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arxiv: 2604.16250 · v1 · submitted 2026-04-17 · ⚛️ physics.ins-det

Event-Level Voxel Reconstruction in Two-Photon Absorption Scans Using Pixel-Overlap Selection in Timepix3

Tianqi Gao This is my paper

Pith reviewed 2026-05-10 06:40 UTC · model grok-4.3

classification ⚛️ physics.ins-det
keywords two-photon absorptionTimepix3voxel reconstructionpixel overlaptiming estimationsilicon detectorsevent reconstructioncharge transport
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The pith

Pixel-overlap selection with highest-charge timing reconstructs unbiased voxel timing in unsynchronized TPA scans.

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

This paper develops a reconstruction framework for two-photon absorption laser scans performed on pixelated silicon detectors read out by Timepix3. Because the laser pulses and detector data stream are not synchronized, a single excitation produces a multi-pixel cluster whose timing cannot be assigned by conventional means without introducing position-dependent errors. The method defines an event as the set of pixels that overlap in time within the intrinsic resolution and selects the timing stamp from the pixel carrying the largest charge deposit inside that cluster. This choice recovers the underlying dwell structure of the scan and assigns stable timing values to each voxel. The approach is shown to eliminate the systematic spatial biases that appear when timing is instead taken from the cluster centroid or the earliest pixel hit.

Core claim

The paper claims that an event definition based on pixel overlap, combined with a timing estimator that selects the highest deposited charge within the region of interest, permits blind reconstruction of voxel-resolved timing and dwell structure from continuous, unsynchronized Timepix3 data. In this framework a single two-photon absorption excitation is treated as one event whose timing is taken from the dominant pixel rather than averaged or taken from the first arrival; the authors demonstrate that the resulting voxel timing maps remain free of the spatial offsets produced by centroid or earliest-hit alternatives.

What carries the argument

Pixel-overlap definition of TPA events paired with a highest-charge timing estimator inside the region of interest.

If this is right

  • Voxel-resolved timing becomes recoverable from continuous, unsynchronized data streams without external triggers.
  • Dwell structure of the laser scan is reconstructed at the single-excitation level.
  • Systematic spatial biases that appear with centroid or earliest-hit timing are removed for multi-pixel clusters.
  • The resulting timing maps can be used directly for three-dimensional electric-field and charge-transport studies in silicon sensors.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same overlap-plus-highest-charge rule may be portable to other segmented readout chips that produce similar multi-pixel clusters.
  • Pairing the reconstructed timing with charge-transport simulations could provide an internal consistency check on residual bias.
  • The framework could support online reconstruction in scanning systems that deliberately omit trigger hardware.

Load-bearing premise

The pixel-overlap definition together with highest-charge timing must produce unbiased voxel timing and correct dwell structure without external synchronization, and the spatial biases of centroid or earliest-hit estimators must be general rather than specific to the experimental setup used.

What would settle it

A controlled TPA scan performed inside a sensor whose electric field profile is independently known; agreement between the reconstructed voxel timing map and the expected field-dependent charge transport, within the stated resolution, would support the claim, while systematic spatial offsets that correlate with cluster size would falsify it.

Figures

Figures reproduced from arXiv: 2604.16250 by Tianqi Gao.

Figure 1
Figure 1. Figure 1: Definition of the region of interest (ROI) within the pixel matrix. The ROI is selected [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Conceptual illustration of reconstruction ambiguities in TPA measurements with [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Comparison of event selection strategies. Pixel-overlap selection accepts clusters that [PITH_FULL_IMAGE:figures/full_fig_p003_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Reconstruction of dwell intervals from continuous, unsynchronised data. Events are [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Illustration of timing estimators within a clustered event. The earliest ToA pixel is [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Overview of the reconstruction workflow. Continuous detector data are filtered using [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Spatial distribution of pixels selected by the highest-ToT criterion. The distribution [PITH_FULL_IMAGE:figures/full_fig_p005_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Spatial distribution of pixels selected by the earliest-ToA criterion. The distribution [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Distribution of spatial separation between highest-ToT and earliest-ToA pixel selec [PITH_FULL_IMAGE:figures/full_fig_p006_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Cluster occupancy map within the ROI, showing the spatial distribution of activated [PITH_FULL_IMAGE:figures/full_fig_p006_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Spatial distribution of highest-ToT pixels within the ROI. The concentration of events [PITH_FULL_IMAGE:figures/full_fig_p007_11.png] view at source ↗
read the original abstract

Two-photon absorption (TPA) enables three-dimensional characterisation of silicon detectors by generating charge carriers within a confined volume around a focused laser spot. In combination with pixelated readout systems, TPA measurements provide access to spatially resolved timing observables relevant for electric field reconstruction. However, the interpretation of TPA data in segmented detectors is non-trivial: a single excitation produces multi-pixel clusters within the intrinsic time resolution of the readout, and in many implementations no external synchronisation between laser pulses and detector data is available. In this work, we present a reconstruction framework for event-level voxelisation of TPA scans using Timepix3, operating on continuous, unsynchronised data. The method introduces a pixel-overlap-based definition of TPA events and a cluster-level timing estimator based on the highest deposited charge within a region of interest. This approach enables blind reconstruction of dwell structure and stable assignment of voxel timing without external triggers. We demonstrate that commonly used alternatives, such as centroid-based selection or earliest-hit timing, introduce systematic spatial biases in clustered events. The proposed framework provides a robust and general method for reconstructing voxel-resolved timing information in segmented detectors, and is directly applicable to TPA-based studies of electric field distributions and charge transport in silicon sensors.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper introduces a reconstruction framework for event-level voxelisation in two-photon absorption (TPA) scans using Timepix3 detectors on continuous, unsynchronised data. It defines events based on pixel overlap and employs a highest-charge timing estimator to reconstruct dwell structure and voxel timing, claiming superiority over centroid-based selection or earliest-hit timing which introduce systematic spatial biases. The method is positioned as robust and general for TPA-based studies of electric field distributions and charge transport in silicon sensors.

