Pith. sign in

REVIEW 3 major objections 4 minor 1 cited by

ZTF SN Ia DR2: High-velocity components in the Si II $\lambda$6355

T0 review · 3 major / 4 minor · reviewed 2026-08-08 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read This paper argues that high-velocity components in the Si II λ6355 line are common in early Type Ia supernova spectra, appearing in about three quarters of spectra before -11 days and fading to about one third near maximum light.

desk verdict Careful efficiency-corrected measurement of Si II HVF rates; the qualitative ubiquity claim holds, but the headline percentages need systematic-error caveats. read the letter →

arxiv 2502.04448 v1 pith:D5CNSV6N submitted 2025-02-06 astro-ph.HE

L. Harvey , K. Maguire , U. Burgaz , G. Dimitriadis , J. Sollerman , A. Goobar , J. Johansson , J. Nordin
show 22 more authors
M. Rigault M. Smith M. Aubert R. Cartier P. Chen M. Deckers S. Dhawan L. Galbany M. Ginolin W. D. Kenworthy Y.-L. Kim C. Liu A. A. Miller P. Rosnet R. Senzel J. H. Terwel L. Tomasella M. Kasliwal R. R. Laher J. Purdum B. Rusholme R. Smith
This is my paper · ORCID
classification astro-ph.HE
keywords TypeIasupernovaeSiII6355high-velocityfeaturessupernovaspectroscopyZTFDR2spectrallinefittingdetectionefficiencyclassification
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

Using the Zwicky Transient Facility's SN Ia Data Release 2, this paper searches pre-peak spectra for a second, faster component in the Si II λ6355 absorption line and measures how common it is. After fitting single- and double-component models and correcting for how often the classifier would miss or falsely claim such a feature, the paper finds that roughly three quarters of spectra earlier than -11 days carry a high-velocity component, with the rate falling to about one third in the six days before maximum light. The extra component tends to be shallower and narrower than the photospheric line, and larger velocity separations fade first. The paper finds no difference in light-curve stretch, peak brightness, decline rate, host mass, or host colour between objects with and without the feature, and it reads that as evidence that the components are a normal, widespread part of SN Ia spectral evolution.

What carries the argument

The machinery is a two-doublet Gaussian model of the Si II $\lambda6355$ feature: one doublet at photospheric velocity and a second identical-shape doublet blue-shifted by a velocity separation $\Delta v$, each doublet's two lines tied in velocity, depth, and width. Single- and double-doublet models are fitted with Markov-chain Monte Carlo and compared with the Bayesian Information Criterion. The load-bearing part is a set of simulations that inject synthetic doublets with parameters drawn from a kernel density estimate of earlier PTF Si II measurements; these simulations measure the true- and false-positive rates of the classifier as a function of signal-to-noise, spectral dispersion, and $\Delta v$, and provide the corrections that convert the raw 85 detections into phase-resolved rates.

What would settle it

Run the same MCMC/BIC classification on only the spectra with the highest signal-to-noise and resolution (for example SNR ≥ 25 and dispersion 2 Å/pix) where detection efficiency is near unity; if the raw fraction of double-component detections before -11 d is far below three quarters, the efficiency correction is overcorrecting and the ubiquity claim would need to be revised.

Watch

Extended reading notes

Core claim

The central claim is that high-velocity components in Si II $\lambda6355$ are a common and phase-dependent feature of Type Ia supernova spectra rather than a rare peculiarity. In the 329 spectra that pass quality cuts the paper identifies 85 double-component detections, and after efficiency correction it estimates the presence rate as 76% (with asymmetric $1\sigma$ uncertainties of about +7/-9 percentage points) before -11 d, 46±7% between -11 and -6 d, and 29±5% in the six days before maximum. The fading of the component happens at different phases in different objects, with larger velocity separations disappearing first, so the global strength-versus-phase trend is flatter than the individual trend. The paper also claims that no SALT2 $x_1$, peak magnitude, decline rate, host mass, or local host colour difference separates objects with and without the feature, supporting ubiquity. A further claim is that single-component fits near peak misclassify up to about 26% of Wang high-velocity and 20% of Branch broad-line classifications by absorbing the high-velocity component into the photospheric measurement.

Load-bearing premise

The efficiency corrections assume that real high-velocity components are Gaussian doublets with the same range of depths, widths, and velocity separations as the simulated population drawn from PTF measurements, so if true components are systematically different in shape or strength, the reported rates and $\Delta v$ distribution could be biased.

