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REVIEW 4 major objections 5 minor 57 references

Impact of nuclear mass models on $r$-process nucleosynthesis and heavy element abundances in $r$-process enhanced metal-poor stars

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper establishes that, among four nuclear mass models, the WS4 model gives the most accurate simulated heavy-element abundances for r-process enriched metal-poor stars, with the smallest rms deviation.

desk verdict WS4 wins the mass-model comparison, but the abundance ranking rests on a 0.025 dex gap with no error bars. read the letter →

arxiv 2411.17076 v1 pith:DMCCHPDY submitted 2024-11-26 astro-ph.HE astro-ph.SRnucl-th

Meng-Hua Chen , Li-Xin Li , En-Wei Liang , Ning Wang This is my paper · ORCID
classification astro-ph.HEastro-ph.SRnucl-th PACS 26.30.-k21.10.Dr25.40.Lw
keywords r-processnucleosynthesisnuclearmassmodelsWS4modelmetal-poorstarsneutronstarmergersrareearthelementsreactionnetworkabundancecomparison
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 asks which theoretical nuclear mass model should be trusted for r-process nucleosynthesis calculations, since the most neutron-rich nuclei involved have never been measured. Using the SkyNet reaction network with updated mass, decay, and reaction-rate data, the authors simulate neutron star merger ejecta with six parameterized trajectories and compare the averaged yields with the observed abundances of eight metal-poor stars that contain both thorium and uranium. They find that the WS4 model reproduces the observed heavy-element pattern most accurately, with a root-mean-square abundance deviation of 0.347 dex, versus 0.456 for FRDM2012, 0.380 for HFB27, and 0.372 for DZ31. The advantage is most visible in the rare-earth region from lanthanum (Z = 57) to lutetium (Z = 71), and the odd-even abundance staggering seen in these stars is also reproduced. If the claim is right, WS4 becomes the preferred nuclear mass input for quantitative r-process abundance work and for Th/U stellar age dating.

What carries the argument

The load-bearing object is the WS4 nuclear mass table, a macroscopic-microscopic model that combines a Weizsaecker-type liquid-drop formula with Skyrme energy-density-functional corrections and a surface-diffuseness term for neutron-rich nuclei. It provides masses, and through the TALYS statistical code it provides neutron-capture rates, for the thousands of unmeasured neutron-rich species that lie on the r-process path. The comparison pipeline is the rms deviation $\sigma(Y)$ between log-abundances from simulation and observation, computed after averaging six neutron-star-merger trajectories and the eight observed stars, with the nucleosynthesis chain built on the SkyNet reaction network and REACLIB/TALYS rates.

What would settle it

Measure masses of neutron-rich nuclei along the r-process path in the rare-earth region, for example in a future ion-storage ring or multi-reflection time-of-flight mass spectrometer, and recompute the abundances; if the measured masses and rates deviate from WS4 by more than the current roughly 0.3 MeV rms claim, the asserted advantage would shrink. Alternatively, recompute the comparison star by star with a much larger set of merger ejecta trajectories or with single-event yields, and check whether WS4 still has the smallest $\sigma(Y)$.

Watch

Extended reading notes

Core claim

The central claim is that nuclear masses and neutron-capture rates from the macroscopic-microscopic WS4 model, fed into a reaction network with merger-like thermodynamic trajectories, reproduce the abundance pattern of r-process enhanced metal-poor stars more accurately than the other three mass models. The paper supports this with a two-stage comparison: theoretical masses from WS4 deviate from the AME2020 experimental masses by about 0.3 MeV rms, noticeably less than the 0.4-0.6 MeV deviations of FRDM2012, HFB27, and DZ31, and the neutron-capture rates computed from WS4 masses likewise agree better with rates based on experimental data. When the calculated element yields are scaled to europium and compared with observed abundances in eight stars, the averaged WS4 pattern gives the smallest rms deviation in log abundance, particularly for rare-earth elements. The paper also reports that the odd-even abundance staggering observed in these stars is reproduced by the WS4-based simulation.

