AMPEL workflows for LSST: Modular and reproducible real-time photometric classification

Jakob Nordin; Jakob van Santen; Marek Kowalski; Simeon Reusch; Valery Brinnel

REVIEW 3 major objections 5 minor 1 cited by

AMPEL workflows for LSST: Modular and reproducible real-time photometric classification

T0 review · 3 major / 5 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read The paper presents three modular AMPEL channels that classify simulated LSST alerts in real time, with SNGuess tagging about 99% of young supernovae and FinalBet correctly classifying more than 80% of extragalactic transients.

desk verdict Solid, reproducible engineering; the abstract's headline numbers overstate what the paper actually measures, so the claims need rescaling before the paper is accepted. read the letter →

arxiv 2501.16511 v1 pith:RXJD6FLL submitted 2025-01-27 astro-ph.IM astro-ph.CO

Jakob Nordin , Valery Brinnel , Jakob van Santen , Simeon Reusch , Marek Kowalski This is my paper · ORCID

classification astro-ph.IMastro-ph.CO

keywords AMPELreal-timeclassificationLSSTalertstreamsupernovaphotometricELAsTiCCXGBoostParSNIP

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

The paper argues that the practical bottleneck for real-time astronomy in the LSST era is not any single machine-learning classifier but the surrounding workflow: how alerts are filtered, models combined, priors applied, and results traced. It presents three public AMPEL channels built to fill that role. SNGuess uses gradient-boosted decision trees on early-detection features to flag young extragalactic transients, FollowMe selects an uncertain, label-worthy subset for spectroscopic follow-up, and FinalBet reweights FollowMe's deep-learning output with redshift, rate, and host-color priors. On the ELAsTiCC v1 simulated alert stream, the paper reports that SNGuess retains about 99% of young supernovae and FinalBet reaches successful classifications for more than 80% of extragalactic transients, with the full jobfiles released for reproduction.

What carries the argument

The central machinery is the AMPEL tiered unit system, through which alerts flow from T0 filtering, T1 state assembly, T2 per-transient analysis, and T3 sample-level reaction, with every state, configuration, and output tied to a provenance record. The classification itself is carried by T2RiseDeclineStat, which extracts up to 32 early-detection features for SNGuess's XGBoost trees, and by T2RunParsnip, which fits ParSNIP, a hybrid generative model combining explicit physical variables such as redshift and peak time with latent variables learned by a variational autoencoder, at discrete sampled redshifts. FinalBet's T2ElasticcReport then multiplies the ParSNIP probabilities by priors built from training-sample redshift distributions, relative rates from a bright-transient survey, and host-galaxy color fits. The jobfile encodes the whole chain, so any user with an AMPEL environment can rerun it.

What would settle it

Run the public jobfiles on the blinded alert stream used for the challenge, or later on real LSST alerts, and compare per-class true-classification fractions with the paper's roughly 99% young-supernova retention and more than 80% extragalactic success; the paper's own blinded appendix already indicates the fractions fall to roughly 50 to 70% for FinalBet, which would falsify the headline figures as a statement about deployed performance. Alternatively, feed identical simulated lightcurves through the two alert-generation tools and check whether the resulting alert streams differ systematically.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that a code-to-data framework can host complete real-time classification programs for the LSST alert stream, and that the three supplied channels perform well on the ELAsTiCC v1 simulation. SNGuess, based on features designed for ZTF alerts and trained with XGBoost, correctly tags roughly 99% of young supernovae among alerts with few detections. FollowMe demonstrates that the ambiguous middle of a ParSNIP-based SNIa/SNIbc classification can be sampled to build a labeled follow-up set whose host-galaxy colors match the parent population. FinalBet adds explicit priors, including redshift distribution, relative rates, and host color, and achieves AUC scores above 0.9 for most classes, with trustworthy classifications correct well above 80% for most transient types.

