candidate gravitational-wave merger has a component in the lower mass gap and the probability that it involves a neutron star. It does so by inferring information from the source chirp mass, which produces correct classifications for mergers with chirp masses above about 15 solar masses but less reliable results for lower-mass systems that require the binary mass ratio to resolve the degeneracy. On the first part of LVK's O4 observing run the model shows a mean prediction error of 9 percent for the mass-gap probability and 6 percent for the neutron-star probability.

What carries the argument

The neural network GWSkyNet-MassGap, which takes candidate gravitational-wave event parameters and outputs two probabilities by learning patterns linked to chirp mass.

If this is right

  • High-mass mergers can be classified quickly enough to decide on electromagnetic follow-up before the signal fades.
  • Lower-mass candidates will still need additional information such as mass ratio to reach the same accuracy.
  • The same architecture could be retrained to output an explicit chirp-mass estimate rather than only probabilities.
  • Real-time use in future observing runs would reduce the volume of alerts that require full parameter estimation.
  • Repeated application across many events could help map the actual population inside the lower mass gap.

Where Pith is reading between the lines

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

  • Pairing the model with existing low-latency pipelines could create a two-stage filter that first screens for mass-gap candidates and then triggers detailed analysis only on promising ones.
  • If the chirp-mass inference remains robust across detector networks, the approach might generalize to other classification tasks such as distinguishing binary neutron-star from neutron-star-black-hole mergers.
  • Extending the input features beyond chirp mass alone could test whether the current performance gap for low-mass systems is fundamental or simply a matter of missing information.
  • Public release of the model weights would let other teams test it on simulated populations with varying mass-gap assumptions and measure how sensitive the outputs are to the training distribution.

Load-bearing premise

The network will generalize from its training examples to new real-world gravitational-wave observations without large errors from overfitting or changes in the data distribution.

What would settle it

Run the trained model on a new set of confirmed gravitational-wave events with independently measured component masses and check whether the predicted probabilities match the actual presence or absence of a mass-gap object or neutron star within the stated error bars.

Figures

Figures reproduced from arXiv: 2605.00391 by Alexandre Larouche, Ashish Mahabal, Audrey Durand, Daryl Haggard, Hadi Moazen, Jess McIver, Man Leong Chan, Nayyer Raza.

Figure 1
Figure 1. Figure 1: Probability that a compact object with mass m is a neutron star (NS) and probability that it is in the lower mass gap (MassGap) (bottom panel). The probabilities are determined from the posterior samples for the maximum NS mass from I. Legred et al. (2021) and the minimum BH mass from the PowerLaw+Peak model from R. Abbott et al. (2023b) (top panel). The probabilities are then used to cal￾culate the combin… view at source ↗
Figure 2
Figure 2. Figure 2: Scatter plot showing the component masses (m1, m2) of all 2 × 104 events in our generated data set, and their determined MassGap and NS probabilities according to Eqs. 1-3. The density of points in the mass gap range of ∼ 2.2−5.1 M⊙ shows that while a lower fraction of events occur in this range, the mass “gap” is not empty. 14% of events in our data set have PMassGap > 0.5, while 24% have PNS > 0.5 (note … view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of the predicted MassGap (green) and NS (orange) probabilities to the true probabilities for all 104 events in our test set. Points that lie along the dashed gray diagonal line indicate perfectly accurate predictions. Overlaid are the mean values of the predictions binned by true probability. The majority of events in the test set have zero (or near-zero) true MassGap and NS probabilities, and s… view at source ↗
Figure 4
Figure 4. Figure 4: Scatter plot showing the chirp mass (Mc) and mass ratio (q = m1/m2) of the 104 events in our test data set, and their true (top row) and GWSkyNet-MassGap predicted (bottom row) MassGap and NS probabilities. The top row is comparable to view at source ↗
Figure 5
Figure 5. Figure 5: Scatter plot showing the GWSkyNet-MassGap predicted MassGap (left panel) and NS (right panel) probabilities of the 104 events in our test set as a function of the source chirp mass. Each point is colored by its true MassGap and NS probability. Overlaid are the predicted (solid pink) and true (dashed blue) mean probabilities when the events are binned according to their chirp mass. For the NS predictions th… view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of the final determined value of the distance to the source (GWTC-4.0 median distance) versus the online low-latency determined value (Bayestar mean distance) for the 69 events in O4a analyzed in this work. The points are colored according to the MassGap event probability predicted by GWSkyNet-MassGap. The dashed gray line along the diagonal indicates where the Bayestar distance is equal to the … view at source ↗
read the original abstract

The physics governing the boundary between the most massive neutron stars (NSs) and the least massive black holes (BHs) is currently uncertain, but could potentially be constrained with new observations. While NSs have been observed with masses up to $\sim2~M_{\odot}$, there is a dearth of electromagnetic observations of compact objects in the $\sim2-5~M_{\odot}$ range, known as the lower mass gap. Recent observations of gravitational-wave (GW) signals from binary mergers detected by the LIGO-Virgo-KAGRA (LVK) collaboration indicate that this gap is likely not empty. Rapidly distinguishing whether a candidate GW event has components in this purported mass gap can indicate the likelihood of a detectable electromagnetic counterpart, and thus inform decisions for follow-up observations. In this work we train a neural network model, GWSkyNet-MassGap, that simultaneously predicts the probability that a candidate merger has a component in the lower mass gap ($P_{\mathrm{MassGap}}$) and the probability that it involves a NS ($P_{\mathrm{NS}}$). We find that the model is able to infer information about the source chirp mass to predict $P_{\mathrm{MassGap}}$ and $P_{\mathrm{NS}}$, leading to correct predictions for high-mass mergers with $\mathcal{M}_c\gtrsim15~M_{\odot}$, but less accurate predictions for lower-mass systems which require knowledge of the binary mass ratio to break the mass degeneracy. For candidate events in the first part of LVK's fourth observing run (O4a), the model has a mean prediction error of 9% for $P_{\mathrm{MassGap}}$ and 6% for $P_{\mathrm{NS}}$. The model could be further developed to rapidly predict the source chirp mass for candidate events in future observing runs.