Editorial extensions

If this is right

  • Before -11 d, roughly three quarters of observed SN Ia spectra should show a second, faster Si II component once detection efficiency is taken into account.
  • The high-velocity component fades at different phases in different objects, so a single epoch cannot reliably decide whether a given SN Ia has or lacks these features.
  • Larger velocity separations fade before smaller ones, so samples that mix phases will be biased toward low-$\Delta v$ components near maximum light.
  • Wang high-velocity and Branch broad-line classifications taken in the -5 to 0 d window can be contaminated by the high-velocity component; the paper estimates upper limits of about 26% and 20% respectively.
  • Any successful explosion or progenitor model must produce silicon at high velocity in most normal Type Ia supernovae without changing the standardised-candle properties.

Reading between the lines

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

  • A direct extension would be to apply the same efficiency-corrected double-component search to Ca II near-infrared and H&K lines in the same DR2 spectra; if both trace the same outer-ejecta structure, their velocity separations and fading phases should correlate.
  • If the high component is really a ubiquitous, phase-dependent feature, single-component Si II velocities in the literature that used pre-peak spectra may be systematically blue-shifted, which would affect velocity-gradient subclasses.
  • Because the simulation priors come from PTF measurements and assume Gaussian doublets, the claim is testable by recomputing the rates with a higher-resolution, higher-SNR subsample where the correction is small; the raw detection fraction there should still approach three quarters early on.
  • The absence of host-mass and colour dependence is more naturally compatible with an intrinsic ejecta density or abundance enhancement than with a circumstellar interaction tied to a particular progenitor environment; spectropolarimetry of the Si II feature across these phases could distinguish the two.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

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 presents a systematic search for high-velocity components (HVFs) in the Si II λ6355 feature using 329 pre-peak spectra from the ZTF SN Ia DR2 sample. The classification pipeline uses MCMC fits of single- and double-Gaussian-doublet models with BIC selection, and it is calibrated with a grid of simulations spanning SNR, spectral dispersion, and velocity separation. Detection efficiencies from these simulations are interpolated with a Gaussian Process and used to correct observed HVF rates. The authors report 85 HVF spectra, phase-resolved HVF rates of 76% before −11 d, 46% between −11 and −6 d, and 29% in the six days before maximum light, no significant differences in SALT2 x1, peak magnitude, decline rate, host mass, or host colour between HVF and non-HVF objects, and estimates of the impact of HVFs on Wang and Branch classifications.

Significance. If the headline rates are robust, this is an important observational result: it would establish that Si II λ6355 HVFs are common in early SN Ia spectra, fade before maximum light, and are not confined to a particular light-curve or host-galaxy demographic. The study has real strengths: simulation-informed quality cuts, explicit true- and false-positive rates, pull-based uncertainty corrections, consistency checks across instrument pairs, and a Monte Carlo treatment of measurement uncertainties in the corrected distributions. The principal weakness is that the central rate measurements inherit systematic uncertainty from the simulation priors used to build the detection-efficiency surface, and that uncertainty is not propagated into the quoted confidence intervals. The significance of the paper therefore depends on whether that systematic error can be quantified and shown to be modest.

major comments (3)
  1. [§3.2.2, §4.1, §4.4, Fig. 12] The headline rates (76%, 46%, 29%) are obtained by dividing raw HVF counts by a GP detection-efficiency surface constructed from simulated Gaussian doublets whose HVF depths and widths are drawn from a KDE of PTF measurements. However, Fig. 7 shows that the real DR2 HVFs are systematically shallower and narrower than those priors. The authors respond in §4.1 and Fig. 8 by recomputing true-positive rates after removing simulated HVFs with aHV > 0.25 or cHV > 70 Å, but those thresholds are informed by the same observed DR2 HVF sample that the corrected surface is then used to correct. Because the observed HVF sample is itself selection-biased—shallow and narrow features are preferentially missed—conditioning the simulations on the observed parameter range can bias the efficiency correction rather than remove the bias. In addition, the GP interpolation is used as a point estimate, and the Clopper-Pearson intervals in Fig. 12 and Conclusion item 1 include only counting noise, not GP interpolation error, prior mismatch, or the uncertainty in the low-Δv regime where corrections are large (e.g., a true-positive rate of ~25% at SNR=8, dispersion=2 Å/pix, Δv=4000 km/s). I request an explicit systematic-error estimate: recompute the three phase-bin rates using alternative simulation priors (uncut PTF, broad uniform, and non-Gaussian line shapes) and report the full range, and propagate the GP interpolation uncertainty into the final rates.
  2. [§5.2 and Conclusion item 7] The quoted Wang misclassification rate is internally inconsistent. The text in §5.2 reports 26 ±14/11% of HVW classifications (24 ±11/8% for the full sample), while Conclusion item 7 reports 26 ±25/17% (and the full-sample value also differs). Since this is a quantitative claim of the paper, the two sets of values and their uncertainty convention should be harmonized. The same check should be applied to the Branch misclassification percentages in §5.2 versus Conclusion item 8.
  3. [§3.2.1] The noise prescription as written states that the Gaussian noise standard deviation is 'the product of the SNR and the depth of the composite feature.' This inverts the definition of SNR given in §2.2, where the local SNR is the ratio of line depth to continuum standard deviation. If the sentence is taken literally, high-SNR simulations would be noisier than low-SNR ones, which is inconsistent with the behaviour shown in Fig. 3. This is likely a typo (the intended relation is presumably std = depth/SNR), but the method section should be corrected because the simulations are load-bearing for the efficiency corrections.
minor comments (4)
  1. [§4.4] The paragraph beginning 'In order to probe the potential variation of this distribution...' is repeated verbatim; one copy should be removed.
  2. [§5.2] The terms 'upper limit' and 'incorrect classification rate' are used somewhat interchangeably. Since false positives near peak could move classifications in the opposite direction, the authors should state more explicitly which numbers are upper limits and why.
  3. [§2.1] There is a typo, 'pre-maxiumum,' which should be 'pre-maximum.'
  4. [§4.4] The Monte Carlo iterations for the phase and Δv distributions resample measurement uncertainties and false-positive reclassifications, but not the uncertainty in the assumed 2% false-positive rate. A brief sensitivity test with a range of false-positive rates would strengthen the error budget.

