Summary and Conclusions

This thesis develops the tools and physical interpretation needed to model backgrounds in the IAXO Micromegas detector program. Its main contributions are a simulation and reconstruction workflow, 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 the future BabyIAXO calculation, whose xenon–neon response, optics, site environment, and final geometry must be evaluated explicitly.

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 at large simulation volumes while preserving the connection between physical inputs and reconstructed events. 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 is valuable because it exposes disagreements in the quantities actually used for 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. Preserved transport 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 diminishing increment 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 $46.9\text{mus}$. 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 measured IAXO-D0 result is stronger and more specific. 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 does not identify the seven rejected events as neutrons, nor validate 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 reference selection 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. No learned topology rejection is credited. The retained energy, tracking, and fiducial requirements nevertheless depend on diffusion, charge sharing, gain, noise, and clustering; their efficiency remains part of the physical response to be validated.

The topology-transfer studies demonstrate why this distinction is necessary. The frozen candidate accepts 80.22% of an untouched simulated ${}^{55}\text{Fe}$ sample but only 13.85% of the measured R02756 transfer sample. A separate repair family reaches its nominal calibration working point for one candidate 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 selection accepts 24.49% of the flat $2\text{–}7\text{keV}$ incident sample under its specified illumination and response model. This number is a simulation result for IAXO-D1, rather than an axion-spectrum-weighted BabyIAXO efficiency. 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.

The source audit identifies specific conditions needed to convert simulated yields into absolute background levels. Muon exposure requires the production geometry and the same angular and flux conventions as the source integral. The HENSA neutron field requires a consistent scalar-fluence measure, energy support, and generated-primary ledger. Gas and radon contributions require generated decay counts and a declared decay-chain convention; saved detector entries are not a substitute for that denominator. The remaining source records are therefore retained with explicit inclusion status instead of being assigned zero contribution or combined into a nominal total.

Neutron-induced activation

Activation is treated separately because the subsequent radioactive decay can fall outside the prompt veto window. A production inventory of $1,390,753$ generated neutrons is coupled to broad and targeted decay-response simulations totaling $1.035\times {10}^9$ decays. The selected dedicated response contains 54 ${}^{60}\text{Co}$ and 19 ${}^{58}\text{Co}$ events. These counts establish simulated decay acceptances for the sampled materials and spatial distributions; an absolute production rate additionally requires the incident-neutron exposure to be validated. Coverage of most produced nuclei does not guarantee coverage of the accepted low-energy background, because rare products can have a larger detection probability.

The time dependence also separates isotope production from detector response. For constant production without feeding, the activity approaches the production rate at saturation, while the half-life controls the approach to saturation and the subsequent cooldown. Thus, the long half-life of ${}^{60}\text{Co}$ explains its persistence but does not itself increase its saturation activity. The final activation prediction must combine spatial production yields, decay acceptances, irradiation history, and the contribution of unmodeled products. No prompt-veto rejection is assigned to this delayed component.

Priorities for completing the prediction

The existing measurements are sufficient for useful response tests without expanding the experimental data set. The first priority is to recover immutable production metadata and close the source integrals before increasing Monte Carlo statistics. Small controlled simulations should then test production-cut 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.

The next priority is to replay the preserved detector information with a consistent waveform window and calibrated pulse energies, and to 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 those conditions will turn the present source inventory into a defensible absolute prediction and identify which further detector or shielding improvements provide the largest gain in helioscope sensitivity.