Calibrated Multi-Signal Auditing Pipeline

• The paper introduces a calibrated multi-signal auditing pipeline that robustly assesses heterogeneous data streams using statistical and Bayesian methods.
• It employs multi-level quality control, advanced error modeling, and periodic reference injection to detect and rectify systematic errors.
• It integrates sensor fusion, parallel processing, and reproducible workflows to support applications in radio astronomy, medical analysis, and AI governance.

A calibrated multi-signal auditing pipeline is a structured workflow designed to robustly assess, process, and assure the scientific utility of multiple, heterogeneous data streams—such as time-series, images, or spectra—by integrating signal extraction, calibration, multi-level quality control, and rigorous error modeling. Originating from requirements in radio astronomy, medical signal analysis, sensor fusion, fairness auditing, and LLM governance, these pipelines employ algorithmic, statistical, and information-theoretic strategies to detect, flag, and rectify systematic errors, silent failures, cross-channel contamination, or covert collusion, with stringent guarantees on calibration accuracy and auditability.

1. Foundational Principles

Calibrated multi-signal auditing pipelines operate on several foundational principles:

• Independence and Heterogeneity: Signals across spatial, temporal, spectral, modality, or agent domains are handled independently for calibration and flagging, enabling tailored modeling of each signal's unique systematic and stochastic effects (e.g., per-dish, per-polarisation, per-channel calibration in MeerKAT HI mapping (Wang et al., 2020); multi-lead, multi-modality SQI assessment in time-series (Gao et al., 1 Feb 2024)).
• Error Propagation and Modeling: Calibration steps are linked to formal noise models and error budgets. For example, the radiometer equation estimates map noise levels in HI mapping, while quality indices in time-series analysis propagate denoising and reliability metrics to downstream classifiers.
• Multi-Level Flagging and QA: Robust multi-stage RFI flagging, outlier detection, and map-based rejection are standard (MeerKAT pipeline: three rounds, 35%+ data excised), with automated quality control and residual analysis to validate calibration integrity.
• Statistical and Bayesian Fitting: Signal extraction is performed using statistical or Bayesian frameworks with physically motivated priors and likelihoods, facilitating unified treatment of instrumental and celestial/background models.

2. Algorithmic Calibration Workflows

Key methodologies characteristic of these pipelines include:

• Periodic Reference Injection: Routine insertion of reference signals (e.g., noise diodes in MeerKAT; phase combs in radio astronomy (Wagner et al., 10 Jan 2025)) provides stable benchmarks for gain drift correction and delay/phase calibration. The associated mathematical models (e.g., per-bin delay accumulation) translate physical effects into quantifiable error correction.
• Multi-component Sky or System Model Fitting: Observed data are modeled as superpositions of physically and instrumentally motivated components; joint optimization (often Bayesian) yields time/frequency-dependent calibration parameters,

$$T_{\mathrm{model}}(t, \nu) = T_{\mathrm{ps}} + T_{\mathrm{diffuse}} + T_{\mathrm{el}} + T_{\mathrm{diode}} + T_{\mathrm{rec}}$$

• Advanced Signal Quality Indices: In time-series data, diverse SQIs (beat agreement, kurtosis, inter-channel consistency, entropy) are extracted; their joint distributions serve as features for outlier and failure classification, and signal-specific denoising strategies (wavelet, EMD, CNN autoencoding) are applied as dictated by SNR regime.
• Sensor Fusion and Consensus Methods: Multilevel fusion algorithms—EKF, FUSVAF, distributed consensus—enable reliable reduction and aggregation of sensor network data, with enhanced fault tolerance and situational awareness (Stamatescu, 2015).

3. Auditing and Quality Assurance Mechanisms

Auditing is enforced via multi-stage, cross-modal QA systems:

• Residual Analysis: After calibration, computation of residuals,

$$T_{\text{res}}(t, \nu) = T_{\text{cal}}(t, \nu) - T_{\text{model}}(t, \nu)$$

ensures that final outputs approximate Gaussian noise with minimal bias and sub-percent fractional residuals.

• Automated Report Generation: All pipeline steps record flags for outliers, RFI, calibration error, and model mismatch; quality metrics and browsing products are delivered per signal, processed map, or agent run (e.g., Herschel HIFI pipeline (Shipman et al., 2017); ALMA WebLog (Hunter et al., 2023)).
• Statistical Error-Modeling in Bias Audits: Fairness-oriented pipelines apply statistical tests (parity, intersectional representation) with explicit correction for classifier imprecision, simulating label uncertainty and reporting robust p-values only for persistent, significant subgroup differences (Lafargue et al., 11 Apr 2025).