Load-bearing premise

The comparison assumes that each of the eight metal-poor stars was enriched by a single r-process event and that the six merger-like trajectories, averaged together, represent the conditions that produced those stars' heavy elements; if the stars' enrichment histories differ, the model ranking could change.

Editorial extensions

If this is right

  • WS4 should be adopted as the default nuclear mass input for r-process abundance calculations until experimental masses for neutron-rich nuclei become available.
  • Predictions in the rare-earth region, where nuclear mass models differ most strongly, become more reliable and can be compared directly with stellar spectra.
  • Th/U chronometry ages of metal-poor stars, which depend on calculated actinide yields, inherit a smaller nuclear-input uncertainty when WS4 masses are used.
  • The same pipeline offers a template for ranking future nuclear mass models: benchmark against AME2020, recompute TALYS rates, and compare averaged merger yields with observed r-process enhanced star abundances.

Reading between the lines

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

  • The paper leaves implicit that the WS4 advantage could be tested star by star rather than on the averaged pattern, since the current comparison averages both the six ejecta trajectories and the eight stars.
  • A testable extension is to apply the same pipeline to r-process enhanced stars without detected thorium and uranium, to see whether the rare-earth match generalizes beyond the eight-star sample.
  • The comparison does not identify which single WS4 ingredient causes the improvement; varying masses, neutron-capture rates, and fission inputs one at a time would isolate the driver of the rare-earth enhancement.
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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. The paper studies the impact of four nuclear mass models (FRDM2012, HFB27, DZ31, WS4) on r-process nucleosynthesis calculations. The authors update REACLIB reaction rates using AME2020 masses, recompute neutron capture rates with TALYS, run SkyNet nucleosynthesis on six parameterized neutron star merger trajectories from Radice et al. (2018), and compare the resulting abundance patterns with observations of eight r-process enhanced metal-poor stars. The paper reports that WS4 has the smallest rms deviation from AME2020 masses (σ(M)tot = 0.295 MeV) and, in Table 3, the smallest rms deviation between simulated and observed stellar abundances (σ(Y) = 0.347 dex, versus 0.372 dex for DZ31). The authors conclude that WS4 accurately reproduces the observed heavy element abundances, especially in the rare earth region, and recommend WS4 as the nuclear mass model input for r-process simulations.

Significance. If the result is robust, the paper would provide a useful practical recommendation: among four widely used mass models, WS4 gives the best agreement with both AME2020 masses and, apparently, with r-process enhanced metal-poor star abundances. The use of AME2020 newly measured nuclei as an out-of-sample test is a genuine strength, and the workflow (mass updates, TALYS rate recalculation, SkyNet network, stellar comparison) is standard and reproducible in principle. However, the central abundance-ranking claim rests on a very small difference in σ(Y) between WS4 and DZ31, and the paper provides no uncertainty quantification or sensitivity analysis. The mechanistic explanation for the abundance ranking is also weakened by the paper's own Table 1, where DZ31 has a smaller rms deviation for neutron capture rates on newly measured nuclei than WS4. The astrophysical conclusion is plausible but not yet established at the level claimed.

