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REVIEW 2 major objections 1 minor 19 references

Two-Body Scattering Observables from Finite-Volume Real-Time Evolution

T0 review · 2 major / 1 minor · reviewed 2026-06-25 · grok-4.3

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Pith's one-line read A convolutional neural network trained on finite-volume real-time evolution data predicts two-body scattering observables for unseen Hamiltonians.

desk verdict The paper sets up real-time finite-volume evolution plus CNN prediction for 2D scattering observables, but supplies no numbers or sampling details so the generalization claim stays untested. read the letter →

arxiv 2606.25199 v1 pith:PNKVS3OD submitted 2026-06-23 nucl-th

Lucas Platter This is my paper · ORCID
classification nucl-th
keywords finite-volumereal-timeevolutionscatteringobservablesconvolutionalneuralnetworktwo-bodylatticemachinelearning
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 examines two-body scattering in a finite periodic box by evolving wave packets in real time for distinguishable particles on a 2D lattice with pointlike s- and p-wave interactions. Detector observables are defined via angular wedges in the relative coordinate, and each evolution is labeled using the bound-state pole equation together with the low-energy scattering amplitude. A convolutional neural network is trained on the resulting dataset and evaluated on held-out scattering problems, where it recovers both the overall magnitude and the angular dependence for interactions not encountered in training.

What carries the argument

Convolutional neural network trained on real-time wave-packet evolutions labeled by the bound-state pole equation and low-energy scattering amplitude.

What would settle it

Apply the trained network to a fresh collection of Hamiltonians with interaction strengths or ranges outside those used in training and check whether its predicted magnitudes or angular distributions match the values obtained from the bound-state pole equation and scattering amplitude.

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Extended reading notes

Core claim

Finite-volume real-time evolution of two-particle wave packets, when labeled with infinite-volume bound-state and scattering information, supplies training data that enables a convolutional neural network to predict the total magnitude and angular shape of scattering observables for previously unseen Hamiltonians.

Load-bearing premise

Finite-volume real-time evolution data labeled with bound-state and low-energy scattering formulas supplies a training distribution that lets the network generalize to Hamiltonians outside the training set.

Editorial extensions

If this is right

  • Scattering observables become accessible from finite-volume simulations without solving the corresponding infinite-volume equations for every new Hamiltonian.
  • Angular wedge observables extracted from the relative coordinate carry enough information to reconstruct both strength and shape of the scattering pattern.
  • The same labeling procedure works for both s-wave and p-wave pointlike interactions on the lattice.

Reading between the lines

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

  • The method could be tested on continuous families of interaction parameters to measure how smoothly the network interpolates between trained cases.
  • Similar finite-volume data plus labeling might support predictions for observables beyond total cross section, such as differential distributions at higher energies.
  • Extending the lattice to three dimensions would check whether the same real-time-plus-network pipeline remains effective.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 1 minor

Summary. The manuscript claims that real-time evolution of two distinguishable particles on a 2D lattice with pointlike s- and p-wave interactions in a finite periodic box yields detector observables (angular wedges) that, when labeled by the bound-state pole equation and low-energy scattering amplitude, form a training set for a convolutional neural network; the CNN is reported to predict total magnitude and angular shape on held-out Hamiltonians.

Significance. If the quantitative performance holds, the approach would supply a machine-learning route to map finite-volume real-time wave-packet data onto infinite-volume scattering observables, complementing traditional Lüscher-type methods in nuclear lattice calculations. The labeling step uses independent standard equations, providing some grounding, but the absence of reported metrics, architecture details, and interaction-parameter coverage prevents a firm assessment of significance.

major comments (2)
  1. [Abstract] Abstract: the claim that the CNN 'is able to predict the total magnitude and angular shape for previously unseen Hamiltonians' supplies no quantitative metrics, error analysis, network architecture, training/validation split sizes, or performance numbers, so the generalization statement cannot be checked against the data.
  2. [Abstract] Abstract: no information is provided on the range, sampling density, or coverage of the s- and p-wave coupling constants used to generate the training distribution; without this, it is impossible to verify whether the finite-volume observables encode the infinite-volume quantities in a manner that transfers outside the training set, which is load-bearing for the central generalization claim.
minor comments (1)
  1. [Abstract] Abstract, last sentence: grammatical error ('find that is able') should read 'find that it is able'.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the careful reading and constructive feedback on our manuscript. The comments correctly identify that the abstract lacks key supporting details. We address each point below and will revise the abstract accordingly.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that the CNN 'is able to predict the total magnitude and angular shape for previously unseen Hamiltonians' supplies no quantitative metrics, error analysis, network architecture, training/validation split sizes, or performance numbers, so the generalization statement cannot be checked against the data.

    Authors: The full manuscript provides the CNN architecture (Section 3), training/validation splits and dataset sizes (Section 4), and quantitative performance metrics with error analysis on held-out Hamiltonians (Section 5). To make the generalization claim verifiable from the abstract alone, we will revise the abstract to include representative performance numbers and a brief reference to the metrics. revision: yes

  2. Referee: [Abstract] Abstract: no information is provided on the range, sampling density, or coverage of the s- and p-wave coupling constants used to generate the training distribution; without this, it is impossible to verify whether the finite-volume observables encode the infinite-volume quantities in a manner that transfers outside the training set, which is load-bearing for the central generalization claim.

