Self-Supervised Calibration of Scientific Instruments Using Physical Consistency Constraints
arXiv:2606.29466v1 Announce Type: new Abstract: Calibration remains one of the principal obstacles to the deployment of machine learning in scientific instrumentation because it typically relies on expert intervention, dedicated procedures, and manually labelled data. We introduce a physics-informed self-supervised framework that jointly learns latent detector calibration parameters and task-specific predictions directly from raw measurements without requiring pre-calibrated signals or external labels. The method exploits known physical constraints to generate pseudo-labels iteratively, transf
The increasing complexity of scientific instrumentation and the maturity of self-supervised learning techniques are converging to address long-standing calibration challenges.
This breakthrough significantly reduces the need for costly, time-consuming, and error-prone manual calibration in scientific machine learning, accelerating research, and improving data reliability.
Machine learning models in scientific contexts can now be deployed more rapidly and robustly without extensive pre-calibration, lowering barriers to AI adoption in labs and observatories.
- · Scientific instrument manufacturers
- · Physics researchers
- · AI/ML in scientific discovery
- · Healthcare diagnostics
- · Traditional calibration service providers
- · Manual data labeling firms
Scientific experiments previously limited by calibration complexity can now be conducted with greater ease and precision.
Faster deployment of AI in scientific instruments could lead to accelerated discovery in fields like material science, medical imaging, and fundamental physics.
The reduced overhead for instrument calibration might enable the proliferation of more specialized and sensitive scientific sensors in various applications, increasing data volume and quality across scientific domains.
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Read at arXiv cs.LG