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

2 major / 2 minor

Summary. The paper trains a neural network called GWSkyNet-MassGap to simultaneously predict the probability that a gravitational-wave candidate has a component in the lower mass gap (P_MassGap) and the probability that it involves a neutron star (P_NS). The model is reported to infer source chirp mass information, yielding correct predictions for high-mass mergers with M_c ≳15 M_⊙ but less accurate results for lower-mass systems due to mass-ratio degeneracy. On O4a candidates, aggregate mean prediction errors are stated as 9% for P_MassGap and 6% for P_NS. The work suggests the model could be extended to predict chirp mass directly.

Significance. If validated with complete methodological details and stratified performance metrics, this approach could offer a practical tool for rapid triage of GW candidates to prioritize electromagnetic follow-up, which is valuable for multi-messenger astronomy. The explicit recognition of the chirp-mass limitation and mass-ratio degeneracy shows physical insight. Strengths include the focus on actionable probabilities for real-time LVK operations rather than full parameter estimation.

major comments (2)
  1. [Abstract] Abstract: The reported mean prediction errors (9% for P_MassGap, 6% for P_NS) on O4a events are aggregate statistics with no stratification by chirp mass, true P_MassGap value, or mass regime. The text states that predictions are less accurate for lower-mass systems (which require mass-ratio information to resolve degeneracy), yet the means may be dominated by high-M_c events where P_MassGap is near zero by construction. Without binned or conditional error metrics, the numbers do not demonstrate that the model adds actionable information precisely for the mass-gap identification task that motivates the work.
  2. [Abstract] Abstract and model description: No information is supplied on training dataset composition (e.g., simulated waveforms, mass distributions, noise realizations), neural network architecture, loss function, training/validation split, cross-validation, or the procedure used to compute the quoted mean prediction errors and their uncertainties. These omissions prevent verification that the reported performance reflects genuine generalization rather than overfitting or distribution shift from training to real O4a data.
minor comments (2)
  1. [Abstract] Ensure consistent notation for chirp mass (e.g., M_c vs. script M_c) between the abstract and main text.
  2. [Abstract] The final sentence on extending the model to predict chirp mass is forward-looking but would benefit from a brief statement of how this would be implemented or evaluated.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive and detailed comments, which have helped us identify areas for improvement. We address each major comment below and have revised the manuscript accordingly to strengthen the presentation of our results.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The reported mean prediction errors (9% for P_MassGap, 6% for P_NS) on O4a events are aggregate statistics with no stratification by chirp mass, true P_MassGap value, or mass regime. The text states that predictions are less accurate for lower-mass systems (which require mass-ratio information to resolve degeneracy), yet the means may be dominated by high-M_c events where P_MassGap is near zero by construction. Without binned or conditional error metrics, the numbers do not demonstrate that the model adds actionable information precisely for the mass-gap identification task that motivates the work.

    Authors: We agree that aggregate mean errors alone are insufficient to fully demonstrate the model's utility for mass-gap identification, given the acknowledged performance differences across mass regimes. In the revised manuscript, we will add stratified performance metrics, including mean prediction errors binned by chirp mass ranges and by true P_MassGap values, to provide a clearer assessment of actionable information for lower-mass systems. revision: yes

  2. Referee: [Abstract] Abstract and model description: No information is supplied on training dataset composition (e.g., simulated waveforms, mass distributions, noise realizations), neural network architecture, loss function, training/validation split, cross-validation, or the procedure used to compute the quoted mean prediction errors and their uncertainties. These omissions prevent verification that the reported performance reflects genuine generalization rather than overfitting or distribution shift from training to real O4a data.

    Authors: We acknowledge these omissions in the current manuscript. The revised version will include complete details on the training dataset composition (simulated waveforms, mass distributions, and noise realizations), neural network architecture, loss function, training/validation splits, cross-validation procedures, and the exact computation of mean prediction errors with uncertainties. This will enable verification of generalization performance. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected in model training or evaluation.

full rationale

The paper presents a standard supervised neural network (GWSkyNet-MassGap) trained to output P_MassGap and P_NS from gravitational-wave candidate features. It explicitly notes that the network infers from chirp mass, performs well on high-M_c systems, and is weaker on low-mass systems due to mass-ratio degeneracy. Evaluation uses separate O4a candidate events with reported mean errors (9% and 6%). No equations or steps reduce by construction to the inputs, no parameters are fitted then relabeled as predictions, and no load-bearing self-citations or uniqueness theorems are invoked. The chain is a conventional ML pipeline with independent test data and acknowledged limitations, remaining self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

1 free parameters · 0 axioms · 0 invented entities

The central performance claims rest on standard supervised learning assumptions that the training distribution matches real events and that the network has not overfit to simulation artifacts.

free parameters (1)
  • Neural network weights and biases
    Fitted during training to minimize prediction error on the (unspecified) training set.

pith-pipeline@v0.9.0 · 5676 in / 1213 out tokens · 44096 ms · 2026-05-09T19:05:41.856407+00:00 · methodology

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