Circularity Check

0 steps flagged · score 2.0 of 10

No significant circularity; the central HVF rates are efficiency-corrected observed proportions, with only a minor non-load-bearing self-citation and a transparent simulation-calibration loop.

full rationale

The central claims are corrected observed rates, not derived quantities: 85/329 spectra are classified by MCMC/BIC, and the headline rates (76%, 46%, 29%) are raw counts divided by detection efficiencies measured from injected simulated features. No quoted number reduces algebraically to a fitted constant or to the simulation input. The Section 4.1 adjustment of the simulated HVF population (excluding aHV > 0.25 or cHV > 70 A) is a sensitivity check informed by the DR2 measurements, and it is applied before the same efficiency surface is used to correct the DR2 rates; this creates a calibration loop and a systematic-uncertainty concern, especially at low Delta-v where the true-positive rate is ~25%, but it is not an equation-level circularity or a fitted parameter renamed as a prediction. The only self-citation with overlapping authorship is Maguire et al. (2014), which supplies the external PTF priors for the simulations; it is not load-bearing in the sense of forcing the quoted rates, and the paper explicitly validates and updates those priors against the independent DR2 measurements. The demographic comparisons (x1, c, host mass, local color) are direct KS tests on the classified subsamples and do not depend on the efficiency correction. The GP interpolation uncertainty and the prior-mismatch systematics are real limitations, but they belong to correctness risk rather than to circular derivation.

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

The central claim rests on two measurement pipelines: the MCMC/BIC line fitting and the simulation-based efficiency correction. Neither introduces new physical entities, and there are no theory constants being fit. The main burden is the representativeness of the simulated HVF population, the hand-chosen thresholds (4000 km/s, SNR 8, dispersion 10, 2% false-positive rate), and the assumption that the Si II λ6355 profile is a sum of optically thick Gaussian doublets.

free parameters (5)
  • Detection efficiency correction surface (GP over SNR, dispersion, Δv) = True-positive rates from 0 to 100% over the simulation grid
    The final phase-bin rates divide raw HVF counts by this simulated true-positive rate. It encodes assumptions about the real HVF population and is adjusted post hoc in Section 4.1 to the observed aHV ≤ 0.25, cHV ≤ 70 Å region.
  • Conservative false positive rate = 2%, with the simulation-measured rate at about 0.2%
    Applied in the Monte-Carlo iterations to reclassify low-Δv HVF candidates as non-HVF. It is a hand-inflated constant that modestly lowers the quoted rates and is not derived from the data.
  • Velocity separation cut = 4000 km/s
    Chosen from Figure 3 as the onset of overlap between true- and false-positive rates. It truncates the measured Δv distribution near its peak, so the distribution below 4000 km/s is unmeasured.
  • Photospheric velocity floor for double-component classification = vPV > 9000 km/s
    Two-component fits with vPV below 9000 km/s are reset to single-component, following Silverman et al. (2015), to avoid C II contamination. This cut could discard genuine HVFs with very low photospheric velocities.
  • MCMC prior bounds and slope-prior coefficients = a > 0.05, c > 30 Å; d = (6e-6)r + (4e-5)
    Bounds avoid overfitting in noisy and low-resolution spectra; the slope-prior width formula was calibrated empirically from simulated fits in preliminary testing (Section 3.1.1).
assumptions (5)
  • domain assumption The Si II λ6355 doublet is modeled as two Gaussians with tied velocity, depth, and width (optically thick regime)
    Equation (3) and Section 3.1.1, following Childress et al. (2013). The entire HVF classification rests on this line-profile model.
  • domain assumption Simulated features drawn from PTF-based priors (power-law velocity evolution, KDE widths and depths, Gaussian noise) adequately represent real DR2 spectra for efficiency calibration
    Section 3.2.1. The true-positive-rate surface used to correct all rates is only as valid as this representativeness.
  • standard math BIC with flux uncertainties estimated from continuum scatter is a reliable model selector for this fitting problem
    Section 3.1.2. If the likelihood scale is wrong, the three-parameter penalty can bias model choice; the simulations partially validate this.
  • domain assumption The local pseudo-continuum is linear over the fitting window and its manual selection does not bias component parameters
    Section 3.1.1. Continuum regions are chosen by a gradient method plus manual checks for cosmic rays and host lines.
  • ad hoc to paper Two-component fits with vPV below 9000 km/s indicate C II contamination, not a genuine low-velocity photosphere
    Section 3.1.2. This cut follows Silverman et al. (2015) and could remove real HVFs with very low PV velocities.