major comments (4)
  1. [§3, Table 3 and Eq. (4)] The central claim that WS4 reproduces the observed stellar abundances 'accurately' rests entirely on the σ(Y) values in Table 3, where WS4 differs from DZ31 by only 0.025 dex (0.347 vs. 0.372). Typical observational abundance uncertainties for these stars are 0.1–0.3 dex per element, so this gap is below the noise floor. The paper provides no per-star or per-trajectory breakdown, no bootstrap or jackknife, and no sensitivity analysis to the elements included in the average. I request an explicit uncertainty estimate for σ(Y), a per-star and per-trajectory table or figure, and a demonstration that the WS4 preference is not driven by a single element, a single star, or the averaging procedure.
  2. [§2, Table 1 and §3, Figure 3] The paper's mechanistic explanation—that WS4 matches abundances best because its nuclear inputs agree best with experimental data—is contradicted by Table 1 for the newly measured nuclei: σ(λ)new = 0.281 for DZ31 versus 0.331 for WS4, with HFB27 also at 0.323. Thus on the out-of-sample neutron capture rates, WS4 is not the best model. The text in §3 states that WS4's neutron capture rates are 'significantly more accurate' than those from the other models, but the table does not support this for the 'new' subset. Please either revise the mechanistic discussion to account for this discrepancy or provide an analysis showing why the total σ(λ)tot (0.318 for WS4) is the relevant quantity for the abundance outcome.
  3. [§2, Table 2 and §3, Table 3 note] The abundance comparison averages six parameterized merger trajectories and eight stars and scales all patterns to Y(Z = 63) = 5.0×10^-5. This scaling is a free normalization choice, and the single-event enrichment assumption for the stars is cited from Wu et al. (2022). The paper does not test whether the model ranking in Table 3 survives when the comparison is made per trajectory, per star, or with a different normalization (e.g., scaling to a different element or using an absolute yield). Because the final ranking is a 0.025 dex effect, these methodological choices are load-bearing. I ask for a robustness check: e.g., repeat the ranking using each trajectory individually, each star individually, or leave-one-out on both the trajectory and star samples.
  4. [§3, Figure 6] The statement that 'the abundance patterns from r-process simulations are broadly consistent with the solar r-process abundances' is only qualitative. Since the paper later makes a quantitative claim based on σ(Y), the solar comparison should also be quantified (e.g., a σ(Y) value against solar r-process abundances for the same four models), or the statement should be softened to avoid giving the impression of a quantitative test.
minor comments (5)
  1. [Equations (1), (3), and (4)] The displayed equations contain garbled components (e.g., 'vt' and 'nX1' in Eq. (1), repeated in Eqs. (3) and (4)). These should be typeset properly as root-mean-square expressions.
  2. [Figure 2 and Figure 3] The figure captions state that solid circles represent newly measured nuclei, but in the rendered figures the symbols are not easily distinguishable. Please increase marker size or use a legend to make the 'new' subset visible.
  3. [§2, Table 2] The table lists labels such as BHBlp_M135135 and LS220_M144139, but the text defines the parameters as Ye, s, and v. The density profile and temperature evolution are only described in prose; a brief statement of the initial density or entropy normalization would help reproducibility.
  4. [§2, Figure 1 caption] The caption says the abundance distribution is at t = 0.2 s, while the text says simulations start when T drops below 6×10^9 K. Since Figure 1 is meant to illustrate the early r-process path, please clarify the relationship between these two times.
  5. [References] Reference 'Wang, X., N3AS Collaboration, Vassh, N., et al. 2020' uses an unusual author-list format; please check the journal style for collaboration entries.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: all benchmarks are external (AME2020, TALYS-based rates, and observed stellar abundances); WS4 author overlap is a bias/robustness concern, not a circular reduction.

full rationale

The paper's derivation chain is (i) compare four published mass models against AME2020 experimental masses (Eq. 1, Table 1); (ii) compute (n,gamma) rates from those masses with TALYS and compare against experimental rates (Eq. 3, Table 1); (iii) run the SkyNet r-process network on six Radice et al. (2018) trajectories with each model's masses and rates and compare average abundance patterns against average observed abundances of eight r-process enhanced metal-poor stars (Eq. 4, Table 3, Fig. 7). No parameter of any mass model is fitted to the stellar abundance data, and no quantity in Eqs. (1)-(4) is defined in terms of the quantity it is used to explain. The WS4 model was published in 2014 (Wang et al. 2014) before the AME2020 data, so the sigma(M)_new and sigma(lambda)_new comparisons are out-of-sample predictions; the stellar abundance comparison is likewise a forward simulation with no fitted abundance parameter. Author Ning Wang is a co-developer of WS4 and the paper cites his model, but the model is an independent public artifact with a published mass table, and the supporting evidence comes from external AME2020, NUBASE2020, TALYS, and stellar observations, not from the citation itself. The small 0.025 dex gap between WS4 and DZ31 in Table 3 and the averaging over trajectories and stars raise robustness concerns, but those are statistical and interpretational issues, not circularity. No step reduces, by construction, to its own input. Score 0.

