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Statistical Analysis of Early Spectra in Type II and IIb Supernovae

T0 review · 4 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read Type IIb supernovae show stronger H-alpha and He I absorption than type II in the first 40 days, a difference strong enough to reclassify 34 supernovae and nearly double the estimated IIb fraction.

desk verdict The spectral separation result and the classification tool are solid and citable, but the headline IIb rate revision (4.0% to 7.26%) does not survive arithmetic and should not be used until the denominator and sample selection are fixed. read the letter →

arxiv 2507.08731 v1 pith:WUJZJRRH submitted 2025-07-11 astro-ph.HE

Maider González-Bañuelos , Claudia P. Gutiérrez , Lluís Galbany , Santiago González-Gaitán This is my paper · ORCID
classification astro-ph.HE
keywords supernovaclassificationtypeIIbsupernovaeIIpseudo-equivalentwidthH-alphalineHeI5876core-collapseratesrandomforestclassifier
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

This paper sets out to show that type II and type IIb supernovae, which look nearly identical in the first days after explosion, can be told apart quantitatively by how much their early spectra absorb. Using 866 public spectra from 393 supernovae, it measures the pseudo-equivalent width and full width at half maximum of H-alpha and He I 5876 absorption within 40 days of explosion and finds that type IIb events consistently show stronger and broader absorption, with the clearest separation between 10 and 20 days. The paper uses those measurements to train a random-forest classifier and applies it to low-resolution spectra, identifying 34 supernovae whose official classifications are probably wrong. Correcting for these raises the inferred fraction of type IIb events from 4.0% to 7.26%, so the practical stake is that published core-collapse supernova subtype rates may be biased by early-time misclassification.

What carries the argument

The load-bearing objects are the pseudo-equivalent width (pEW) and the full width at half maximum (FWHM) of the H-$\alpha$ and He I $\lambda5876$ absorption features. pEW, defined as $\mathrm{pEW}=\sum_i (1 - f(\lambda_i)/f_0(\lambda_i))\,\Delta\lambda_i$, measures how much flux a line removes relative to the local continuum, and the FWHM, obtained from a Gaussian fit as $2\sqrt{2\ln 2}\,\sigma$, measures the velocity spread of the ejecta. The paper combines these four measurements with the epoch and feeds them to a Random Forest Classifier, whose classification probabilities provide the practical tool; Quadratic Discriminant Analysis supplies the decision regions used for visual checks, and t-SNE with LDA shows that the two classes form two clusters with a mixed continuum between them.

What would settle it

Take a sample of type II and IIb supernovae classified by an independent method, such as late-time spectra that clearly show either persistent hydrogen lines or helium-dominated ejecta, and re-measure pEW and FWHM in the first 10-20 days; if those two distributions overlap nearly completely, the paper's separation is an artifact of its training labels, whereas a clean offset would confirm the claim.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central discovery is that the strength of the H-alpha and He I 5876 absorption features is a clear discriminator between SNe II and SNe IIb in the first 40 days, not just in individual well-known objects but across a large balanced sample. SNe IIb have higher pseudo-equivalent widths and broader profiles at all early phases, the difference is statistically significant up to about day 30, and it peaks in the 10-20 day window. A Random Forest Classifier built from the pEW and FWHM of both lines plus the epoch separates the two classes with precision and recall above 0.8, and when applied to 106 low-resolution spectra it flags 34 likely misclassifications: 26 objects listed as type II look like type IIb, one listed as IIb looks like type II, and six remain ambiguous. Reclassifying those events changes the low-resolution sample's IIb fraction from 4.0% to 7.26%, which the paper interprets as evidence that spectroscopic misclassification of early spectra has a measurable effect on estimated core-collapse supernova rates.

Load-bearing premise

The classifications used to train the classifier were partly assigned by visually inspecting the same H-alpha and He I features that later serve as classifier inputs, so the measured separation may partly encode the authors' own judgments rather than an independent ground truth.

