Summary and Conclusions

We developed simulation and analysis methods to model backgrounds in the Micromegas detectors for the IAXO experiment. The main contributions are a simulation and reconstruction workflow with a developed TPC detector-response model, a study of the shielding and active veto for surface operation, and a source-resolved background inventory for IAXO-D1 in argon–isobutane. The IAXO-D0 measurements provide an experimental benchmark for the veto strategy. Together, these contributions support a future background prediction for BabyIAXO, which requires explicit treatment of its xenon–neon response, optics, site environment, and final geometry.

From radiation transport to detector observables

The software work connects source generation, Geant4 transport, detector response, and reconstruction through REST-for-Physics and restG4. Versioned geometry generation enables comparisons of shielding thickness, veto layers, detector orientation, and channel layouts. The production and analysis tools make those comparisons practical for large simulation campaigns while preserving the connection between physical inputs and reconstructed events. The TPC response developed in this work follows gas ionization, electron drift, amplification and individual charge collection with Heed/Garfield++, then forms signals for the same REST reconstruction. Section 3.4 illustrates the full sequence from the incident particle to reconstructed observables. Historical calibrated comparisons improve the description of several distributions, while differences in selection acceptance remain. The refined model passes independent charge-accounting and waveform checks and now has frozen reconstructed comparisons across 40 completed calibration runs. Shared pulse and delay maps, with one fitted amplitude per run, describe the central widths reasonably closely, while simulated baseline acceptance exceeds data in 38 runs. Numerical gain convergence and reliable selection transfer remain unresolved (Section 3.6). The published cosmic-ray injection method improves the efficiency of transporting particles toward a bounded detector geometry. Its application requires a precise distinction between directional intensity, horizontal-plane crossings, and scalar fluence; the geometric optimization does not remove that normalization requirement.

Common processing exposes disagreements in the observables used for event selection. It does not, by itself, establish agreement between simulation and data. The thesis therefore records the retained event representation, source denominator, reconstruction settings, and validation status for each response. Stored particle-transport histories can support further response studies when the required deposits, times, particle identities, and track information remain available. This is particularly important for nonlinear quenching corrections and for radioactive decays that share an incident parent but occur in separate time windows.

Surface backgrounds and the active veto

The neutron studies explain why increasing passive shielding alone is insufficient for a surface helioscope. High-energy neutrons interact in lead and generate secondary showers whose photons and charged particles can deposit X-ray-like energies in the gas. In the saved-history sample, electromagnetic descendants account for 62.8% of neutron-induced TPC events, whereas direct neutron interactions in the gas account for 5.0%. These are conditional fractions for the simulated source and geometry. They motivate tagging the surrounding shower and capture activity rather than attempting to identify the incident neutron solely from its TPC deposit.

The scintillator–cadmium design combines prompt shower detection with delayed neutron-capture signals. The simulated probability of any reconstructed veto tag increases from 0.465 with one layer to 0.741 with three and 0.771 with four. The smaller gain from the fourth layer supports three layers as a practical engineering choice, subject to the specified source and response assumptions. It does not establish a global sensitivity optimum: additional channels also affect threshold stability, accidental activity, cost, and operation. At sufficiently low expected background counts, these choices must be compared through a counting likelihood with signal acceptance and live time included.

The capture-time study identifies a post-trigger physical tail with a characteristic time of approximately $46.9\text{μs}$. A cumulative fraction of captures in a truth-time interval is distinct from the fraction of reconstructed pulses retained in a finite waveform. This distinction matters for the classifier comparison: the historical flat sample includes a small population of peaks outside the declared acquisition window. Removing their timing and multiplicity contributions leaves the nominal six-observable classifier decisions unchanged in the bounded frozen-model check, but changes decisions in a model that omits the total-energy feature. The stored total-energy scalar cannot be fully corrected without calibrated pulse or waveform information. Consequently, the hierarchy quantifies discrimination in the available response sample; it does not determine an absolute neutron efficiency or a universal ceiling on the benefit of a more complex model.

The IAXO-D0 data provide a direct measurement of the veto performance. In $52.1\mathrm{d}$ of surface data, the prompt veto removes $201/257=78.2\%$ of the Micromegas-selected events. The advanced prompt/post-trigger selection removes a further $7/56=12.5\%$, leaving a background level of $({8.56}_{-1.91}^{+2.30})\times {10}^{-7}$ $\text{counts}{\text{keV}}^{-1}{\text{cm}}^{-2}{\mathrm{s}}^{-1}$ at 90% confidence. The complete veto retains 97.0% of the Micromegas-selected calibration sample. This establishes additional background rejection beyond the prompt selection. It neither identifies the seven rejected events as neutrons nor validates the later simulated classifier hierarchy on the same physical population. A neutron-efficiency measurement would require a matched source or an independently constrained mixture of backgrounds.

