Synthetic Training and Representation Bridging in Reconstruction Domains

Wonyong Chung

REVIEW 4 major objections 6 minor 9 references

Synthetic Training and Representation Bridging in Reconstruction Domains

T0 review · 4 major / 6 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This paper proposes that reconstructing particle properties from detector data should be factorized through a synthetic intermediate representation of detector physics, because the two-step mapping loses less information and is more…

desk verdict A genuinely new idea for reconstruction—training on simulated optical-photon tracks as an intermediate target—but the 'less lossy' claim is untested and, as stated, information-theoretically wrong; deserves a serious referee, not acceptance. read the letter →

arxiv 2505.05664 v1 pith:Q5TCNGXS submitted 2025-05-08 hep-ph hep-ex

Wonyong Chung This is my paper · ORCID

classification hep-phhep-ex

keywords syntheticrepresentationsdomaingapdual-readoutcalorimetryimage-to-imagetranslationU-Netdetectorsimulationinterpretablemachinelearningcalorimeterreconstruction

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper proposes that reconstructing particle properties from detector data should be factorized as $D\to S\to T$, passing through a synthetic intermediate space $S$ that encodes detector-specific physics available only in full simulation. The claim is that because $S$ is closer to the detector space $D$ in dimensionality and information content, the two-step mapping loses less information than the direct map $D\to T$, and it anchors the machine-learning model to a known physical method. As a first demonstration, the paper trains a U-Net to recover simulated scintillation and Cerenkov optical-photon track images from realistic calorimeter hits for 50 GeV single particles in a dual-readout crystal calorimeter. The first results are qualitative: the network resolves the sparse Cerenkov structure first, which the author interprets as the model implicitly performing the dual-readout correction. If the hypothesis holds, ML-based reconstruction and anomaly detection would become more accurate and interpretable across experiments that rely on high-statistics Monte Carlo simulation.

What carries the argument

The load-bearing machinery is the synthetic representation space $S$, instantiated as per-crystal track hits of optical scintillation and Cerenkov photons saved in full simulation with a track-level wavelength cut (200–600 nm) instead of an energy-deposit threshold. This turns the intermediate representation into a simulated observable: it would never appear in a real detector, yet it encodes a real physical process tied to detector hardware. The U-Net's image-to-image translation $D\to S$ then anchors the network's latent space to dual-readout physics, and the eventual $S\to T$ mapping is argued to be better conditioned because $S$ is closer to $D$ in dimensionality and information content.

What would settle it

Train the full factorized pipeline $D\to S\to T$ and a direct $D\to T$ regressor or classifier on the same simulated events with the same training budget, and compare truth-reconstruction accuracy on a held-out set. If the direct mapping matches or beats the factorized pipeline, the central claim is false; a second check is to test whether $S$ carries information about the truth label $T$ that is not already in $D$, since without such extra information no factorization can reduce information loss.

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Extended reading notes

Core claim

The central claim, stated as a hypothesis in the introduction, is that factorizing inference through a synthetic representation of truth reduces information loss compared with a direct mapping from truth to experimental data. The synthetic space $S$ is engineered from detector hardware capabilities and real physical processes, here the track-level trajectories of scintillation and Cerenkov photons in a longitudinally segmented dual-readout crystal calorimeter, so that the mapping from realistic hits $D$ to $S$ is less degenerate, and the subsequent mapping $S\to T$ operates on a richer, better-conditioned feature space. The paper's demonstration trains a three-level U-Net to infer the synthetic channels from the realistic channels of event images, and finds that the Cerenkov signal is resolved first, which it reads as the network implicitly learning the dual-readout correction.

Load-bearing premise

The argument rests on the untested premise that the synthetic space $S$ is genuinely closer to the detector space $D$ in information content, making the two-stage map $D\to S\to T$ better conditioned and less lossy than the direct $D\to T$; if $S$ does not actually capture the relevant physics, or the factorization is no better conditioned, the central advantage of the proposal collapses.