Editorial extensions

If this is right

  • SNe IIb will, on average, have stronger H-alpha and He I absorption than SNe II throughout the first 30 days, so an early spectrum alone can flag an object whose official type is doubtful.
  • The 10-20 day window is the most informative; spectra taken before day 10 or after day 30 lose discriminating power, so classification efforts should prioritize that window.
  • Line strength (pEW) separates the classes better than line width (FWHM), so coarse-resolution spectra that still resolve line depth may suffice for screening.
  • Applying the classifier to a magnitude-limited low-resolution sample shifts the inferred SNe IIb fraction from 4.0% to 7.26%, implying published subtype fractions for core-collapse supernovae are affected by misclassification.
  • The two populations form a continuum with overlapping regions rather than a clean gap, so the classifier's output is a probability, and objects near the boundary need additional information such as light-curve shape.

Reading between the lines

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

  • An extension the paper does not make: if the 7.26% fraction holds up, the true volumetric rate of SNe IIb may be roughly twice the commonly quoted value, and rate comparisons across surveys should re-derive completeness using the classifier's probabilities rather than hard labels.
  • The pEW continuum between the classes could be mapped directly to hydrogen-envelope mass using the models cited in the paper, turning a classification tool into a physical mass-loss estimator.
  • A direct testable extension would be to measure the same pEW/FWHM features on H-beta and the He I 6678 line; since the paper shows H-alpha and He I 5876 lose separation after day 30, additional lines may extend reliable classification later.
  • The classifier's success on low-resolution spectra suggests that real-time early classification from survey-grade spectra is feasible; a follow-up would be to run the same feature measurements on fully automatic continuum fitting and quantify how much the interactive continuum placement affects the probabilities.
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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. This paper compiles 866 early-time spectra of 393 SNe II and IIb from WISeREP, measures the pseudo-equivalent width (pEW) and FWHM of Hα and He I λ5876, and finds that SNe IIb show systematically stronger and broader features, with the largest differences in the 10–20 day interval. The authors support this with KS tests, density contours, QDA, t-SNE+LDA, and a Random Forest Classifier, and then apply the classifier to low-resolution SEDM spectra drawn from TNS, claiming 34 misclassifications and an increase in the estimated SNe IIb fraction from 4.0% to 7.26%.

Significance. If the spectral separation is real, the paper provides a large, carefully measured sample and a practical, publicly released classification tool; the comparison of measurement methods against IRAF and the release of median spectra and code are concrete strengths. However, the headline rate revision is not supported by the sample definition and internal arithmetic, and the statistical tests ignore the clustering of multiple spectra within individual SNe, so the central quantitative claims need substantial revision.

major comments (4)
  1. [Section 5.3, Figure 13] The claimed revision of the SNe IIb fraction from 4.0% to 7.26% is not reproducible from the stated sample counts. Section 5.2 gives an initial comparison sample of 106 SNe (92 II and 14 IIb) and a final sample of 145 SNe (123 II and 22 IIb) after adding 39 new objects; the IIb fraction of the final sample is therefore 22/145 = 15.2%, not 7.26%, and the 'before' 4.0% appears to be 25/634 from the full TNS SEDM collection rather than from the 145-SNe sample that was actually reclassified. Moreover, Tables B.2 and B.3 list 25 and 39 objects, respectively, and the number of definite II-to-IIb reclassifications in those tables does not add up to the 26 reported in the text; the 34 'misclassified' count and the before/after percentages therefore require an explicit denominator, a selection model for the SEDM/TNS sample, and a reconciliation of the table entries.
  2. [Section 4.1, Table 2] The KS tests treat all 866 spectra as independent, but the sample contains 393 SNe with 271 single-spectrum objects, 42 with two spectra, 22 with three, and 55 with four or more; repeated spectra from the same SN are correlated measurements of a single object. This clustering inflates the significance of the reported p-values (e.g., 7.9e-51 for the 0–40 d pEW comparison) and can bias the identification of the 10–20 d interval as the most significant. The authors should repeat the KS analysis with one randomly selected spectrum per SN, or use a mixed-effects model or a bootstrap resampled by SN, to verify that the time-interval ranking is robust.
  3. [Section 3.1, Table 1] The reclassification of 16 objects before the analysis was based on visual inspection of the same Hα and He I features that later serve as the pEW/FWHM inputs to the classifier in Section 4.2.4. This is a mild circularity because the training labels are partly derived from the discriminating features, so the measured separation and the classifier accuracy partly encode the authors' visual judgments. The authors should quantify the impact by rerunning the classification with the original TNS/WISeREP labels or by excluding the 16 reclassified objects from the training set.
  4. [Section 5.2 and abstract] The low-resolution sample is described in the abstract as from the 'Zwicky Transient Facility Bright Transient, a magnitude-limited survey,' but Section 5.2 states that the sample was gathered as 'all available SEDM spectra for SNe II and IIb from the TNS' (with objects lacking light curves excluded). These are different selections; the TNS collection is not necessarily the magnitude-limited BTS sample, and no selection function is given. Extrapolating the reclassification fraction from the 106/145-SNe subsample to the full population therefore requires a model relating the subsample to the underlying rate, which is not provided.
minor comments (5)
  1. [Abstract] The phrase 'Zwicky Transient Facility Bright Transient' should be 'Zwicky Transient Facility Bright Transient Survey (BTS)', and the sample used in Section 5.2 should be explicitly identified as a TNS SEDM collection, not as BTS itself.
  2. [Section 2.1] The text states that the initial sample contains 449 low-redshift SNe, but the final dataset after cuts is 393 SNe; please state the number of objects removed by each cut (resolution, explosion-date quality, and the SEDM exclusion).
  3. [Table 3] The table caption 'Comparison of precision and recall scores for the different classification methods' does not match the column layout; the columns labelled 'pEW' and 'FWHM' appear to be separate sub-tables, and the caption should clarify which features each block uses.
  4. [Figure A.7 caption] The caption 'FWHM HeI vs pEW Halpha' is likely a typo; the panel shows FWHM of He I versus FWHM of Hα, and the axis labels should match.
  5. [Section 4.2.4] Please report the number of trees, maximum depth, and other hyperparameters of the Random Forest Classifier, and use repeated cross-validation rather than a single 60/40 split to avoid optimistic precision/recall estimates.