Background inventory and detector-response validation

The IAXO-D1 inventory separates the physical source strength from the probability that an incident particle produces a selected event. Its deterministic baseline requires a reconstructed energy of $2\text{–}7\text{keV}$, one valid track in each projected view, and a reconstructed track center within $10\text{mm}$ of the detector center. The source calculations then apply the calibrated TPC BDT where matching response and independent evaluation samples are available, retaining the baseline results for comparison. This additional rejection is a simulation prediction; its transfer to laboratory data remains unvalidated. Both stages depend on diffusion, charge sharing, gain, noise and clustering, so their efficiencies require physical response checks.

The topology-transfer studies demonstrate why this distinction is necessary. The frozen classifier accepts 80.22% of a held-out simulated ${}^{55}\text{Fe}$ sample but only 13.85% of the measured R02756 transfer sample. One candidate from a separate family of revised classifiers reaches its nominal calibration working point while retaining $50/83=60.24\%$ of the independent background candidates. No candidate meets the declared validation requirements, and the reserved blind samples remain unopened. These negative results prevent simulated discrimination performance from being counted as an experimentally supported reduction of the background. Further classifier development is optional; validation of the deterministic response remains necessary.

The uniform-X-ray simulation provides the incident-photon denominator for the reference response, including losses before successful reconstruction. The historical selection accepts 24.49% of the flat $2\text{–}7\text{keV}$ incident sample under its specified illumination and response model. Recomputation with the final baseline remains pending, as explained in Section 5.1.7. This IAXO-D1 result is therefore a response diagnostic; an axion-spectrum-weighted BabyIAXO efficiency remains to be determined. The latter requires a response resolved in incident energy and position, folded with the solar spectrum and optical image. Absorption, geometric acceptance, and reconstruction losses already contained in that response must not be applied a second time.

We now report absolute partial source estimates for neutrons, photons, electrons/positrons and protons, with their generated-parent exposure and conservative veto accounting. The CRY and inclusive HENSA neutron hypotheses remain alternatives, while neither muon sign has qualified exposure in this evaluation. Unresolved coincidences remain selected; zero-count sources retain positive conditional limits. Completion and response-selection bias, source models and response transfer remain outside the quoted Monte Carlo uncertainties. These estimates and the radioactive-inventory subtotal do not establish a complete detector total.

Neutron-induced activation

Neutron-induced activities enter the same decay-response calculation as screened material contamination, with the irradiation history and source placement specified separately. The adopted cobalt-in-copper estimate at formal equilibrium is $2.31\times {10}^{-10}$ $\text{counts}{\text{keV}}^{-1}{\text{cm}}^{-2}{\mathrm{s}}^{-1}$, after baseline and BDT and before veto (Section 5.2.2). It remains a partial modeled estimate; incomplete response coverage and unquantified source uncertainties prevent a full activation prediction. Overlap with screened ${}^{60}\text{Co}$ must also be resolved before adding it to the material subtotal.

A later response cohort selected ${}^{62}\text{Cu}$ and ${}^{64}\text{Cu}$ as well as cobalt, but incomplete source cells and excluded response segments prevent it from replacing the adopted estimate. The cobalt-only survivors therefore characterize the reference calculation rather than all activation products. The half-lives control buildup and cooldown; they do not by themselves determine saturation activity or selected background. No prompt-veto rejection is assigned to decays occurring after their production shower.

Priorities for completing the prediction

The existing measurements are sufficient for useful response tests without expanding the experimental data set. The first priorities are to complete the missing source exposure and quantify response-transfer and sample-selection effects. Small controlled simulations should then test secondary-production threshold convergence, gas and scintillator quenching, thermal-neutron treatment, capture cascades, and the effect of retaining full cosmic-shower correlations. The same frozen reconstruction and selection should be applied to each variation so that changes in accepted yield have a physical interpretation.

Response studies should test calibrated pulse amplitudes and native acquisition timing, and validate energy migration, tracking, fiducial acceptance, and accidental-veto losses against the available runs. Independent calibration runs should test any response tuning; the final blind blocks should remain reserved for a predeclared evaluation. Only after these checks should larger campaigns target the source components whose statistical uncertainty materially affects the result. Zero-survivor samples should be planned and reported as limits with their generated exposure, rather than as negligible backgrounds.

The principal outcome is a connection between physical background mechanisms, reproducible simulation, and measured detector observables. The IAXO-D0 result demonstrates the value of combining prompt and delayed veto information, while the source and response studies define the conditions under which that strategy can be transferred to IAXO-D1 and BabyIAXO. Completing these source and response checks is necessary for a complete background prediction and for ranking further detector or shielding improvements.