Assumptions & free parameters 2 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new entities and fits no scientific parameters to the stellar abundance data. The central comparison relies on external mass models, reaction rate calculations, and astrophysical trajectory choices. The main free choices are the abundance normalization and the polynomial fitting of reaction rates, neither of which is a physical parameter. The load-bearing assumptions are astrophysical (single-event pollution, trajectory representativeness) and methodological (TALYS reliability).

free parameters (2)
  • REACLIB polynomial coefficients a0..a6 = Not tabulated; fitted per nuclide
    Fitted to TALYS neutron capture rates via least squares in log space (Section 2, Eq. 2). They are a numerical compression of the rates, not physical parameters, but the fit quality affects the rates used in the network.
  • Abundance scaling at Z=63 = 5.0e-5
    All observed and simulated abundances are scaled to Y(Z=63)=5e-5 for comparison (Section 3, Figure 7). This common normalization removes absolute yield information and is applied identically to all models, so it does not favor one model but is a hand-set scale.
assumptions (5)
  • domain assumption The eight selected metal-poor stars were each polluted by a single r-process event
    The paper relies on Th/U chronometer ages (Wu et al. 2022) to argue for single-event pollution (Section 1). If multiple events contributed, the comparison to a single merger simulation would be invalid.
  • domain assumption The average of six parameterized NSM trajectories from Radice et al. (2018) is representative of the ejecta that enriched the stars
    The six trajectories in Table 2 are used without weighting or uncertainty. The resulting abundance patterns are averaged, and this average is compared to the stellar data (Section 2 and Figure 7). Poor trajectory coverage would bias the model ranking.
  • domain assumption TALYS statistical reaction rates are reliable for extremely neutron-rich nuclei near the r-process path
    TALYS is used to compute (n,gamma) rates for all models (Section 2). Its applicability to unmeasured, very neutron-rich nuclei is assumed; deviations from real rates could affect all models differently.
  • domain assumption The observed element abundances in the selected stars are pure r-process products for the elements compared
    The paper selects r-process enhanced stars and assumes that elements from Sr to U, particularly Th and U, are r-process-only (Section 1). Residual s-process or weak r-process contributions would distort the comparison.
  • domain assumption AME2020 and NUBASE2020 provide accurate experimental data for the measured nuclei
    The experimental masses and decay data are treated as ground truth (Section 2). This is standard practice in the field.

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Cite this review

Pith. "Pith review of Impact of nuclear mass models on $r$-process nucleosynthesis and heavy element abundances in $r$-process enhanced metal-poor stars." pith.science (2026). https://pith.science/paper/DMCCHPDY

@misc{pith2026241117076,
  author       = {Pith},
  title        = {Pith review of: Impact of nuclear mass models on $r$-process nucleosynthesis and heavy element abundances in $r$-process enhanced metal-poor stars},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DMCCHPDY}},
  note         = {Machine review of arXiv:2411.17076}
}
abstract

Due to the lack of experimental data on extremely neutron-rich nuclei, theoretical values derived from nuclear physics models are essential for the rapid neutron capture process ($r$-process). Metal-poor stars enriched by the $r$-process offer valuable cases for studying the impact of nuclear physics models on $r$-process nucleosynthesis. This study analyzes four widely used nuclear physics models in detail: Finite-Range Droplet Model, Hartree-Fock-Bogoliubov, Duflo-Zuker, and Weizs$\ddot{\rm a}$cker-Skyrme (WS4). Theoretical values predicted by the WS4 model are found to be in good agreement with experimental data, with deviations significantly smaller than those predicted by other models. The heavy element abundances observed in $r$-process enhanced metal-poor stars can be accurately reproduced by $r$-process nucleosynthesis simulations using the WS4 model, particularly for the rare earth elements. This suggests that nuclear data provided by nuclear physics model like WS4 are both essential and crucial for $r$-process nucleosynthesis studies.

Figures

Figures reproduced from arXiv: 2411.17076 by the authors.

