Learning What's Real: Disentangling Signal and Measurement Artifacts in Multi-Sensor Data, with Applications to Astrophysics
arXiv:2604.09787v2 Announce Type: replace-cross Abstract: Data collected from the physical world is always a combination of multiple sources: an underlying signal from the physical process of interest and a signal from measurement-dependent artifacts from the sensor or instrument. This secondary signal acts as a confounding factor, limiting our ability to extract information about the physics underlying the phenomena we observe. Furthermore, it complicates the combination of observations in heterogeneous or multi-instrument settings. We propose a deep learning framework that leverages overlapp
The proliferation of increasingly complex multi-sensor data sets, particularly in fields like astrophysics, necessitates advanced methods for data interpretation, and deep learning techniques have matured sufficiently to address this challenge.
This development enhances the ability to extract accurate information from noisy or artifact-laden scientific data, improving the reliability of discoveries and accelerating scientific progress in data-heavy domains.
The ability to automatically disentangle true physical signals from measurement artifacts will significantly improve data quality and reduce manual preprocessing, making multi-sensor data more actionable across various scientific and engineering applications.
- · Astrophysicists
- · Deep Learning researchers
- · Scientific instrument manufacturers
- · AI/ML-driven analytics platforms
- · Traditional data cleaning methodologies
- · Research reliant solely on single-sensor data
Improved accuracy and efficiency in scientific data analysis, particularly in astrophysics.
Accelerated discovery of new astrophysical phenomena and more robust scientific theories due to clearer data interpretation.
The methodology could generalize to other multi-sensor data problems beyond astrophysics, such as medical imaging or climate modeling, fostering cross-disciplinary AI applications.
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Read at arXiv cs.LG