how reviews work

0 comments
Cite this review

Pith. "Pith review of ZTF SN Ia DR2: High-velocity components in the Si II $\lambda$6355." pith.science (2026). https://pith.science/paper/D5CNSV6N

@misc{pith2026250204448,
  author       = {Pith},
  title        = {Pith review of: ZTF SN Ia DR2: High-velocity components in the Si II $\lambda$6355},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/D5CNSV6N}},
  note         = {Machine review of arXiv:2502.04448}
}
abstract

The ZTF SN Ia Data Release 2 provides a perfect opportunity to perform a thorough search for, and subsequent analysis of, high-velocity components in the Si II $\lambda$6355 feature in the pre-peak regime. The source of such features remains unclear, with potential origins in circumstellar material or density/abundance enhancements intrinsic to the SN ejecta. Therefore, they may provide clues to the elusive progenitor and explosion scenarios of SNe Ia. We employ a MCMC fitting method followed by BIC testing to classify single and double Si II $\lambda$6355 components in the DR2. The detection efficiency of our classification method is investigated through the fitting of simulated features, allowing us to place cuts upon spectral quality required for reliable classification. These simulations were also used to perform an analysis of the recovered parameter uncertainties and potential biases in the measurements. Within the 329 spectra sample that we investigate, we identify 85 spectra exhibiting Si II $\lambda$6355 HVFs. We find that HVFs decrease in strength with phase relative to their photospheric counterparts - however, this decrease can occur at different phases for different objects. HVFs with larger velocity separations from the photosphere are seen to fade earlier leaving only the double components with smaller separations as we move towards maximum light. Our findings suggest that around three quarters of SN Ia spectra before -11 d show high-velocity components in the Si II $\lambda$6355 with this dropping to around one third in the six days before maximum light. We observe no difference between the populations of SNe Ia that do and do not form Si II $\lambda$6355 HVFs in terms of SALT2 light-curve parameter x1, peak magnitude, decline rate, host mass, or host colour, supporting the idea that these features are ubiquitous across the SN Ia population.

Figures

Figures reproduced from arXiv: 2502.04448 by the authors.