Load-bearing premise

The headline performance numbers are measured on a held-out test sample explicitly built from the same relative class, redshift, and rate distributions as the training data, and through an alert-generation path that the paper says was never checked against the official blind-test tools; if either of those differences matters, the reported percentages will not transfer to the real LSST stream.

Editorial extensions

If this is right

If the reported performance holds, groups can take the public jobfiles and adapt them to their own VRO science programs without rebuilding an alert-processing infrastructure.
The SNGuess channel, combined with external multi-messenger constraints, can provide a pure stream of young-transient and counterpart candidates in real time.
FinalBet demonstrates that adding explicit population priors improves classification accuracy over the raw model output, at the cost of a biased selection that must be tracked.
Because AMPEL records provenance for each classification, follow-up decisions made in real time can later be linked to final, reprocessed classifications for sample correction.
The same workflows can be rerun at scale at a computer center or on the live AMPEL broker channel, making simulation-era development translate directly to the LSST stream.

Reading between the lines

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

The paper's own blinded test results show much lower true-classification fractions, roughly 50 to 70% for FinalBet; if that gap persists on real LSST data, the headline 99% and >80% figures should be read as matched-distribution upper bounds rather than deployed expectations.
A direct side-by-side test of the two alert-generation paths used here would isolate whether the performance drop comes from the priors or from unrecognized format differences between the internal loader and the official LSST tools.
The placeholder machinery described, including classifier combination, host information, and non-Gaussian redshift sampling, suggests the architecture itself may be the lasting contribution, allowing newer classifiers to be plugged in and compared fairly within the same jobfile.
One testable extension is to rerun SNGuess with the updated unit that handles negative-flux detections, which should reduce the AGN and microlensing contamination visible in the blinded confusion matrix.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

You should know two things about this paper. First, the engineering is real and genuinely useful: three working AMPEL channels for LSST-class alert streams, with public job files, provenance tracking, and a local reproduction path. Second, the abstract's headline percentages are not supported by the evidence in the paper. The FinalBet '>80% of all extragalactic transients' claim is actually precision on a confident subset: with a probability threshold of 0.68 only 67% of transients receive any classification, and the correct-class rate for those is 'typically above 80%'. That works out to roughly half of all transients correctly classified, not 80%. The DESC blind test in Appendix B shows true classification fractions around 50–70%, consistent with that lower bound. The SNGuess 99% figure is measured on a held-out test that shares the training distribution, and the internal alert loader was never checked against the LSST-tools stream used for the blind test. The priors in FinalBet are calibrated on the same training sample, so their advertised benefit is partly an artifact of the evaluation setup.

That said, the paper earns credit where it matters. The authors are unusually honest: the body text explicitly says the priors are suboptimal, that the loader outputs were not compared, and that blind-test performance is worse. The appendices include the full blind-test confusion matrices. The workflows themselves are modular, documented, and reproducible—an actual contribution to the time-domain community as LSST comes online. The redshift-sampling unit and negative-flux handling are small but real pieces of new engineering. Citation practice is fair: the ML engines and AMPEL itself are properly credited to prior work.

The soft spots are, in proportion: the abstract overstates performance (load-bearing for the paper's claims, but fixable by relabeling what is measured); the internal test is distribution-matched so headline numbers should carry that caveat; code versions are pinned by branch rather than commit hash; and no error bars or sample sizes accompany the headline percentages. None of this undermines the infrastructure, but it does mean the advertised performance should not be taken at face value.

Who benefits: anyone building broker pipelines for LSST, or groups wanting a reference implementation for real-time photometric classification. It deserves a serious referee. My recommendation: send it to peer review, but require the authors to either rescale the abstract to the confident-subset definition or report the blind-test numbers as the headline, and to state explicitly that the internal test is distribution-matched.