Circularity Check

2 steps flagged · score 3.0 of 10

Mild circularity: 16 SNe were visually reclassified using the same H/He lines later used as classifier inputs, and RFC probabilities for transitional objects are in-sample; the central pEW/FWHM separation retains substantial independent support.

  1. self definitional [Section 3.1 (Re-classification) and Section 3.2 (Spectral line identification)]
    "Before starting our analysis, we inspected all SN spectra in our sample to verify the accuracy of the assigned classifications. We visually checked the main features in the spectra and the LC morphology. For objects with multiple spectroscopic follow-up observations, the appearance of some spectral lines allows us to easily identify possible misclassifications. Through this inspection, we found 16 misclassified SNe. ... our analysis is focused only on the Hα absorption feature and the He I λ5876 line."

    The 14 objects reclassified from II to IIb were reassigned by visually inspecting the main spectral features, which are the same H and He I lines whose pEW and FWHM are later used as the exclusive classifier inputs (Hα and He I λ5876). The measured separation, KS-test p-values, and RFC precision/recall therefore partly re-encode the authors' prior visual judgments rather than providing a fully independent ground truth. The impact is limited because these 16 objects are a small fraction of the 393-SNe sample, but the validation claim is not entirely external.

  2. fitted input called prediction [Section 5.4 (Transitional events) and Section 5.5 (A new tool to classify SNe II and IIb based on early spectra)]
    "we performed again a RFC (see section 4.2.4), taking this time our entire sample as a training sample and a newly measured SN as a test sample. ... For each transitional event, we also computed the RFC probability of belonging to each classification category (II/IIb/87A-like)."

    The transitional objects in Table 4 (e.g., 2019nyn, 2019uqp, 2019yyf, 2021agle, 2023rhj, 2024dgy, 2018arx, 2022emz) are part of the main sample listed in Table B.5. If the RFC is retrained on the entire sample and then applied to these objects, the reported probabilities (e.g., II: 1.0 for 2019nyn) are in-sample fits: the model has already seen the object's label, so its 'prediction' trivially agrees with the label. This does not provide independent support for the official classifications, though it is peripheral to the main spectral-separation claim.

full rationale

The core spectral comparison is not circular: 866 spectra from 393 SNe are measured for pEW/FWHM of Hα and He I λ5876, and the KS tests, QDA, t-SNE, and RFC are descriptive of those measurements. The labels mostly come from the literature/TNS, and the RFC is evaluated on a held-out 40% test set with precision/recall above 0.8, providing independent support. Two partial circularities exist. First, 16 objects were reclassified before analysis by visually inspecting the same H/He features that later form the classifier inputs; this partly injects the authors' visual judgments into the ground truth, although it is a small fraction of the sample. Second, the RFC probabilities reported for the 'transitional events' in Table 4 appear to be in-sample if the entire sample was used for training, making those probabilities tautological. The headline rate revision from 4.0% to 7.26% has an apparent denominator inconsistency (the 106/145-object SEDM subsample has 13-15% IIb, not 4%), but that is a sample-selection/arithmetic concern rather than a circular derivation. Self-citations to Gutiérrez et al. (2017) are methodological and not load-bearing. Overall, the central claim that SNe IIb show stronger Hα and He I absorption in early spectra retains substantial independent content, so the circularity score is moderate (3/10).