Figure 1
Figure 1. The distributions of resolution and SNR in the 1930spectra be￾fore peak, covering the relevant wavelength region with a clean enough signal for the SNR estimation. The bottom panel represents the red￾shift distribution of the 1557objects sampled by these spectra. The dot￾ted lines and accompanying values correspond to the medians of the three measurements. Final cuts upon SNR and resolution will be imple￾mented base… view at source ↗
Figure 2
Figure 2. The PTF (Maguire et al. 2014) SN Ia spectral measurements for velocity, width and depth in the case of the Si ii λ6355 absorption feature. The power law fit to the velocity evolution and the Gaussian KDE (described by the contours) for the width and depth are used to inform the generation of the synthetic features in the simulations. linear fit to the continuum regions provided a smaller residual with the true value… view at source ↗
Figure 3
Figure 3. The true (circles) and false (arrows) positive rates of the MCMC/BIC classification method as a function of the velocity separations derived from the simulations for increasing SNRs from the left to the right panel and different spectral dispersions shown as different colours. 1D slices (in ∆v space) of the 3D GP interpolation of SNR, dispersion, and velocity separation are presented as the coloured lines and associ… view at source ↗
Figures from the paper (11 more)
Figure 4
Figure 4. Figure 4: The distribution of the pulls (residual/uncertainty) for the six feature parameters in the case of spectra with a SNR of 15, a dispersion of 5 Å/pix and a velocity separation of 5000 km s−1 . The solid black lines indicate the desired zero pull value, while the solid c…
Figure 5
Figure 5. Figure 5: The standard deviations of the pull distributions about their means for each of the simulations. The three SNRs are represented by different colours as indicated above the top left panel. The thick solid lines describe the linear regression fits to the datapoints with …
Figure 6
Figure 6. Figure 6: The means of the pull distributions for each of the simulations after applying the uncertainty corrections. The formatting matches that of [PITH_FULL_IMAGE:figures/full_fig_p009_6.png]
Figure 8
Figure 8. Figure 8: The residuals between the true positive rates taken from sim￾ulated spectra with aHV ≤ 0.25 or cHV ≤ 70 Å, and the original true positive rates as a function of velocity separation for the three SNR, shown with increasing SNR from left to right panels. tions. To evalua…
Figure 7
Figure 7. Figure 7: The evolution of the velocities of the PV and HV Si ii λ6355 components for all 329 spectra in the sample (top panel). The hollow points correspond to the 244 PV components with no HV counterpart. The solid blue line is fit to all the PV components, with the dash-dotte…
Figure 10
Figure 10. Figure 10: Top: KS p-values between our base sample and the control sample for six parameters. The solid black line describes the threshold below which a parameter should be considered to show a bias when compared to the ‘low-bias’ control sample. The dotted black line indi￾cate…
Figure 11
Figure 11. Figure 11: Velocity separation distribution of the 64 HVF spectra from our low-bias sample of 210 SNe. The black curve presents the median spectrum density after correcting for the detection efficiency of our clas￾sification method. The individual orange curves correspond to 100…
Figure 12
Figure 12. Figure 12: Phase histogram for the 64 HVF spectra compared to the full 210 low-bias spectral sample. The solid black curve presents the median HVF spectrum density after correcting for the detection efficiency of our classification method, with the dotted black curve as the dens…
Figure 15
Figure 15. Figure 15: The ratio of the pseudo-equivalent widths of the HV and PV components against the photospheric velocity of the Si ii λ6355 feature for the low-bias sample of 190 SNe. For objects with multiple spectra we take the mean value in both dimensions, treating the uncertainti…
Figure 16
Figure 16. Figure 16: Comparison of the photospheric velocity (top) and pEW (bot￾tom) measured using the single component fits against taking into con￾sideration the HV components and using the two component fits wher￾ever a HV component was identified. Solid datapoints represent the low￾b…
Figure 17
Figure 17. Figure 17: Comparison of the measured distributions of PV and HV com￾ponents in our sample for spectra identified as having both (top) against the distributions of silicon from a number of theoretical explosion mod￾els from the HESMA archive (Kromer et al. 2017). Each of the thr…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Optical observations on the young Type Ia SN 2021fxy with detached high velocity features

    astro-ph.HE 2026-07 conditional novelty 5.0 of 10

    In SN 2021fxy, the high-velocity Si II λ6355 absorption declines as roughly t^−0.1, much shallower than the t^−0.22 expected from standard outer ejecta, pointing to detached density structures.

Reference graph

Works this paper leans on

91 extracted references · 54 canonical work pages · cited by 1 Pith paper

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archiveprefix author booktitle chapter edition editor howpublished institution eprint journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts #0 'before.all := #1 ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in " " * FUNCTION format....

  3. [3]

    2002, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol

    Aldering , G., Adam , G., Antilogus , P., et al. 2002, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 4836, Survey and Other Telescope Technologies and Discoveries, ed. J. A. Tyson & S. Wolff , 61--72

    2002

  4. [4]

    2002, , 114, 892

    Allington-Smith , J., Murray , G., Content , R., et al. 2002, , 114, 892

    2002

  5. [5]

    2012, in Astronomical Society of the Pacific Conference Series, Vol

    Barnsley , R., Smith , R., & Steele , I. 2012, in Astronomical Society of the Pacific Conference Series, Vol. 461, Astronomical Data Analysis Software and Systems XXI, ed. P. Ballester , D. Egret , & N. P. F. Lorente , 517

    2012

  6. [6]

    C., Kulkarni , S

    Bellm , E. C., Kulkarni , S. R., Graham , M. J., et al. 2019, , 131, 018002

    2019

  7. [7]

    Bellm , E. C. & Sesar , B. 2016, pyraf-dbsp: Reduction pipeline for the Palomar Double Beam Spectrograph , Astrophysics Source Code Library, record ascl:1602.002

    2016

  8. [8]

    A., et al

    Benetti , S., Cappellaro , E., Mazzali , P. A., et al. 2005, , 623, 1011

    2005

Show all 91 references
  1. [9]

    2008, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol

    Benn , C., Dee , K., & Ag \'o cs , T. 2008, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 7014, Ground-based and Airborne Instrumentation for Astronomy II, ed. I. S. McLean & M. M. Casali , 70146X

    2008

  2. [10]

    D., Walters , R., et al

    Blagorodnova , N., Neill , J. D., Walters , R., et al. 2018, , 130, 035003

    2018

  3. [11]