Referee Report

3 major / 5 minor

Summary. The paper describes three AMPEL-based processing channels designed for real-time classification of LSST alert streams, using the ELAsTiCC v1 simulated dataset as a testbed. SNGuess is a gradient-boosted decision tree filter for identifying young extragalactic transients; FollowMe uses ParSNIP to select an unbiased, observationally accessible subset of Ia/Ibc supernovae for spectroscopic follow-up; FinalBet adds ad hoc priors based on redshift, host-galaxy color, and expected rates to produce final classifications. The authors report SNGuess retaining 99% of young supernovae, FollowMe producing a representative uncertain-class subset, and FinalBet achieving AUC > 0.9 for most classes with correct-class rates above 80% on the classified subset. They also present results from the blind DESC ELAsTiCC evaluation, which show somewhat lower performance. The paper emphasizes reproducibility through public jobfiles, provenance tracking, and a local execution workflow.

Significance. If the reported performance is correctly stated, the paper provides a valuable, fully public, modular framework for real-time transient classification, with concrete implementations that other groups can adapt. Strengths include the explicit provenance system, the release of jobfiles and code, the use of a realistic simulated alert stream, and the careful description of the three distinct science-use cases. The internal held-out evaluation is clearly presented with confusion matrices and ROC curves. However, the headline quantitative claims in the abstract are not supported by the metrics actually defined in Section 5.3 and Appendix B, and the main-text evaluation does not adequately separate the distribution-matched internal test from the blind stream results. The paper's central value is the workflow infrastructure and the reproducible demonstration, not the specific performance numbers; those numbers need to be qualified and corrected before the paper can be accepted.

major comments (3)

[Abstract; Section 5.3, Figs. 10-11] The abstract claims FinalBet 'achieves successful classifications for ≳ 80% of all extragalactic transients', but the evaluation in Section 5.3 defines a different quantity: with a probability threshold of 0.68, only 67% of all transients receive a classification at all, and the correct-class rate for that subset is 'typically above 80%'. The implied overall fraction of correctly classified transients is therefore at most about 0.67 × 0.80 ≈ 54%, not >80%, and the actual fraction depends on the per-class distribution. The DESC blind test (Fig. B.3) gives true classification fractions of roughly 50-70%, consistent with this lower bound. The abstract and Section 5.3 must be rephrased to distinguish 'precision on the 67% classified subset' from 'fraction of all extragalactic transients that are correctly classified', and the headline percentages should be revised accordingly.
[Section 5; Section 5.4] The internal evaluation is explicitly distribution-matched: the test data are described as 'based on the same relative distributions' as the training sample, and Section 5.4 states that internal alert generation used ElasticcTrainingSetLoader while the blind test used LSST tools, adding 'No tests were made to verify whether the output is similar'. Consequently, the claims 'SNGuess correctly tags 99% of all young supernovae' and 'AUC > 0.9 for most FinalBet classes' are established only for the matched internal test set, not for the actual ELAsTiCC alert stream or for future LSST data. The authors should either clearly qualify these claims throughout the abstract and conclusions as applying to the distribution-matched internal test, or they should perform and report a comparison of the two alert-generation paths.
[Section 5.4; Appendix B] Section 5.4 acknowledges that the FinalBet priors 'certainly do not match the underlying class distribution that was simulated for ELAsTiCC', and Appendix B shows that the priors improve common classes (e.g., SNIa) at the expense of rare classes (e.g., SNIax, CART, with true classification fractions near 30%). The paper should quantify the effect of the priors more directly, for example by comparing the FollowMe and FinalBet confusion matrices in the blind test (Figs. B.2 and B.3), and should soften the conclusion that FinalBet 'includes priors to achieve successful classifications' without stating that the priors are semi-arbitrary and can reduce performance for rare classes. The current presentation overstates the benefit of the priors.

minor comments (5)