Editorial extensions

If this is right

A trained $D\to S$ mapping can recover fine-grained shower structure, such as skin depth and hair-like track protrusions, that is invisible in realistic detector hits, giving reconstruction access to hidden physics information.
Because the network locks onto the sparse Cerenkov signal before the scintillation signal, the method offers a built-in interpretability axis: the order of feature learning mirrors the physics of dual-readout correction.
The same construction, an intermediate synthetic response grounded in hardware physics, can be applied to other detectors or to cosmological surveys that map low-dimensional truth to high-dimensional observations.
If the factorization is trained end-to-end with the combined loss, the pipeline yields truth labels $T$ directly from detector hits while keeping the synthetic anchor during training.
Anomalous detector signals flagged by networks trained this way would be more likely to correspond to genuine physical anomalies rather than artifacts of the latent space.

Reading between the lines

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

A direct quantitative test is implied but not performed: comparing the information content or reconstruction accuracy of $g\circ f$ against a direct $D\to T$ network on identical data would settle whether the factorization actually reduces information loss.
The claim that the Cerenkov signal is learned first because it is sparse could be checked against a control network trained on a non-physical intermediate target; if a random sparse target produces the same learning order, the physics grounding is not what drives the observed behavior.
The synthetic-space idea suggests a new anomaly-detection strategy: use residuals in $S$-space, predicted versus true synthetic tracks, as a physics-anchored anomaly score, potentially flagging new particles or detector effects as deviations in known physical structure.
Because $S$ is defined by the detector's hardware capabilities, geometrically reconfigurable simulation could be used to optimize detector design jointly with the reconstruction network, treating the synthetic representation as a tunable inductive bias.

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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

The core concept here is new and worth taking seriously. Training a network to map realistic calorimeter hits to simulated optical-photon tracks, then using that synthetic representation as an intermediate for downstream inference, is a real departure from the latent-space and unfolding literature. The author has built an actual full simulation with a dual-readout crystal calorimeter, defined a concrete synthetic target space, and shown that a U-Net can plausibly infer the missing synthetic channels from realistic-only inputs. That is a legitimate proof-of-concept, clearly written and honest about its preliminary nature.

The soft spot is exactly where the reader and the stress test put it. The headline claim—that D→S→T is "less lossy" than direct D→T—is never tested. No NN2 is trained, no reconstruction accuracy reported, no direct comparison to a one-stage mapping. Worse, the claim as stated cannot be true in the information-theoretic sense: the composite g(f(D)) is a deterministic function of D, so I(T; g(f(D))) ≤ I(T; D). Any advantage must come from inductive bias—better-conditioned optimization, easier targets, or regularization at finite data/compute—and that version is not tested either. The qualitative images in Fig. 7 are suggestive, and the interpretation that the U-Net picks up the sparse Cerenkov signal first is plausible, but that does not establish that S is more informative than D or that the two-stage pipeline beats the direct one.

I also note the "first fully dynamic and reconfigurable geometry" is supported by a self-citation (ref. 7). That is not a flaw by itself, but it is a reminder that the detector construction is the author's own prior contribution, so the novelty sits in the synthetic-target idea, not the simulation.

Who is this for? Researchers working on ML-based reconstruction in HEP, especially those interested in physically interpretable intermediate representations or in the interplay between differentiable simulation and reconstruction. They will find the idea stimulating, but they will not find the evidence to adopt it yet.

My recommendation: send it to peer review, because the idea deserves referee time, but do not accept as is. The revision should either reframe the "less lossy" language as an inductive-bias hypothesis and then test it, or drop the claim. A quantitative comparison D→T vs. D→S→T with the same architecture would be the obvious next step.