    S., & Kirshner , R

    Blondin , S., Mandel , K. S., & Kirshner , R. P. 2011, , 526, A81

    2011

  4. [12]

    2009, , 121, 238

    Branch , D., Chau Dang , L., & Baron , E. 2009, , 121, 238

    2009

  5. [13]

    C., Hall , N., et al

    Branch , D., Dang , L. C., Hall , N., et al. 2006, , 118, 560

    2006

  6. [14]

    M., Baliber , N., Bianco , F

    Brown , T. M., Baliber , N., Bianco , F. B., et al. 2013, , 125, 1031

    2013

  7. [15]

    Buckley , D. A. H., Swart , G. P., & Meiring , J. G. 2006, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 6267, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, ed. L. M. Stepp , 62670Z

    2006

  8. [16]

    2024, arXiv e-prints, arXiv:2407.06828

    Burgaz , U., Maguire , K., Dimitriadis , G., et al. 2024, arXiv e-prints, arXiv:2407.06828

    2024 arXiv

  9. [17]

    1984, The Messenger, 38, 9

    Buzzoni , B., Delabre , B., Dekker , H., et al. 1984, The Messenger, 38, 9

    1984

  10. [18]

    B., Fox , D

    Cenko , S. B., Fox , D. B., Moon , D.-S., et al. 2006, , 118, 1396

    2006

  11. [19]

    G., et al

    Cepa , J., Aguiar , M., Escalera , V. G., et al. 2000, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 4008, Optical and IR Telescope Instrumentation and Detectors, ed. M. Iye & A. F. Moorwood , 623--631

    2000

  12. [20]

    J., Filippenko , A

    Childress , M. J., Filippenko , A. V., Ganeshalingam , M., & Schmidt , B. P. 2014, , 437, 338

    2014

  13. [21]

    J., Scalzo , R

    Childress , M. J., Scalzo , R. A., Sim , S. A., et al. 2013, , 770, 29

    2013

  14. [22]

    C., Crain , J

    Clemens , J. C., Crain , J. A., & Anderson , R. 2004, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 5492, Ground-based Instrumentation for Astronomy, ed. A. F. M. Moorwood & M. Iye , 331--340

    2004

  15. [23]

    M., Still , M., Schellart , P., et al

    Crawford , S. M., Still , M., Schellart , P., et al. 2010, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 7737, Observatory Operations: Strategies, Processes, and Systems III, ed. D. R. Silva , A. B. Peck , & B. T. Soifer , 773725

    2010

  16. [24]

    K., Kasliwal , M

    Das , K. K., Kasliwal , M. M., Fremling , C., et al. 2023, , 959, 12

    2023

  17. [25]

    M., Riddle , R., et al

    Dekany , R., Smith , R. M., Riddle , R., et al. 2020, , 132, 038001

    2020

  18. [26]

    2024, arXiv e-prints, arXiv:2409.04200

    Dimitriadis , G., Burgaz , U., Deckers , M., et al. 2024, arXiv e-prints, arXiv:2409.04200

    2024 arXiv

  19. [27]

    J., Arendse , N., et al

    Dimitriadis , G., Foley , R. J., Arendse , N., et al. 2022, , 927, 78

    2022

  20. [28]

    2011, , 123, 288

    Dressler , A., Bigelow , B., Hare , T., et al. 2011, , 123, 288

    2011

  21. [29]

    R., et al

    Fink , M., Kromer , M., Seitenzahl , I. R., et al. 2014, , 438, 1762

    2014

  22. [30]

    W., Lang , D., & Goodman , J

    Foreman-Mackey , D., Hogg , D. W., Lang , D., & Goodman , J. 2013, , 125, 306

    2013

  23. [31]

    L., H \"o flich , P., Fesen , R

    Gerardy , C. L., H \"o flich , P., Fesen , R. A., et al. 2004, , 607, 391

    2004

  24. [32]

    2022, Asymmetric Uncertainty: Handling nonstandard numerical uncertainties , Astrophysics Source Code Library, record ascl:2208.005

    Gobat , C. 2022, Asymmetric Uncertainty: Handling nonstandard numerical uncertainties , Astrophysics Source Code Library, record ascl:2208.005

    2022

  25. [33]

    J., Kulkarni , S

    Graham , M. J., Kulkarni , S. R., Bellm , E. C., et al. 2019, , 131, 078001

    2019

  26. [34]

    T., et al

    Gronow , S., Collins , C., Ohlmann , S. T., et al. 2020, , 635, A169

    2020

  27. [35]

    E., Sim , S

    Gronow , S., Collins , C. E., Sim , S. A., & R \"o pke , F. K. 2021, , 649, A155

    2021

  28. [36]

    2007, , 466, 11

    Guy , J., Astier , P., Baumont , S., et al. 2007, , 466, 11

    2007

  29. [37]