Significance. If the central claims hold, the work offers a practical approach to extracting voxel-resolved timing information from TPA data without requiring external synchronization, which could advance non-destructive characterization of pixelated detectors. A notable strength is the definition of the approach directly from physical observables like pixel overlap and charge deposit, avoiding free parameters or self-referential fitting. The relative comparison to alternative methods highlights potential issues with common practices. However, the significance is tempered by the absence of absolute validation, limiting confidence in the unbiased nature of the results for quantitative applications.

major comments (2)
  1. [Abstract] Abstract: The abstract asserts that alternatives introduce biases and that the new method is stable, but provides no quantitative validation, error analysis, or data exclusion details; the central claims rest on unshown demonstrations of relative performance only.
  2. [Method and Results] Method/Results: The highest-charge timing estimator is claimed to produce unbiased voxel timing and dwell structure, but without ground-truth reference (external trigger or physics simulation of charge sharing/diffusion), relative bias comparisons to centroid/earliest-hit estimators cannot confirm absolute accuracy across the voxel volume under position-dependent effects.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the two major comments point by point, providing clarifications on the validation strategy and noting revisions to the text.

read point-by-point responses

  1. Referee: The abstract asserts that alternatives introduce biases and that the new method is stable, but provides no quantitative validation, error analysis, or data exclusion details; the central claims rest on unshown demonstrations of relative performance only.

    Authors: We agree that the abstract would benefit from greater specificity. The manuscript presents quantitative comparisons in the results section, including standard deviations of reconstructed timing (reduced by 20-30% relative to alternatives) and spatial uniformity metrics across the voxel. Data exclusion is based on requiring full pixel overlap within the event definition, as detailed in the methods. We have revised the abstract to reference these relative performance improvements explicitly and to describe the method as providing 'reduced systematic biases' rather than claiming absolute stability without qualification. revision: yes

  2. Referee: The highest-charge timing estimator is claimed to produce unbiased voxel timing and dwell structure, but without ground-truth reference (external trigger or physics simulation of charge sharing/diffusion), relative bias comparisons to centroid/earliest-hit estimators cannot confirm absolute accuracy across the voxel volume under position-dependent effects.

    Authors: This is a fair observation. The work focuses on blind reconstruction from unsynchronized data, where external ground truth is unavailable. The physical basis for the highest-charge estimator is that it corresponds to the pixel with minimal charge sharing, thus least susceptible to position-dependent timing variations from diffusion. In the revised manuscript, we have added a dedicated discussion section and supporting Monte Carlo simulations of charge transport to demonstrate that the estimator yields timing consistent with the known laser dwell structure, with spatial biases below the level of statistical fluctuations. We have also updated the claims to emphasize relative improvement and physical motivation over absolute unbiasedness. revision: partial

Circularity Check

0 steps flagged

No circularity: definitions grounded in independent observables

full rationale

The paper defines TPA events via pixel-overlap and selects timing via the highest-charge pixel within a cluster. These are direct mappings from raw detector observables (hit positions and charge deposits) rather than parameters fitted to the target quantities or equations that presuppose the output. No self-citation chain, uniqueness theorem, or ansatz is invoked to justify the core reconstruction; the comparison to centroid/earliest-hit alternatives is purely empirical and does not reduce the proposed estimator to its own inputs by construction. The framework therefore remains self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The method rests on standard domain assumptions about TPA charge generation and Timepix3 readout behavior; no new physical entities or free parameters are introduced in the abstract description.

axioms (2)
  • domain assumption Two-photon absorption generates charge carriers within a confined volume around the focused laser spot
    Invoked in the first sentence of the abstract as the basis for 3D characterisation.
  • domain assumption A single TPA excitation produces multi-pixel clusters within the intrinsic time resolution of the readout
    Stated as the core interpretation challenge for segmented detectors.

pith-pipeline@v0.9.0 · 5511 in / 1294 out tokens · 35843 ms · 2026-05-10T06:40:23.572068+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

4 extracted references · 4 canonical work pages

  1. [1]

    Two-photon absorption transient current technique for 3d silicon detector characterisation

    Sebastian Pape et al. Two-photon absorption transient current technique for 3d silicon detector characterisation. JINST, 2023

    work page 2023

  2. [2]

    Wiehe et al

    M. Wiehe et al. Charge collection studies in silicon sensors using tpa-tct. Nuclear Instruments and Methods A , 2021

    work page 2021

  3. [3]

    Recent advances in tpa-tct for segmented silicon sensors

    Sebastian Pape et al. Recent advances in tpa-tct for segmented silicon sensors. Proceedings of Science, 2024

    work page 2024

  4. [4]

    Poikela et al

    T. Poikela et al. Timepix3: a 65k channel hybrid pixel readout chip with simultaneous toa/tot and sparse readout. JINST, 2014. 7

    work page 2014