Referee Report

4 major / 6 minor

Summary. This paper proposes an intermediate 'synthetic representation' space S, constructed from full-simulation optical-photon track hits in a dual-readout crystal calorimeter, to factorize the reconstruction mapping from detector space D to truth space T into two stages, D→S→T. The authors define two networks, NN1 (D→S) and NN2 (S→T), and implement NN1 as a 3-level U-Net trained on 50 GeV single-particle events to infer the synthetic channels from realistic detector channels. The presented results are qualitative inference images (Figure 7), along with an interpretation that the network learns the Cerenkov signal first and thereby implicitly performs a dual-readout correction. NN2 is never trained, and no quantitative reconstruction accuracy or comparison with a direct D→T mapping is reported.

Significance. If the central hypothesis were validated, the idea of using hardware-motivated synthetic observables as an interpretable intermediate representation for ML-based reconstruction would be genuinely valuable, both for future collider detectors and for other experimental domains facing similar domain-gap problems. The paper also provides a reasonably detailed description of a reconfigurable DD4hep/Geant4 dual-readout calorimeter simulation with custom optical-photon readout, and the channel encoding in Table I is clearly documented. However, the paper's central claim—that the factorization D→S→T is 'less lossy' than direct D→T—is never tested in any quantitative sense, and as literally stated it is in tension with the data processing inequality. The qualitative observation that a U-Net can learn to produce plausible synthetic Cerenkov and scintillation track images is a useful proof-of-concept detail but does not by itself establish the proposed method's advantage.

major comments (4)

[Section I and Section II (Eq. 1-3)] The claim that factorizing the mapping as g∘f : D→T through S is 'less lossy' than a direct D→T mapping is not supported and, if understood information-theoretically, is contradicted by the data processing inequality: for any deterministic functions f and g, I(T; g(f(D))) ≤ I(T; D). The paper explicitly computes S1=f(D1) and T1=g(S1) in Section II, so the composite is a deterministic function of D and cannot recover information about T that is absent from D. If the intended claim is instead that the two-stage pipeline provides a useful inductive bias or better optimization conditioning at finite data or model capacity, that claim requires explicit empirical testing, which is not provided.
[Section II and Section VI] NN2, the S→T mapping, is defined in Section II but never trained or evaluated in this work. Section VI trains only the U-Net for the image-to-image task D→S, and Section VII shows only qualitative inference images. Consequently, no reconstruction performance metrics (energy resolution, particle identification efficiency, or classification accuracy) are reported for the full D→S→T pipeline, so the paper cannot substantiate any statement about the quality of the final reconstruction.
[Section VII and Figure 7] There is no quantitative comparison between the proposed D→S→T pipeline and a direct D→T baseline. The only empirical evidence is a set of qualitative U-Net inference images (Figure 7), which show that the model learns features resembling Cerenkov and scintillation tracks. The interpretation that this 'implicitly performs the dual-readout correction' is plausible but is not tied to any measurable reconstruction quantity, and no comparison to a standard dual-readout calibration or to a direct mapping is attempted.
[Table I and Section IV] The construction of the synthetic space S depends on several manually chosen parameters: the wavelength range 200–600 nm and the scaling/offset factors se=2, sp=5, we=wp=1. The paper provides no sensitivity or ablation study showing how the results (or the information content of S) depend on these choices. Since the central argument is that S is a particularly informative representation, the dependence of the method on these free parameters must be characterized before the claim can be evaluated.

minor comments (6)