    I., et al

    Hamuy , M., Folatelli , G., Morrell , N. I., et al. 2006, , 118, 2

    2006

  30. [38]

    Hatano , K., Branch , D., Fisher , A., Baron , E., & Filippenko , A. V. 1999, , 525, 881

    1999

  31. [39]

    M., J rgensen , I., Allington-Smith , J

    Hook , I. M., J rgensen , I., Allington-Smith , J. R., et al. 2004, , 116, 425

    2004

  32. [40]

    2017, , 842, 29

    Hung , T., Gezari , S., Blagorodnova , N., et al. 2017, , 842, 29

    2017

  33. [41]

    2003, , 593, 788

    Kasen , D., Nugent , P., Wang , L., et al. 2003, , 593, 788

    2003

  34. [42]

    M., Cannella , C., Bagdasaryan , A., et al

    Kasliwal , M. M., Cannella , C., Bagdasaryan , A., et al. 2019, , 131, 038003

    2019

  35. [43]

    L., Rigault , M., Neill , J

    Kim , Y. L., Rigault , M., Neill , J. D., et al. 2022, , 134, 024505

    2022

  36. [44]

    A., Nordsieck , K

    Kobulnicky , H. A., Nordsieck , K. H., Burgh , E. B., et al. 2003, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 4841, Instrument Design and Performance for Optical/Infrared Ground-based Telescopes, ed. M. Iye & A. F. M. Moorwood , 1634--1644

    2003

  37. [45]

    S., Shappee , B

    Kochanek , C. S., Shappee , B. J., Stanek , K. Z., et al. 2017, , 129, 104502

    2017

  38. [46]

    Kromer , M., Ohlmann , S., & R \"o pke , F. K. 2017, , 88, 312

    2017

  39. [47]

    T., Pakmor , R., et al

    Kromer , M., Ohlmann , S. T., Pakmor , R., et al. 2015, , 450, 3045

    2015

  40. [48]

    Lezmy , J., Copin , Y., Rigault , M., Smith , M., & Neill , J. D. 2022, , 668, A43

    2022

  41. [49]

    C., et al

    Maguire , K., Sullivan , M., Pan , Y. C., et al. 2014, , 444, 3258

    2014

  42. [50]

    H., Vinko , J., Wheeler , J

    Marion , G. H., Vinko , J., Wheeler , J. C., et al. 2013, , 777, 40

    2013

  43. [51]

    J., Laher , R

    Masci , F. J., Laher , R. R., Rusholme , B., et al. 2019, , 131, 018003

    2019

  44. [52]

    A., Benetti , S., Altavilla , G., et al

    Mazzali , P. A., Benetti , S., Altavilla , G., et al. 2005 a , , 623, L37

    2005

  45. [53]

    A., Benetti , S., Stehle , M., et al

    Mazzali , P. A., Benetti , S., Stehle , M., et al. 2005 b , , 357, 200

    2005

  46. [54]

    K., Cohen , J

    McCarthy , J. K., Cohen , J. G., Butcher , B., et al. 1998, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 3355, Optical Astronomical Instrumentation, ed. S. D'Odorico , 81--92

    1998

  47. [55]

    Miller , J. S. & Stone , R. P. S. 1993, in , Santa Cruz, CA, Lick Observatory

    1993

  48. [56]

    2011, , 526, A119

    Nordin , J., \"O stman , L., Goobar , A., et al. 2011, , 526, A119

    2011

  49. [57]

    T., Kromer , M., Fink , M., et al

    Ohlmann , S. T., Kromer , M., Fink , M., et al. 2014, , 572, A57

    2014

  50. [58]

    B., Cohen , J

    Oke , J. B., Cohen , J. G., Carr , M., et al. 1995, , 107, 375

    1995

  51. [59]

    C., Yang , Y., Brink , T

    Patra , K. C., Yang , Y., Brink , T. G., et al. 2022, , 509, 4058

    2022

  52. [60]

    2011, Journal of Machine Learning Research, 12, 2825

    Pedregosa, F., Varoquaux, G., Gramfort, A., et al. 2011, Journal of Machine Learning Research, 12, 2825

    2011

  53. [61]

    Perley , D. A. 2019, , 131, 084503

    2019

  54. [62]

    1999, , 517, 565

    Perlmutter , S., Aldering , G., Goldhaber , G., et al. 1999, , 517, 565

    1999

  55. [63]

    Phillips , M. M. 1993, , 413, L105

    1993

  56. [64]

    S., Steele , I

    Piascik , A. S., Steele , I. A., Bates , S. D., et al. 2014, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 9147, Ground-based and Airborne Instrumentation for Astronomy V, ed. S. K. Ramsay , I. S. McLean , & H. Takami , 91478H

    2014

  57. [65]