[Section 2.1] There are several typos in this section: 'unqiue' should be 'unique', 'algorthmic' should be 'algorithmic', and 'specfiying' should be 'specifying'.
[Section 4.3; Appendix A] The text contains typos such as 'lableed' (should be 'labeled') and 'chocen' (should be 'chosen'); in Appendix A, 'smallestsnsep/sqradius' should read 'smallest snsep/sqradius'.
[Section 5.2] The KS test p-value of 0.22 is quoted without stating the sample sizes or the exact test used; adding this information would make the claim more verifiable.
[Listing 1] The code block contains a line break artifact ('Ampel-HU-astro.git,→') that should be cleaned up for the published version.
[Appendix B, Fig. B.3 caption] The blind-test caption states that true classification fractions 'generally vary between 50 and 70%'; since this is the most realistic LSST-like evaluation available, this figure should also be cited in the main text when discussing FinalBet performance.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper's performance claims rest on independent holdout and blind-test evaluations, and the self-citations are not load-bearing.

full rationale

The paper's central quantitative claims are based on standard held-out evaluation from the ELAsTiCC training batches (Section 5) and on the externally administered DESC blind test (Appendix B). The FinalBet priors are fit to the training sample (redshift prior derived from ELAsTiCC training batches, host u-g color prior from a normal fit to the training sample), and the internal test set is 'distinct from the training sample, but based on the same relative distributions' (Section 5). This is a conventional train/test split, not a construction in which the evaluation metric is the fitted quantity: the ParSNIP lightcurve fits and XGBoost features still determine the classifications, and the priors only reweight them. The paper explicitly acknowledges the priors are suboptimal for the blind test ('these were semi-arbitrarily defined, and certainly do not match the underlying class distribution'), and the blind test indeed shows lower performance (about 50-70% true classification fractions), which would not happen if the reported performance were forced by construction. The abstract's phrasing that FinalBet 'achieves successful classifications for >~80% of all extragalactic transients' overstates what is measured (Figure 10 shows a probability threshold of 0.68 is met for only 67% of transients, with correct-class rates typically above 80% within that subset), and the SNGuess '99% of all young supernovae' likewise omits the <=5-detection restriction used in the evaluation. These are reporting/completeness concerns, not circularity: the reported percentage is not defined in terms of the input in a way that makes the outcome tautological. Self-citations to Nordin et al. (2019) for the AMPEL framework and Miranda et al. (2022) for the SNGuess feature set are normal methodological references; the new content here is the workflow implementations, the ELAsTiCC evaluation, and the public jobfiles, which are independently assessable. No load-bearing argument reduces to a self-citation, and no 'uniqueness' or ansatz is imported from the authors' prior work to force the conclusion. The paper is self-contained against an external benchmark (the DESC blind test), so the circularity score is 0.

Assumptions & free parameters 7 free parameters · 6 assumptions · 0 invented entities

The paper's contribution is workflow assembly and evaluation, not derivation. The load-bearing inputs are: (a) the ELAsTiCC v1 simulation and its labeled training data, (b) off-the-shelf ML models trained on those data, (c) several priors and thresholds calibrated on the same training sample, and (d) a test set sharing the training distributions. The FinalBet prior benefit is therefore partly an artifact of calibration and evaluation distribution matching, exposed when the DESC blind test shows lower performance. No invented physical entities are introduced; the seven fitted or hand-chosen parameter groups above define the space in which the central performance claims live.

free parameters (7)