Figure 1
Figure 1. Comparison of typical features of the r-process path with the latest measured nuclei from the AME2020 database. The abundance distribution corresponds to the early stages of r-process nucleosynthe￾sis at t = 0.2 s, when the neutron capture process has begun. Our nucleosynthesis calculations start when the temperature drops below T = 6 × 109 K. The astrophysical parameters are typical for binary neutron star mergers:… view at source ↗
Figure 2
Figure 2. Deviations between theoretical values from nuclear mass models and experimental values in the AME2020 database. Four widely used nuclear mass models are considered: FRDM2012, HFB27, DZ31, and WS4. The solid circles represent newly measured nuclei included in the AME2020 but not in the previous AME2016. ergy density functional and accounts for the surface diffuseness effect in neutron-rich nuclei (Wang et al. 2014). … view at source ↗
Figure 3
Figure 3. The same as [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (4 more)
Figure 5
Figure 5. Figure 5: Deviations between the neutron capture rates obtained using the fitted polynomial format and those calculated with the TALYS code. The neutron capture rates are shown at temperatures of 1 × 109 K (top panel) and 6 × 109 K (bottom panel). Nuclear mass values are based o…
Figure 4
Figure 4. Figure 4: Comparison of neutron capture rates for each nucleus calculated using various nuclear physics models. accurate than those from the other three nuclear physics models. The deviations of neutron capture rates with the WS4 model are within the same order of magnitude as t…
Figure 6
Figure 6. Figure 6: Abundance patterns of r-process elements produced by neutron star mergers at t = 109 s. The solar r-process abundances from Arnould et al. (2007) are shown for comparison. Astrophysical parameters used in the simulations are detailed in [PITH_FULL_IMAGE:figures/full_f…
Figure 7
Figure 7. Figure 7: Comparison of r-process abundances from neutron star mergers with those observed in r-process enhanced metal-poor stars. Eight r-process enhanced metal-poor stars are considered: CS 31082-001 (Hill et al. 2002), BD +17°3248 (Cowan et al. 2002), HE 1523-0901 (Frebel et …

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Works this paper leans on

57 extracted references · 32 canonical work pages

  1. [1]

    P., Abbott, R., Abbott, T

    Abbott, B. P., Abbott, R., Abbott, T. D., et al. 2017, ApJ, 848, L12

    2017

  2. [2]

    & Martínez-Pinedo, G

    Arcones, A. & Martínez-Pinedo, G. 2011, Phys. Rev. C, 83, 045809

    work page 2011

  3. [3]

    & Thielemann, F.-K

    Arcones, A. & Thielemann, F.-K. 2023, A&A Rev., 31, 1

    2023

  4. [4]

    2007, Phys

    Arnould, M., Goriely, S., & Takahashi, K. 2007, Phys. Rep., 450, 97

    2007

  5. [5]

    M., Burbidge, G

    Burbidge, E. M., Burbidge, G. R., Fowler, W. A., & Hoyle, F. 1957, Reviews of Modern Physics, 29, 547

    1957

  6. [6]

    2022, ApJ, 932, L7

    Chen, M.-H., Hu, R.-C., & Liang, E.-W. 2022, ApJ, 932, L7

    2022

  7. [7]

    2023, MNRAS, 520, 2806

    Chen, M.-H., Hu, R.-C., & Liang, E.-W. 2023, MNRAS, 520, 2806

    2023

  8. [8]

    2021, ApJ, 919, 59

    Chen, M.-H., Li, L.-X., Lin, D.-B., & Liang, E.-W. 2021, ApJ, 919, 59

    2021

Show all 57 references
  1. [9]

    & Liang, E.-W

    Chen, M.-H. & Liang, E.-W. 2024, MNRAS, 527, 5540

    2024

  2. [10]

    J., Sneden, C., Burles, S., et al

    Cowan, J. J., Sneden, C., Burles, S., et al. 2002, ApJ, 572, 861

    2002

  3. [11]

    J., Sneden, C., Lawler, J

    Cowan, J. J., Sneden, C., Lawler, J. E., et al. 2021, Reviews of Modern Physics, 93, 015002