    J., Maguire , K., Smartt , S

    Prentice , S. J., Maguire , K., Smartt , S. J., et al. 2018, , 865, L3

    2018

  58. [66]

    2020, The Journal of Open Source Software, 5, 2308

    Prochaska , J., Hennawi , J., Westfall , K., et al. 2020, The Journal of Open Source Software, 5, 2308

    2020

  59. [67]

    Pskovskii , I. P. 1977, , 21, 675

    1977

  60. [68]

    J., et al

    Quimby , R., H \"o flich , P., Kannappan , S. J., et al. 2006, , 636, 400

    2006

  61. [69]

    G., Filippenko , A

    Riess , A. G., Filippenko , A. V., Challis , P., et al. 1998, , 116, 1009

    1998

  62. [70]

    D., Blagorodnova , N., et al

    Rigault , M., Neill , J. D., Blagorodnova , N., et al. 2019, , 627, A115

    2019

  63. [71]

    2024, arXiv e-prints, arXiv:2409.04346

    Rigault , M., Smith , M., Goobar , A., et al. 2024, arXiv e-prints, arXiv:2409.04346

    2024 arXiv

  64. [72]

    2010, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol

    Rockosi , C., Stover , R., Kibrick , R., et al. 2010, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 7735, Ground-based and Airborne Instrumentation for Astronomy III, ed. I. S. McLean , S. K. Ramsay , & H. Takami , 77350R

    2010

  65. [73]

    K., Kromer , M., Seitenzahl , I

    R \"o pke , F. K., Kromer , M., Seitenzahl , I. R., et al. 2012, , 750, L19

    2012

  66. [74]

    R., Ciaraldi-Schoolmann , F., R \"o pke , F

    Seitenzahl , I. R., Ciaraldi-Schoolmann , F., R \"o pke , F. K., et al. 2013, , 429, 1156

    2013

  67. [75]

    2023, , 948, 52

    Sharma , Y., Sollerman , J., Fremling , C., et al. 2023, , 948, 52

    2023

  68. [76]

    M., Kong , J

    Silverman , J. M., Kong , J. J., & Filippenko , A. V. 2012, , 425, 1819

    2012

  69. [77]

    M., Vink \'o , J., Marion , G

    Silverman , J. M., Vink \'o , J., Marion , G. H., et al. 2015, , 451, 1973

    2015

  70. [78]

    A., Fink , M., Kromer , M., et al

    Sim , S. A., Fink , M., Kromer , M., et al. 2012, , 420, 3003

    2012

  71. [79]

    J., Valenti , S., Fraser , M., et al

    Smartt , S. J., Valenti , S., Fraser , M., et al. 2015, , 579, A40

    2015

  72. [80]

    W., Smartt , S

    Smith , K. W., Smartt , S. J., Young , D. R., et al. 2020, , 132, 085002

    2020

  73. [81]

    A., Smith , R

    Steele , I. A., Smith , R. J., Rees , P. C., et al. 2004, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 5489, Ground-based Telescopes, ed. J. Oschmann , Jacobus M., 679--692

    2004

  74. [82]

    B., Bean , J

    Stevenson , K. B., Bean , J. L., Seifahrt , A., et al. 2016, , 817, 141

    2016

  75. [83]

    A., Maeda , K., & Nomoto , K

    Tanaka , M., Mazzali , P. A., Maeda , K., & Nomoto , K. 2006, , 645, 470

    2006

  76. [84]

    2017, in Handbook of Supernovae, ed

    Taubenberger , S. 2017, in Handbook of Supernovae, ed. A. W. Alsabti & P. Murdin , 317

    2017

  77. [85]

    C., Branch , D., Baron , E., et al

    Thomas , R. C., Branch , D., Baron , E., et al. 2004, , 601, 1019

    2004

  78. [86]

    L., Denneau , L., Heinze , A

    Tonry , J. L., Denneau , L., Heinze , A. N., et al. 2018, , 130, 064505

    2018

  79. [87]

    A., Shappee , B

    Tucker , M. A., Shappee , B. J., Huber , M. E., et al. 2022, , 134, 124502

    2022

  80. [88]

    2014, , 438, L101

    Valenti , S., Sand , D., Pastorello , A., et al. 2014, , 438, L101

    2014

  81. [89]

    2003, , 591, 1110

    Wang , L., Baade , D., H \"o flich , P., et al. 2003, , 591, 1110

    2003

  82. [90]

    V., Ganeshalingam , M., et al

    Wang , X., Filippenko , A. V., Ganeshalingam , M., et al. 2009, , 699, L139

    2009

  83. [91]

    M., et al

    Yarbrough , Z., Baron , E., DerKacy , J. M., et al. 2023, , 521, 3873

    2023

Pith tools

Reviewed August 8, 2026 · model on record in the stance chip above.