FinalBet redshift prior = per-class redshift histograms with 0.1-width bins, normalized and summed
Calibrated on ELAsTiCC training batches 1-20 (Section 4.1); the paper itself calls this prior 'clearly suboptimal' relative to the true test distribution.
FinalBet rate prior = ZTF BTS class rates with a 0.01 floor for all other classes
Taken from the ZTF Bright Transient Survey (Section 4.1); the paper acknowledges it does not match the ELAsTiCC simulated rates.
FinalBet host u-g color prior = per-class Gaussian mean and std of (u-g), Table 2
Fitted to the ELAsTiCC training sample and applied as a Gaussian reweighting factor (Section 4.1).
Negative-flux classification thresholds = z split at 0.001; 20% negative-fraction split
Set by visual inspection of training alerts during initial training (Section 4.3, Appendix A); described as 'fixed, semi-arbitrary limits'.
Redshift sampling scheme = 3 samples at chosen quantile means with weights 0.2/0.6/0.2; no-host grid {0.01,0.2,0.5,0.8} with weights…
Weights and quantiles are 'arbitrarily chosen' (Appendix A, T2ElasticcRedshiftSampler); affects ParSNIP predictions for each alert.
SNGuess evaluation detection cut = alerts with <= 5 detections
The SNGuess evaluation is restricted to alerts with at most 5 detections (Section 5.1); the 99% retention figure is defined on this subset.
Trained ML model weights = XGBoost and ParSNIP+lightgbm models trained on batches 1-20 with 10x augmentation
Off-the-shelf algorithms (Miranda et al. 2022; Boone 2021); hyperparameters grid-searched per Section 4.2 but not reported; model files referenced in Appendix D (e.g., model1-30pct-sn+2ulens+dwarfs-mw.h5).

assumptions (6)

domain assumption ELAsTiCC v1 simulation faithfully represents future LSST alerts (noise profile, cadence, alert content)
The entire evaluation rests on this simulation (Section 3); there is no real LSST stream yet to confirm transfer.
domain assumption Held-out test batches are representative because they share the training relative distributions
Section 5 states test data is 'based on the same relative distributions'; the DESC blind test, which does not share them, yields lower performance, making this assumption load-bearing.
domain assumption Host galaxy associations and photometric redshift quantiles in alerts are usable for redshift sampling
T2ElasticcRedshiftSampler relies on the closest-host heuristic snsep/sqradius and on photo-z quantiles (Appendix A); the paper notes host association can be spurious and photo-z catastrophically wrong (Section 3).
domain assumption Transient class correlates with host (u-g) color as in the training sample
The host color prior (Table 2, Section 4.1) assumes these correlations persist; the paper flags the risk of biased selection functions (Section 3).
standard math Standard ML library implementations (XGBoost, lightgbm, torch/ParSNIP) perform as documented
Models are executed as library code (Section 4.2); no independent verification is provided.
ad hoc to paper Rule-based labeling of negative-flux alerts is valid for the real alert stream
AGN/microlens/eclipsing-binary labels for negative detections use thresholds chosen from training data (Section 4.3); the paper says these were later removed in updated classifiers.

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Cite this review

Pith. "Pith review of AMPEL workflows for LSST: Modular and reproducible real-time photometric classification." pith.science (2026). https://pith.science/paper/RXJD6FLL

@misc{pith2026250116511,
  author       = {Pith},
  title        = {Pith review of: AMPEL workflows for LSST: Modular and reproducible real-time photometric classification},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RXJD6FLL}},
  note         = {Machine review of arXiv:2501.16511}
}

read the original abstract

Modern time-domain astronomical surveys produce high throughput data streams which require tools for processing and analysis. This will be critical for programs making full use of the alert stream from the Vera Rubin Observatory (VRO), where spectroscopic labels will only be available for a small subset of all transients. In this context, the AMPEL toolset can work as a code-to-data platform for the development of efficient, reproducible and flexible workflows for real-time astronomical application. We here introduce three different AMPEL channels constructed to highlight different uses of alert streams: to rapidly find infant transients (SNGuess), to provide unbiased transient samples for follow-up (FollowMe) and to deliver final transient classifications (FinalBet). These pipelines already contain placeholders for mechanisms which will be essential for the optimal usage of VRO alerts: combining different classifiers, including host galaxy information, population priors and sampling non-gaussian photometric redshift distributions. Based on the ELAsTiCC simulation, all three channels are already working at a high level: SNGuess correctly tags 99% of all young supernovae, FollowMe illustrates how an unbiased subset of alerts can be selected for spectroscopic follow-up in the context of cosmological probes and FinalBet includes priors to achieve successful classifications for >~80% of all extragalactic transients. The fully functional workflows presented here are all public and can be used as starting points for any group wishing to optimize pipelines for their specific VRO science programs. AMPEL is designed to allow this to be done in accordance with FAIR principles: both software and results can be easily shared and results reproduced. The code-to-data environment ensures that models developed this way can be directly applied to the real-time LSST stream parsed by AMPEL.

