arxiv: 2604.12704 · v1 · submitted 2026-04-14 · 🌌 astro-ph.SR · astro-ph.GA

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The undetectable fraction of core-collapse supernovae in luminous infrared galaxies -- II. GSAOI/GeMS dataset

I. M\"antynen , E. Kankare , S. Mattila , A. Efstathiou , S. D. Ryder , E. Kool , K. Matilainen , T. M. Reynolds

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C. Vassallo P. V\"ais\"anen

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Pith reviewed 2026-05-10 14:33 UTC · model grok-4.3

classification 🌌 astro-ph.SR astro-ph.GA

keywords core-collapse supernovaeluminous infrared galaxiesdust extinctionundetectable fractionadaptive opticssupernova surveysstar formation rate

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The pith

Near-infrared adaptive optics data show that 86 percent of core-collapse supernovae in local luminous infrared galaxies remain undetectable in optical surveys.

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

The paper measures the fraction of core-collapse supernovae hidden by dust in a sample of nine local luminous infrared galaxies using repeated K-band imaging with laser-guide-star adaptive optics. It runs Monte Carlo simulations that fold in each epoch's detection limits, the survey timing, the mix of supernova subtypes, and the range of their brightness evolution to find the probability that any given event would be caught. These probabilities are then compared to the galaxies' intrinsic supernova rates, which are derived from detailed modeling of their spectral energy distributions. The resulting numbers indicate that even surveys tolerant of substantial dust extinction still miss the majority of events. This matters because luminous infrared galaxies account for a large share of local star formation, so their hidden supernovae directly affect estimates of the cosmic supernova rate.

Core claim

Based on the GSAOI/GeMS near-infrared adaptive optics monitoring dataset, the total undetectable fraction of core-collapse supernovae reaches 86.0^{+4.7}_{-5.9} percent for optical surveys limited to A_V = 3 mag extinction and 53.6^{+15.6}_{-19.6} percent for near-infrared surveys limited to A_V = 16 mag. When the new results are combined with an earlier adaptive-optics LIRG monitoring sample, the fractions increase to 88.3^{+2.6}_{-3.2} percent and 61.4^{+8.5}_{-10.6} percent, respectively. These values are obtained by comparing simulated detection probabilities against intrinsic rates estimated from spectral energy distribution modeling of each galaxy.

What carries the argument

Monte Carlo simulations that combine measured limiting magnitudes from artificial-supernova injection tests, survey cadence, literature-based CCSN subtype distributions, and light-curve diversity to compute per-galaxy detection probabilities.

Load-bearing premise

The simulations rest on assumed distributions of supernova subtypes and light-curve shapes taken from the literature, plus the accuracy of the galaxies' intrinsic supernova rates derived from spectral energy distribution modeling.

What would settle it

A significantly larger number of core-collapse supernovae detected in a comparable sample of local LIRGs than the model predicts, or a measured extinction distribution that deviates strongly from the one used in the simulations.

Figures

Figures reproduced from arXiv: 2604.12704 by A. Efstathiou, C. Vassallo, E. Kankare, E. Kool, I. M\"antynen, K. Matilainen, P. V\"ais\"anen, S. D. Ryder, S. Mattila, T. M. Reynolds.

**Figure 1.** Figure 1: Luminous infrared galaxies of the GSAOI/GeMS in 36" × 36" field of view (except NGC 3110 in 46" × 46"). North is up, and east is to the left. Due to the pointing of the observations, the target galaxy is not necessarily in the centre of the shown image subsection. regions, or high host extinction. Disentangling the effect of host extinction is not straightforward; however, we aimed to constrain this by sim… view at source ↗

**Figure 2.** Figure 2: Template K-band light curves of CCSN subtypes used in the Monte Carlo simulation. The light curves have been shifted vertically for clarity. The magnitudes are in the Vega system. et al. 2014; Kochanek et al. 2017), resulting in mean values of AV = 0.23 ± 0.18 and 0.30 ± 0.16 mag for Type II and stripped envelope subtypes, respectively. It is clear that especially the optical observations are extremely bi… view at source ↗