[Throughout] The paper uses the spelling 'Cerenkov' throughout; the standard physics spelling is 'Cherenkov'. Please use one consistent spelling, ideally 'Cherenkov'.
[Section VI] The last sentence of Section VI says 'Then we run inference to generate images for a test set of 1000 images for each particle type and classify them,' but no classification task or classification result is described anywhere in the paper. This sentence should be clarified or connected to an actual reported analysis.
[Figure 7] The caption of Figure 7 says 'various training epochs and batch sizes' but the individual panels are not labeled with the exact epoch and batch size. Adding explicit per-panel labels would make the qualitative comparison much easier to follow.
[Section V] The angular acceptance is described only as 'a uniform angular distribution in the barrel of the detector only.' Please specify the exact polar-angle range used so the study is reproducible.
[Section I and VII] The term 'machinistic interpretability' is unusual. Consider defining it or replacing it with a more standard term such as 'mechanical interpretability' or 'architectural interpretability'.
[Section III] The assertion that this is 'the first fully dynamic and reconfigurable geometry written for a collider detector' is a strong novelty claim and would benefit from a supporting reference or a clearer qualification of what 'fully dynamic and reconfigurable' means in this context.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation is present: the central claim is an untested hypothesis, and the interpretability observation is an acknowledged design property.

full rationale

The load-bearing claim of the paper is explicitly framed as a hypothesis, not a derived result: Section I states 'The core hypothesis is that by factorizing the inference problem into two stages, D→S→T, the overall process becomes less susceptible to information loss,' and Section II introduces NN2 g:S↔T as a proposed second stage that is never trained in the implementation. No equation in the paper is used to derive the 'less lossy' statement from the definition of S, and no fitted parameter is renamed as a prediction. The only definitional aspect is that S is constructed as simulated Cerenkov/scintillation optical-photon tracks (Section IV), so a network trained with LS=E[LS(f(D),S)] will by construction reproduce those tracks; the paper states this design intent explicitly ('Because we have deliberately engineered the synthetic data to represent the known physical process of dual-readout...'). That is an acknowledged design tautology, not a hidden circular derivation, and the paper does not use it to prove the untested 'less lossy' claim. The self-citations (refs 6 and 7) concern jet-resolution performance and detector-construction details; they are not load-bearing for the factorization proposal. The data-processing-inequality objection to the literal 'less lossy' wording is a correctness/evidence issue, not circularity, and is not scored here.

Assumptions & free parameters 4 free parameters · 3 assumptions · 1 invented entities

The central claim depends on hand-chosen encoding constants, the definition of which photon tracks count as 'synthetic', and the untested assumption that an intermediate space defined by simulated optics is better-conditioned for inference. The paper provides no quantitative test of this assumption, and the only evidence for the value of the invented space is the qualitative observation that a network can learn to produce it.

free parameters (4)

se (energy scaling factor) = 2
Hand-chosen in Table I to compress the dynamic range of the energy deposit channels. It determines the relative scale of the input/target channels in the U-Net loss.
sp (S/C count scaling factor) = 5
Hand-chosen in Table I for the photon-count channels; affects their weight in the L1+SSIM loss.
we, wp (offset factors) = 1
Hand-chosen in Table I as additive offsets in the log weighting.
Optical wavelength range for synthetic tracks = 200-600 nm
Chosen in Section IV to match lead tungstate production wavelengths; defines which photon tracks enter the synthetic representation.

assumptions (3)

domain assumption The synthetic space S is closer in dimensionality and information content to D, and this makes the S→T mapping better-conditioned than D→T.
Stated in Section I as the core hypothesis; no derivation or measurement is provided to support the 'closer' property or its benefit.
domain assumption The simulated optical-photon tracks faithfully encode physical shower structure that is useful for reconstruction.
Section IV claims the tracks 'represent a true physical process', but this is asserted, not validated against any reconstruction task.
domain assumption A U-Net with a masked-channel objective is a sufficient model for the D→S mapping.
Section VI chooses a 3-level U-Net without comparison to other architectures; the paper acknowledges 'any suitable architecture may be used'.

invented entities (1)

Synthetic representation space S (simulated observables: zero-energy optical-photon track hits)
purpose: Intermediate supervised target for ML reconstruction, bridging realistic detector hits D and truth labels T.
The entity is defined entirely within the authors' simulation; no external observable or demonstrated improvement in reconstruction provides independent evidence that it carries the claimed information.