Figures

Figures reproduced from arXiv: 2501.16511 by the authors.

**Figure 1.** Schematics of provenance and management features. Top section: Relationship between measurements and derived products stored in the AMPEL DB. Mid: Additional DB collections designed to guarantee provenance. Bottom: Workflow reproduction at different levels: as jobfile, container and orchestrated live instance. rich transients - CART, pair instabilitiy supernovae - PISN).3 The ELAsTiCC simulation also provides spectr… view at source ↗

**Figure 2.** Redshift priors as applied by the FinalBet classifier channel. The best fit transient redshift (x-axis) will be used to derive the probability prior to add to the classifier output. For example: Transients at z = 2 are assumed to be ≈ 90 % SLSN, with TDE, SNII, PISN also being possible. a slightly different path and accepts that photometric classifications have an unavoidable uncertainty. The goal here is rather t… view at source ↗

**Figure 3.** Relative probabilities of classes as estimated from BTS. Each class (also these not defined by BTS) has a minimal probability of 0.01 assigned. Class Mean (u-g) Std (u-g) CART 1.17 0.55 ILOT 1.15 0.51 KN 1.31 0.33 PISN 0.34 0.45 SLSN-I 0.38 0.37 SNIa91bg 1.89 0.43 SNIa 0.61 0.39 SNIax 0.69 0.37 SNIbc 0.95 0.57 SNII 0.88 0.53 TDE 0.88 0.72 [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (7 more)

**Figure 4.** Figure 4: AMPEL schema for the SNGuess channel. T0: Top row (green parallelograms) show possible loader classes, each of which provides LSST like alerts to the alert filter (cyan diamond). T2: Alerts which pass the filter stage are stored into the database and augment/compute op…

**Figure 6.** Figure 6: Confusion matrix for alerts derived from ELAsTiCC labeled sample #34-#37 (not included in training). Alerts were classified as either "AGN" (recurrent, extragalactic transients), "Stellar" (galactic transients) or "SN-like". The SN-like subset encompass non-recurrent…

**Figure 5.** Figure 5: AMPEL schema for the FollowMe and FinalBet channels. The SNGuess workflow ( [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

**Figure 7.** Figure 7: Top: Separation of SNIa and SNIbc transients into three groups (I, II, III) based on the SNIa probability reported by the FollowMe classifier. G-I with P(SNIa)>0.95 encompasses most SNIa events, while G-III captures almost all SNIbc but with a small SNIa contamination…

**Figure 8.** Figure 8: Distribution of photometric redshift (top) and u-i galaxy color (bottom) for SNe Ia in the ELAsTiCC training sample. Each panel shows the distributions for "secure" SNIa (G-I), "secure" SNIbc (G-III) and uncertain (G-II), together with the full SNIa population (grey li…

**Figure 11.** Figure 11 [PITH_FULL_IMAGE:figures/full_fig_p011_11.png]

**Figure 10.** Figure 10: Confusion matrix for non-recurrent transients classified by the FinalBet classifier. As predictions any final classification with a probability larger than 68% were included, which is fulfilled for 67% of all ELAsTiCC transients. Numbers normalized along columns (pre…

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Pith tools

Reviewed August 10, 2026 · model on record in the stance chip above.

[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....

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Balluch, M. 1988, A&A, 200, 58

1988

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Cox, J. P. 1980, Theory of Stellar Pulsation (Princeton University Press, Princeton) 165

1980

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N.,& Stewart, J

Cox, A. N.,& Stewart, J. N. 1969, Academia Nauk, Scientific Information 15, 1

1969

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1980, Prog

Mizuno H. 1980, Prog. Theor. Phys., 64, 544

1980

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Tscharnuter W. M. 1987, A&A, 188, 55

1987