**Figure 3.** Figure 3: Results of the SED modelling (solid black) of seven LIRGs of the GSAOI/GeMS dataset produced in this work. The model components are: either a spheroidal (dot-dashed orange) or a disc (dot-dashed green) galaxy, starburst contribution (dashed red), and an AGN (dotted blue). near-IR evolution of representative examples of H-rich and Hpoor SNe of the Type IIP SN 1999em (Krisciunas et al. 2009) and the Type II… view at source ↗

**Figure 4.** Figure 4: Absolute light curves of AT 2015cf (with detections as points and limits with downwards pointing arrows) compared to a selection of events (curves) including the ILRT AT 2019abn (left), the LRN AT 2021blu (centre), and the Type IIP SN 2023ixf (right). The extrapolated tail phase of SN 2023ixf is indicated with dotted lines. The intrinsic brightness of the events has not been adjusted and no host galaxy ext… view at source ↗

**Figure 5.** Figure 5: Number of expected CCSN detections over the survey period of the dataset as a function of the missing fraction. Left: The GSAOI/GeMS dataset. Right: The GSAOI/GeMS dataset combined with the ALTAIR/NIRI dataset from Paper I. Red line: The mean value. Shaded red areas: 1σ and 2σ confidence intervals. Poissonian upper and lower limits due to small number statistics and the cumulative effect of other error sou… view at source ↗

read the original abstract

Core-collapse supernovae (CCSNe) in luminous infrared galaxies (LIRGs) can have extreme line-of-sight host galaxy dust extinctions, which leads to a large fraction of the events remaining undetected by optical and infrared surveys. This population of undetected CCSNe is important to constrain in order to determine the cosmic CCSN rates. Our aim is to confirm and refine our estimates for the undetectable fraction of CCSNe in LIRGs in the local Universe. Our study is based on the near-infrared K-band multi-epoch SUNBIRD survey monitoring dataset of a sample of nine LIRGs using the Gemini-South telescope with the multi-conjugate GSAOI/GeMS laser guide star adaptive optics system. We determined the limiting magnitudes for CCSN detection for each epoch in our dataset with artificial supernova injection and image subtraction methods. Subsequently, we used a Monte Carlo method to determine the combined effects of limiting magnitudes, survey cadence, CCSN subtype distribution, and their light curve evolution diversity. The intrinsic CCSN rates of the sample galaxies were estimated based on detailed modelling of their spectral energy distribution. Finally, we combined the resulting CCSN detection probabilities with the intrinsic CCSN rates for the dataset, and compared that against the real CCSN detections over the survey period. Based on our GSAOI/GeMS dataset, assuming optical or near-infrared example surveys with capabilities to detect CCSNe in local LIRGs with host extinctions of $A_V =$ 3 or 16 mag, respectively, the resulting total undetectable fractions are $86.0^{+4.7}_{-5.9}$ % and $53.6^{+15.6}_{-19.6}$ %. When folding in the results from our previous near-infrared adaptive optics assisted LIRG monitoring dataset, the corresponding total undetectable fractions are $88.3^{+2.6}_{-3.2}$ % and $61.4^{+8.5}_{-10.6}$ %, respectively.

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.

Desk Editor's Note private letter to a colleague

New Gemini AO data on nine LIRGs tightens the undetectable CCSN fraction to 86% optical and 54% NIR, with combined prior results at 88% and 61%.

read the letter

This paper's main takeaway is that even near-IR adaptive optics monitoring still misses most core-collapse supernovae in local LIRGs, with undetectable fractions of 86% for optical surveys and 54% for NIR ones; folding in their earlier dataset pushes those to 88% and 61% respectively. The work adds fresh GSAOI/GeMS K-band observations of nine galaxies not covered before. They measure epoch-by-epoch limits via artificial supernova injection and subtraction, then run Monte Carlo trials that combine those limits with cadence, literature subtype fractions, light-curve diversity, and intrinsic rates taken from SED fits. The steps are transparent and the new data directly constrains the detection thresholds. The central fractions follow logically from the inputs they describe. The main soft spot is the dependence on external priors for CCSN subtypes and light-curve shapes, which carry their own scatter and could shift the percentages if updated. The error bars already reflect some of that spread, but the sample of galaxies is still modest and the NIR result remains especially uncertain. No circularity or hidden fitting appears in the derivation. Researchers who need quantitative corrections for hidden supernovae when estimating cosmic rates or star-formation history in dusty systems will find the numbers directly usable. The analysis is careful and the limitations are stated, so the paper deserves peer review rather than a desk reject.