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Cite this review

Pith. "Pith review of Synthetic Training and Representation Bridging in Reconstruction Domains." pith.science (2026). https://pith.science/paper/Q5TCNGXS

@misc{pith2026250505664,
  author       = {Pith},
  title        = {Pith review of: Synthetic Training and Representation Bridging in Reconstruction Domains},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Q5TCNGXS}},
  note         = {Machine review of arXiv:2505.05664}
}

read the original abstract

Reconstructing low-dimensional truth labels from high-dimensional experimental data is a central challenge in any scenario that relies on robust mappings across this so-called domain gap, from multi-particle final states in high-energy physics to large-scale early-universe structure in cosmological surveys. We introduce a new method to bridge this domain gap with an intermediate, synthetic representation of truth that differs from methods operating purely in latent space, such as normalizing flows or invertible approaches, in that the synthetic data is specifically engineered to represent intrinsic detector hardware capabilities of the system at hand. The hypothesis is that by encoding physical properties of the detector response available only in full simulation, such synthetic representations result in a less lossy compression and recovery than a direct mapping from truth to experimental data. We demonstrate a first implementation of this concept with full simulation of a dual-readout crystal electromagnetic calorimeter for future collider detectors, in which the synthetic data is constructed to be the simulated detector hits corresponding to photon tracks of scintillation and Cerenkov photons. We refer to these signals as simulated observables as they would not be physical observables in a real detector, but are nonetheless representations of a real physical process. First results show that the synthetic representation naturally anchors the neural network architecture to a known physical method, in this case the dual-readout correction. We believe this strategy opens new avenues for machinistic interpretability and explainability of ML-based reconstruction methods. In the case of anomalous signal detection, we hypothesize that anomalous signals detected in networks trained on synthetic data rooted in a physical process are more likely to be indicative of a genuinely physical anomaly.

Figures

Figures reproduced from arXiv: 2505.05664 by the authors.

**Figure 2.** FIG. 2. Left and middle panes: The full IDEA detector showing all subdetectors with labels. Right [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** FIG. 3. A single phi-slice of the dual-readout segmented crystal calorimeter used for this study. [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (4 more)

**Figure 4.** Figure 4: FIG. 4. Event displays for a single 50 GeV electron showing detector hits for realistic and synthetic [PITH_FULL_IMAGE:figures/full_fig_p006_4.png]

**Figure 5.** Figure 5: FIG. 5. Representative samples of constructed images for 50 GeV electrons, gammas, and neutrons. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]

**Figure 6.** Figure 6: FIG. 6. Representative samples of constructed images for 50 GeV neutral and charged pions. [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]

**Figure 7.** Figure 7: FIG. 7. Inference images of a 50 GeV electron after various training epochs and batch sizes. See [PITH_FULL_IMAGE:figures/full_fig_p009_7.png]

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

9 extracted references · 2 canonical work pages

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title title Deep Generative Models for Fast Photon Shower Simulation in ATLAS , \ https://doi.org/10.1007/s41781-023-00106-9 journal journal Comput. Softw. Big Sci. \ volume 8 ,\ pages 7 ( year 2024 ) ,\ https://arxiv.org/abs/2210.06204 arXiv:2210.06204 NoStop

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Andreassen , author P

author author A. Andreassen , author P. T. \ Komiske , author E. M. \ Metodiev , author B. Nachman ,\ and\ author J. Thaler ,\ title title Omnifold: A method to simultaneously unfold all observables , \ https://doi.org/10.1103/PhysRevLett.124.182001 journal journal Phys. Rev. Lett. \ volume 124 ,\ pages 182001 ( year 2020 ) NoStop

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author author N. Huetsch , author J. M. \ Villadamigo , author A. Shmakov , author S. Diefenbacher , author V. Mikuni , author T. Heimel , author M. Fenton , author K. Greif , author B. Nachman , author D. Whiteson , author A. Butter ,\ and\ author T. Plehn ,\ title title The landscape of unfolding with machine learning , \ https://doi.org/10.21468/SciPos...