    2021

  4. [12]

    H., Amthor, A

    Cyburt, R. H., Amthor, A. M., Ferguson, R., et al. 2010, ApJS, 189, 240

    2010

  5. [13]

    & Zuker, A

    Duflo, J. & Zuker, A. P. 1995, Phys. Rev. C, 52, R23

    1995

  6. [14]

    2015, ApJ, 808, 30

    Eichler, M., Arcones, A., Kelic, A., et al. 2015, ApJ, 808, 30

    2015

  7. [15]

    E., et al

    Frebel, A., Christlieb, N., Norris, J. E., et al. 2007, ApJ, 660, L117

    2007

  8. [16]

    Goriely, S., Chamel, N., & Pearson, J. M. 2009, Phys. Rev. Lett., 102, 152503

    2009

  9. [17]

    Goriely, S., Chamel, N., & Pearson, J. M. 2013, Phys. Rev. C, 88, 061302

    2013

  10. [18]

    Goriely, S., Hilaire, S., & Koning, A. J. 2008, A&A, 487, 767

    2008

  11. [19]

    W., Niu, Y

    Hao, Y . W., Niu, Y . F., & Niu, Z. M. 2023, Physics Letters B, 844, 138092

    2023

  12. [20]

    C., et al

    Hill, V ., Christlieb, N., Beers, T. C., et al. 2017, A&A, 607, A91

    2017

  13. [21]

    2002, A&A, 387, 560

    Hill, V ., Plez, B., Cayrel, R., et al. 2002, A&A, 387, 560

    2002

  14. [22]

    M., Beers, T

    Holmbeck, E. M., Beers, T. C., Roederer, I. U., et al. 2018, ApJ, 859, L24

    2018

  15. [23]

    J., Arcones, A., Côté, B., et al

    Horowitz, C. J., Arcones, A., Côté, B., et al. 2019, Journal of Physics G Nuclear Physics, 46, 083001

    2019

  16. [24]

    2018, International Journal of Modern Physics D, 27, 1842005

    Hotokezaka, K., Beniamini, P., & Piran, T. 2018, International Journal of Modern Physics D, 27, 1842005

    2018

  17. [25]

    2016, MNRAS, 459, 35

    Hotokezaka, K., Wanajo, S., Tanaka, M., et al. 2016, MNRAS, 459, 35

    2016

  18. [26]

    B., et al

    Kajino, T., Aoki, W., Balantekin, A. B., et al. 2019, Progress in Particle and Nuclear Physics, 107, 109

    2019

  19. [27]

    2017, Na- ture, 551, 80

    Kasen, D., Metzger, B., Barnes, J., Quataert, E., & Ramirez-Ruiz, E. 2017, Na- ture, 551, 80

    2017

  20. [28]

    & Takahashi, K

    Kodama, T. & Takahashi, K. 1975, Nucl. Phys. A, 239, 489

    1975

  21. [29]

    G., Wang, M., Huang, W

    Kondev, F. G., Wang, M., Huang, W. J., Naimi, S., & Audi, G. 2021, Chinese Physics C, 45, 030001

    2021

  22. [30]

    Lattimer, J. M. & Schramm, D. N. 1974, ApJ, 192, L145

    1974

  23. [31]

    J., Gompertz, B

    Levan, A. J., Gompertz, B. P., Salafia, O. S., et al. 2024, Nature, 626, 737

    2024

  24. [32]

    & Paczy´nski, B

    Li, L.-X. & Paczy´nski, B. 1998, ApJ, 507, L59

    1998

  25. [33]

    & Roberts, L

    Lippuner, J. & Roberts, L. F. 2015, ApJ, 815, 82

    2015

  26. [34]

    & Roberts, L

    Lippuner, J. & Roberts, L. F. 2017, ApJS, 233, 18

    2017

  27. [35]

    Mendoza-Temis, J. d. J., Wu, M.-R., Langanke, K., et al. 2015, Phys. Rev. C, 92, 055805

    2015

  28. [36]

    D., Martínez-Pinedo, G., Darbha, S., et al

    Metzger, B. D., Martínez-Pinedo, G., Darbha, S., et al. 2010, MNRAS, 406, 2650 Möller, P., Myers, W. D., Sagawa, H., & Yoshida, S. 2012, Phys. Rev. Lett., 108, 052501 Möller, P., Nix, J. R., Myers, W. D., & Swiatecki, W. J. 1995, Atomic Data and Nuclear Data Tables, 59, 185 Mö...