Referee Report

0 major / 4 minor

Summary. The manuscript reports on the second installment of the SUNBIRD survey, presenting new multi-epoch K-band GSAOI/GeMS adaptive-optics observations of nine local LIRGs. Artificial-supernova injection and image-subtraction techniques are used to measure epoch-specific limiting magnitudes. These empirical limits, together with survey cadence, literature CCSN subtype fractions, light-curve diversity, and SED-derived intrinsic rates, are propagated via Monte Carlo simulations to obtain CCSN detection probabilities. The resulting undetectable fractions for example optical (A_V=3 mag) and near-infrared (A_V=16 mag) surveys are 86.0^{+4.7}_{-5.9}% and 53.6^{+15.6}_{-19.6}% from the new data alone; when the previous AO dataset is folded in, the fractions become 88.3^{+2.6}_{-3.2}% and 61.4^{+8.5}_{-10.6}%. The simulated detection numbers are compared with the actual CCSNe found in the survey.

Significance. If the quoted fractions hold, the work supplies a quantitatively important constraint on the hidden CCSN population in dusty starbursts, directly relevant to cosmic CCSN rate determinations. The new GSAOI/GeMS dataset supplies direct empirical limiting-magnitude measurements that tighten the earlier estimates. The Monte Carlo framework is standard and well-suited; the transparent combination of two independent AO datasets and the consistency check against real detections are strengths. The approach avoids circularity by deriving intrinsic rates independently from SED modeling and detection probabilities from simulations benchmarked on the observations.

minor comments (4)

§2.2: The text states that limiting magnitudes were determined for each epoch but does not tabulate the per-epoch values or their uncertainties; adding a supplementary table would allow readers to reproduce the Monte Carlo input distribution directly.
Figure 4: The caption does not specify whether the plotted light-curve templates are in the observed K band or rest-frame; clarifying the bandpass and the exact references for each subtype template would remove ambiguity.
§4.3: When the two datasets are combined, the procedure for merging the two independent Monte Carlo realizations (joint sampling versus weighted averaging of the separate fractions) is not stated explicitly; a short sentence or equation would make the 88.3% and 61.4% values fully reproducible.
Table 3: The reported asymmetric uncertainties on the undetectable fractions are given to one decimal place, but the underlying Monte Carlo sample size used to derive them is not mentioned; stating the number of trials would indicate whether the quoted precision is limited by sampling noise.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive and constructive assessment of our manuscript. The recommendation for minor revision is appreciated, and we note that no specific major comments were raised in the report.

Circularity Check

0 steps flagged

No significant circularity; central derivation uses independent inputs and simulations

full rationale

The undetectable fractions are obtained from Monte Carlo simulations that combine empirically measured limiting magnitudes (via artificial SN injections), survey cadence, literature CCSN subtype fractions and light-curve diversity, plus intrinsic rates from separate SED modeling of the galaxies. The GSAOI/GeMS dataset supplies direct observational constraints on detection thresholds. Folding in the authors' prior AO dataset constitutes dataset combination, not a load-bearing self-citation that collapses the result to an unverified premise. No equation reduces a prediction to a fitted parameter by construction, no uniqueness theorem is imported from self-citation, and the derivation remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

The claim rests on standard astrophysical modeling assumptions rather than new free parameters fitted to this dataset; subtype fractions and light-curve templates are taken from the literature.

free parameters (2)

CCSN subtype distribution
Weights for different core-collapse subtypes used in Monte Carlo sampling; taken from prior literature rather than fitted here.
Light-curve diversity parameters
Assumed range of peak luminosities, durations, and evolution shapes for each subtype in the simulations.

axioms (2)

domain assumption Intrinsic CCSN rates can be reliably estimated from galaxy SED modeling combined with a standard initial mass function.
Used to compute the expected number of events before applying detection probabilities.
domain assumption The combination of measured limiting magnitudes, survey cadence, and modeled light curves fully determines detection probability.
Foundation of the Monte Carlo method described.

pith-pipeline@v0.9.0 · 5721 in / 1694 out tokens · 34006 ms · 2026-05-10T14:33:31.645347+00:00 · methodology

discussion (0)

Reference graph

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2021