    Authors: The ranges, sampling density, and coverage of the s- and p-wave couplings are specified in the dataset-generation section of the main text. We agree that a concise summary of this information belongs in the abstract to directly support the generalization claim. We will add one sentence to the abstract stating the parameter ranges and sampling strategy. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; labels and held-out testing are independent

full rationale

The paper generates finite-volume real-time evolution data, attaches labels from the standard bound-state pole equation and low-energy scattering amplitude (external to the CNN), trains the network, and evaluates generalization on held-out Hamiltonians. No quoted step reduces a claimed prediction to a fitted input or self-citation by construction; the generalization performance is assessed via external held-out testing rather than being forced by the training procedure itself.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

Based on the abstract alone, the central claim rests on the assumption that standard infinite-volume formulas can be used to label finite-volume simulation data; no explicit free parameters or invented entities are identifiable from the provided text.

assumptions (1)
  • domain assumption Infinite-volume scattering observables can be reliably extracted from finite-volume real-time evolution data using the bound-state pole equation and low-energy scattering amplitude.
    This labeling step is explicitly described in the abstract as the source of training targets.

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Cite this review

Pith. "Pith review of Two-Body Scattering Observables from Finite-Volume Real-Time Evolution." pith.science (2026). https://pith.science/paper/PNKVS3OD

@misc{pith2026260625199,
  author       = {Pith},
  title        = {Pith review of: Two-Body Scattering Observables from Finite-Volume Real-Time Evolution},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PNKVS3OD}},
  note         = {Machine review of arXiv:2606.25199}
}
abstract

We study two-body scattering observables from real-time evolution in a finite periodic box. The system consists of two distinguishable particles on a two-dimensional lattice interacting through pointlike $s$- and $p$-wave interactions. We evolve their wave packets in real time, define detector observables through angular wedges in the relative coordinate, and attach infinite-volume labels obtained from the bound-state pole equation and the low-energy scattering amplitude. We train a convolutional neural network on this data and test its performance on held-out scattering problems and find that is able to predict the total magnitude and angular shape for previously unseen Hamiltonians.

Figures

Figures reproduced from arXiv: 2606.25199 by the authors.

Figure 1
Figure 1. FIG. 1. Infinite-volume pure [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. FIG. 2. Normalized wedge fractions for fixed physical ini [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. FIG. 3. Comparison of CNN prediction and held out cross [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: FIG. 4. Mixed ( [PITH_FULL_IMAGE:figures/full_fig_p009_4.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

19 extracted references · 3 canonical work pages

  1. [1]

    R. P. Feynman, Int. J. Theor. Phys.21, 467 (1982)

    1982

  2. [2]

    S. P. Jordan, K. S. M. Lee, and J. Preskill, Science336, 1130 (2012)

    2012

  3. [3]

    Simenel, Eur

    C. Simenel, Eur. Phys. J. A48, 152 (2012)

    2012

  4. [4]

    Benchmark Test Calculation of a Four-Nucleon Bound State

    H. Kamadaet al., Phys. Rev. C64, 044001 (2001), arXiv:nucl-th/0104057

    work page Pith review arXiv 2001

  5. [5]

    Deltuva and A

    A. Deltuva and A. C. Fonseca, Phys. Rev. C79, 014606 (2009)

    2009

  6. [6]

    Gloeckle, H

    W. Gloeckle, H. Witala, D. Huber, H. Kamada, and J. Golak, Phys. Rept.274, 107 (1996)

    1996

  7. [7]

    L¨ uscher, Commun

    M. L¨ uscher, Commun. Math. Phys.104, 177 (1986)

    1986

  8. [8]

    L¨ uscher, Commun

    M. L¨ uscher, Commun. Math. Phys.105, 153 (1986)

    1986

Show all 19 references
  1. [9]

    D. A. Burbano, M. A. Carrillo, R. Urek, A. N. Ciavarella, and R. A. Brice˜ no, arXiv e-prints , arXiv:2506.06511 (2025), arXiv:2506.06511 [hep-lat]

    2025

  2. [10]

    Sharma, T

    S. Sharma, T. Papenbrock, and L. Platter, Phys. Rev. C109, L061001 (2024)

    2024

  3. [11]

    Turro, K

    F. Turro, K. A. Wendt, S. Quaglioni, F. Pederiva, and A. Roggero, Phys. Rev. C110, 054604 (2024)

    2024

  4. [12]

    M. Yusf, L. Gan, C. Moffat, and G. Rupak, Phys. Rev. C111, 034001 (2025)

    2025

  5. [13]

    Bennewitz, B

    E. Bennewitz, B. Ware, A. Schuckert, A. Lerose, F. Muzzio, J. C. Halimeh, F. M. Surace, H. Pichler, and M. Knap, Quantum9, 1773 (2025)

    2025

  6. [14]

    J. R. Taylor,Scattering Theory: The Quantum Theory of Nonrelativistic Collisions(John Wiley & Sons, Inc., New York, 1972)

    1972

  7. [15]

    Y. Chai, A. Crippa, K. Jansen, S. K¨ uhn, V. R. Pascuzzi, F. Tacchino, and I. Tavernelli, Quantum9, 1638 (2025)

    2025

  8. [16]

    Davoudi, C.-C

    Z. Davoudi, C.-C. Hsieh, and S. V. Kadam, Quantum8, 1520 (2024)

    2024

  9. [17]

    Rule and I

    E. Rule and I. Stetcu, (2026), arXiv:2603.26881 [nucl-th]

    2026

  10. [18]

    S. K. Adhikari, Am. J. Phys.54, 362 (1986)

    1986

  11. [19]

    Chadan, N

    K. Chadan, N. N. Khuri, A. Martin, and T. T. Wu, Phys. Rev. D58, 025014 (1998)

    1998

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Reviewed June 25, 2026 · model on record in the stance chip above.