    2010

  29. [37]

    R., McLaughlin, G

    Mumpower, M. R., McLaughlin, G. C., & Surman, R. 2012, Phys. Rev. C, 86, 035803

    2012

  30. [38]

    R., Surman, R., Fang, D

    Mumpower, M. R., Surman, R., Fang, D. L., et al. 2015, Phys. Rev. C, 92, 035807

    2015

  31. [39]

    R., Surman, R., McLaughlin, G

    Mumpower, M. R., Surman, R., McLaughlin, G. C., & Aprahamian, A. 2016, Progress in Particle and Nuclear Physics, 86, 86

    2016

  32. [40]

    2021, ApJ, 906, 98

    Nedora, V ., Bernuzzi, S., Radice, D., et al. 2021, ApJ, 906, 98

    2021

  33. [41]

    2009, Phys

    Niu, Z., Sun, B., & Meng, J. 2009, Phys. Rev. C, 80, 065806

    2009

  34. [42]

    M., Holmbeck, E

    Placco, V . M., Holmbeck, E. M., Frebel, A., et al. 2017, ApJ, 844, 18

    2017

  35. [43]

    2018, ApJ, 869, 130

    Radice, D., Perego, A., Hotokezaka, K., et al. 2018, ApJ, 869, 130

    2018

  36. [44]

    U., Beers, T

    Roederer, I. U., Beers, T. C., Hattori, K., et al. 2024, ApJ, 971, 158

    2024

  37. [45]

    J., & Gallino, R

    Sneden, C., Cowan, J. J., & Gallino, R. 2008, ARA&A, 46, 241

    2008

  38. [46]

    & Engel, J

    Surman, R. & Engel, J. 2001, Phys. Rev. C, 64, 035801

    2001

  39. [47]

    & Schramm, D

    Symbalisty, E. & Schramm, D. N. 1982, Astrophys. Lett., 22, 143

    1982

  40. [48]

    2024, Phys

    Vassh, N., Wang, X., Larivière, M., et al. 2024, Phys. Rev. Lett., 132, 052701

    2024

  41. [49]

    G., et al

    Wang, M., Audi, G., Kondev, F. G., et al. 2017, Chinese Physics C, 41, 030003

    2017

  42. [50]

    J., Kondev, F

    Wang, M., Huang, W. J., Kondev, F. G., Audi, G., & Naimi, S. 2021, Chinese Physics C, 45, 030003

    2021

  43. [51]

    2010, Phys

    Wang, N., Liang, Z., Liu, M., & Wu, X. 2010, Phys. Rev. C, 82, 044304

    2010

  44. [52]

    2014, Physics Letters B, 734, 215

    Wang, N., Liu, M., Wu, X., & Meng, J. 2014, Physics Letters B, 734, 215

    2014

  45. [53]

    2020, ApJ, 903, L3

    Wang, X., N3AS Collaboration, Vassh, N., et al. 2020, ApJ, 903, L3

    2020

  46. [54]

    J., Selsing, J., et al

    Watson, D., Hansen, C. J., Selsing, J., et al. 2019, Nature, 574, 497

    2019

  47. [55]

    H., Zhao, P

    Wu, X. H., Zhao, P. W., Zhang, S. Q., & Meng, J. 2022, ApJ, 941, 152

    2022

  48. [56]

    S., et al

    Yong, D., Kobayashi, C., Da Costa, G. S., et al. 2021, Nature, 595, 223

    2021

  49. [57]

    L., Lund, K

    Zhu, Y . L., Lund, K. A., Barnes, J., et al. 2021, ApJ, 906, 94 Article number, page 8 of 8

    2021

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.