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

The paper rests on standard domain assumptions of supernova spectral classification. Key fragility: the training labels are not independent of the features used by the classifiers, because the authors revised 16 object labels before analysis.

free parameters (4)
  • t-SNE perplexity = 20
    Chosen for the 2D visualization in Section 4.2.3; different values change the projection and apparent separation.
  • KDE bandwidth = 1.0
    Default Gaussian bandwidth used for density contours in Section 4.2.1.
  • RFC train/test split ratio = 60/40
    Chosen for training the random forest; affects reported precision/recall.
  • RFC number of trees and depth = not stated
    Hyperparameters for the Random Forest Classifier in Section 4.2.4 are not specified, so results may not be exactly reproducible.
assumptions (3)
  • domain assumption The WISeREP/TNS type labels, after the authors' own reclassifications, are accurate enough to serve as ground truth.
    Used to train and evaluate all classifiers (Sections 2.1 and 3.1).
  • domain assumption Multiple spectra of the same supernova can be treated as independent samples for the KS tests.
    All 866 spectra are pooled in Section 4.1, Table 2; no correction for intra-object correlation is applied.
  • domain assumption Explosion epochs derived from last non-detection and first detection or template matching are accurate to within the stated uncertainty.
    All phase bins (0-10, 10-20, etc.) depend on these epochs (Section 2.2).

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Cite this review

Pith. "Pith review of Statistical Analysis of Early Spectra in Type II and IIb Supernovae." pith.science (2026). https://pith.science/paper/WUJZJRRH

@misc{pith2026250708731,
  author       = {Pith},
  title        = {Pith review of: Statistical Analysis of Early Spectra in Type II and IIb Supernovae},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WUJZJRRH}},
  note         = {Machine review of arXiv:2507.08731}
}
abstract

We present a comprehensive analysis of the early spectra of type II and type IIb supernovae (SNe) to explore their diversity and distinguishable characteristics. Using 866 publicly available spectra from 393 SNe, 407 from type IIb SNe (SNe IIb) and 459 from type II SNe (SNe II), we analysed H$\alpha$ and He~I 5876 A at early phases ($<40$ days from the explosion) to identify possible differences between these two SN types. By comparing the pseudo-equivalent width (pEW) and full width at half maximum (FWHM), we find that the strength of the absorption component of these lines serves as a quantitative discriminator, with SNe IIb exhibiting stronger lines at all times. The most significant differences emerge within the first 10-20 days. To assess the statistical significance of these differences, we apply statistical methods and machine-learning techniques. Population density evolution reveals a clear distinction in both pEW and FWHM. Quadratic Discriminant Analysis confirms distinct evolutionary patterns, particularly in pEW, while FWHM variations are less pronounced. A combination of t-distributed Stochastic Neighbour Embedding and Linear Discriminant Analysis effectively separates the two SN types. Additionally, a Random Forest Classifier demonstrates the robustness of pEW and FWHM as classification criteria, allowing for accurate classification of newly observed SNe II and IIb based on computed classification probabilities. Applying our method to low-resolution spectra obtained from the Zwicky Transient Facility Bright Transient, a magnitude-limited survey, we identified 34 misclassified SNe. This revision increases the estimated fraction of SNe IIb from 4.0% to 7.26%. This finding suggests that misclassification significantly impacts the estimated core-collapse SN rate. Our approach enhances classification accuracy and provides a valuable tool for future supernova studies.

Figures

Figures reproduced from arXiv: 2507.08731 by the authors.