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author author M. Lucchini , author W. Chung , author S. Eno , author Y. Lai , author L. Lucchini , author M. Nguyen ,\ and\ author C. Tully ,\ title title New perspectives on segmented crystal calorimeters for future colliders , \ https://doi.org/10.1088/1748-0221/15/11/p11005 journal journal Journal of Instrumentation \ volume 15 ,\ pages P11005--P11005 ...

work page doi:10.1088/1748-0221/15/11/p11005
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\ volume 320 ,\ pages 00052 ( year 2025 ) NoStop

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[9]

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

title title Deep Generative Models for Fast Photon Shower Simulation in ATLAS , \ https://doi.org/10.1007/s41781-023-00106-9 journal journal Comput. Softw. Big Sci. \ volume 8 ,\ pages 7 ( year 2024 ) ,\ https://arxiv.org/abs/2210.06204 arXiv:2210.06204 NoStop

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[2] [2]

Paganini , author L

author author M. Paganini , author L. de Oliveira ,\ and\ author B. Nachman ,\ title title Calogan: Simulating 3d high energy particle showers in multilayer electromagnetic calorimeters with generative adversarial networks , \ https://doi.org/10.1103/PhysRevD.97.014021 journal journal Phys. Rev. D \ volume 97 ,\ pages 014021 ( year 2018 ) NoStop

doi:10.1103/physrevd.97.014021

[3] [3]

Musella \ and\ author F

author author P. Musella \ and\ author F. Pandolfi ,\ title title Fast and Accurate Simulation of Particle Detectors Using Generative Adversarial Networks , \ https://doi.org/10.1007/s41781-018-0015-y journal journal Comput. Softw. Big Sci. \ volume 2 ,\ pages 8 ( year 2018 ) ,\ https://arxiv.org/abs/1805.00850 arXiv:1805.00850 [hep-ex] NoStop

arXiv

[4] [4]

Andreassen , author P

author author A. Andreassen , author P. T. \ Komiske , author E. M. \ Metodiev , author B. Nachman ,\ and\ author J. Thaler ,\ title title Omnifold: A method to simultaneously unfold all observables , \ https://doi.org/10.1103/PhysRevLett.124.182001 journal journal Phys. Rev. Lett. \ volume 124 ,\ pages 182001 ( year 2020 ) NoStop

work page doi:10.1103/physrevlett.124.182001

[5] [5]

Huetsch , author J

author author N. Huetsch , author J. M. \ Villadamigo , author A. Shmakov , author S. Diefenbacher , author V. Mikuni , author T. Heimel , author M. Fenton , author K. Greif , author B. Nachman , author D. Whiteson , author A. Butter ,\ and\ author T. Plehn ,\ title title The landscape of unfolding with machine learning , \ https://doi.org/10.21468/SciPos...

doi:10.21468/scipostphys.18.2.070

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Lucchini , author W

author author M. Lucchini , author W. Chung , author S. Eno , author Y. Lai , author L. Lucchini , author M. Nguyen ,\ and\ author C. Tully ,\ title title New perspectives on segmented crystal calorimeters for future colliders , \ https://doi.org/10.1088/1748-0221/15/11/p11005 journal journal Journal of Instrumentation \ volume 15 ,\ pages P11005--P11005 ...

work page doi:10.1088/1748-0221/15/11/p11005

[7] [7]

\ volume 320 ,\ pages 00052 ( year 2025 ) NoStop

author author Chung, Wonyong ,\ title title Differentiable full detector simulation of a projective dual-readout crystal electromagnetic calorimeter with longitudinal segmentation and precision timing , \ https://doi.org/10.1051/epjconf/202532000052 journal journal EPJ Web Conf. \ volume 320 ,\ pages 00052 ( year 2025 ) NoStop

arXiv

[8] [8]

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