4. Multi-Signal Integration and Reproducibility

An essential component is the fusion and reproducibility of multi-signal assessments:

• Parallel Processing and SIMD-Optimized Extraction: Signal extraction code for phase cal pipelines uses LUTs or digital oscillators, explicitly paralleling computation across signal types/channels (Wagner et al., 10 Jan 2025), supporting scaling to Gs/s rates on CPU/GPU/FPGA.
• Union Meta-Tests and Multi-Signal Fusion: Auditing frameworks (e.g., Audit the Whisper for multi-agent LLM collusion (Tailor, 5 Oct 2025)) utilize a union meta-test—logical-or of distinct detectors (mutual information, permutation invariance, watermark variance, fairness acceptance)—each individually calibrated to stringent false positive rates and validated across hundreds of independent runs.
• Infrastructure for Reproducibility: Delivered pipelines include regeneration scripts, seed-stamped manifests, and transparent logging/documentation, ensuring end-to-end reproducibility for external auditors and downstream practitioners.

5. Quantitative Performance Guarantees

Pipelines are evaluated according to stringent numerical and statistical standards:

• Calibration Accuracy: Gain and noise-diodes are typically stable to <2.5%, map noise approaches $1.4\times$ thermal noise (MeerKAT), SQI-based classifiers reach up to ~0.88 accuracy on benchmark datasets (Gao et al., 1 Feb 2024).
• Residual Suppression: Residual maps commonly attain $<$1% of model temperature or reach sub-mK accuracy (HI intensity mapping), flagging outliers with statistical rigor.
• Auditing Power and Transferability: In multi-agent LLM audits, union detector TPR = 1.0, FPR = 0.0 at $10^{-3}$ budget; ablation analysis demonstrates each signal addresses distinct collusion strategies, and calibration transfers robustly across environments (Tailor, 5 Oct 2025).
• Error-Aware Fairness Audits: Statistical tests incorporating classifier error perturbation produce more tolerant and accurate bias identification, with significantly lower false positive rates and robust conclusions under limited manual labeling (Lafargue et al., 11 Apr 2025).

6. Domain-Specific Exemplars and Applications

Pipeline architectures are tailored to domain requirements:

• Radio Astronomy: Multi-dish autocorrelation strategies (MeerKAT, HIFI, ALMA) support degree-scale mapping of Galactic HI, full pipeline modularity, and routine calibration at per-dish/channel/polarization granularity; innovations such as spectral renormalization and moment-difference analysis mitigate amplitude and continuum subtraction errors in line-rich datasets (Wang et al., 2020, Hunter et al., 2023, Shipman et al., 2017).
• Medical/Biological Signals: Multi-lead ECG and physiological data are processed with broad-spectrum SQIs and advanced denoising; multi-signal calibration enables robust predictive diagnostics and rescue of artifact-ridden data (Gao et al., 1 Feb 2024).
• Industrial/Robotics: Joint SLAM-based calibration of microphone arrays optimizes multi-array geometric and clock parameters via FIM-based observability analysis and robust initialization strategies, outperforming prior frameworks in speed and accuracy (Wang et al., 30 May 2024).
• Fairness and Governance: Auditing pipelines detect, quantify, and localize multi-group miscalibration with Kuiper statistic-based metrics normalized to signal-to-noise ratio; distance-to-multicalibration frameworks establish both geometric and auditability requirements for robust fairness certification (Derhake et al., 21 Sep 2025, Guy et al., 12 Jun 2025).

7. Limitations and Open Directions

While current pipelines achieve high reproducibility and calibration fidelity, several limitations persist:

• Exponential Complexity in Intersectional Audits: Certifying full multicalibration via intersectional metrics is statistically feasible only for modest subgroup cardinality $|C|$; otherwise, sample complexity is prohibitive (Derhake et al., 21 Sep 2025).
• Sensitivity to Domain Shift: Transfer learning between synthetic and real datasets may require full retraining to avoid classification accuracy drop (e.g., in image fairness audits (Lafargue et al., 11 Apr 2025)).
• Attack Resistance in Governance Pipelines: Adversarially trained models may resist behavioral attacks, limiting efficacy of black-box audit techniques in LLM systems; objective interpretability offers a partial solution (Marks et al., 14 Mar 2025).

A plausible implication is that future pipelines will pursue advanced meta-learning strategies for adaptive error-modeling, scalable multicalibration estimation, and resilience to adversarial or nonstationary environments.

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Pipeline Domain	Core Calibration Approach	Auditing Signals / Metrics
Radio astronomy	Per-channel/beam, Bayesian sky model fit	Residual maps, RFI flagging, external matches
Medical/biological	SQI featurization, multi-lead denoising	Ensemble SQI, classifier-based audit
Sensor fusion	EKF, FUSVAF, consensus	Local fusion, distributed consensus errors
LLM governance	Multi-signal fusion, union meta-test	MI, permutation, watermark, fairness bias
Fairness/image analysis	CNN with error-aware statistics	Parity, entropy, Wasserstein, simulation-based

In summary, calibrated multi-signal auditing pipelines are an essential infrastructure for scientifically robust, application-ready analysis of heterogeneous data streams, integrating error-modeling, multi-level QA, cross-signal fusion, and reproducible benchmarking. Their careful design and calibration underpin trusted signal extraction, artifact mitigation, and decision-making in domains spanning observational science, medicine, industrial monitoring, and AI governance.