Figure 1
Figure 1. Histogram of the number of spectra per SN. In total, our sample is composed of 866 spectra for 393 SNe. 2. Data sample and spectral measurements 2.1. Sample The sample analysed in this study includes all available SN IIb spectra in the WISeREP1 (Yaron & Gal-Yam 2012) repository. We retrieved these data using the wiserep_api2 tool. To ensure a comparable sample, and considering that SNe IIb are less abun￾dant than SN… view at source ↗
Figure 2
Figure 2. Redshift distribution for the 393 SNe in our sample. The vertical lines indicated the median of the whole sample (solid black line) at 0.022, SNe II (dashed-dotted red line) at 0.021 and IIb (dashed blue line) at 0.023. the number of spectra by SN type. Table B.5 presents the whole dataset (Appendix B). The redshift distribution of our sample is shown in [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. An example of the pEW and FWHM measurement for the He I line. On the top panel, we show the selection of the two regions to define the line to be measured. The defined line and the Gaussian fitting for the pEW and FWHM computation are shown in the bottom panel. The different line colours represent the shifted measurements used to estimate the uncertainties. 3.2. Spectral line identification Given that the spectra of… view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: Evolution of type II SNe 1999em (Leonard et al. 2001) (red) and 2013ej (Valenti et al. 2013) (orange) and type IIb SNe 1993J (Barbon et al. 1995) (blue) and 2011dh (Valenti et al. 2013; Ergon et al. 2014) (purple). The epoch of each of the spectra is computed from the …
Figure 5
Figure 5. Figure 5: Comparison between the median spectra of SNe II and IIb in different time ranges: 0 – 10 days post-explosion, 10 – 20 days, 20 – 30 days and 30 – 40 days. Blue spectra correspond to SNe IIb, and red spectra to SNe II. The dashed vertical line marks the rest wavelength …
Figure 6
Figure 6. Figure 6: Left panel: pEW of the H𝛼 absorption profile versus the pEW of the He I 𝜆5876 line measured across the full-time interval (0 – 40 days). The markers are established by SN type: SNe IIb are shown as blue triangles, SNe II as red circles, and SNe 87A-like as green square…
Figure 7
Figure 7. Figure 7: pEW and FWHM values of H𝛼 line and He I 𝜆5876 for 0 – 10 days and 10 – 20 days. The markers are established by SN type: SNe IIb are shown as blue triangles, SNe II as red circles, and SNe 87A-like as green squares. The colour bars on the right side indicate the SN phas…
Figure 8
Figure 8. Figure 8: Density contours of SNe II (in red) and IIb (in blue) based on the pEW measurements. The colour bars on the right side indicate the normalised density values, with darker colours representing higher densities. Each plot represents the density contours at different time…
Figure 9
Figure 9. Figure 9: Decision boundaries defined by the QDA classifier based on the pEW measurements for SN II and IIb pEW are shown across three time intervals: 0 – 10 days (left), 10 – 20 days (middle) and 0 – 40 days after the explosion (right). The red region corresponds to predictions…
Figure 10
Figure 10. Figure 10: Resulting t-SNE 2D reduced plot, using a perplexity value of 20. The red and blue dots represent SNe II and IIb, respectively, while green triangles indicate 87A-like events. The figure is separated into two regions defined by the LDA, with the solid line representing…
Figure 11
Figure 11. Figure 11: Probabilities assigned by the RFC based on five parameters (pEW and FWHM of H𝛼 and He I, along with the epoch) within the first 40 days post-explosion. Top panel: Top panel: Classification probabil￾ities obtained by randomly selecting one spectrum per SN from those wi…
Figure 12
Figure 12. Figure 12: pEW measurements of H𝛼 and He I for the comparison sample (SEDM plus newly classified SNe) overlaid on the density contours of SNe II (red) and IIb (blue) obtained in Section 4.2.1 (same as [PITH_FULL_IMAGE:figures/full_fig_p012_12.png]
Figure 13
Figure 13. Figure 13: Comparison of the fraction of SEDM spectra of each SN type before and after the performance of our re-classifications. the model fails to identify the SN type. The metric scores for the methods are presented in [PITH_FULL_IMAGE:figures/full_fig_p013_13.png]
Figure 14
Figure 14. Figure 14: CC-SNe fractions computed by Shivvers et al. (2017) (right￾top), Perley et al. (2020) (bottom) and the fractions computed in this work by using the available data on TNS since 2016 (left-top). This suggests that classification uncertainties, especially in dis￾tinguish…

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Pith tools

Reviewed August 6, 2026 · model on record in the stance chip above.