Radiometer Calibration using Machine Learning

Radiometers are crucial instruments in radio astronomy, forming the primary component of nearly all radio telescopes. They measure the intensity of electromagnetic radiation, converting this radiation into electrical signals. A radiometer's primary components are an antenna and a Low Noise Amplifier (LNA), which is the core of the ``receiver'' chain. Instrumental effects introduced by the receiver are typically corrected or removed during calibration. However, impedance mismatches between the antenna and receiver can introduce unwanted signal reflections and distortions. Traditional calibration methods, such as Dicke switching, alternate the receiver input between the antenna and a well-characterised reference source to mitigate errors by comparison. Recent advances in Machine Learning (ML) offer promising alternatives. Neural networks, which are trained using known signal sources, provide a powerful means to model and calibrate complex systems where traditional analytical approaches struggle. These methods are especially relevant for detecting the faint sky-averaged 21-cm signal from atomic hydrogen at high redshifts. This is one of the main challenges in observational Cosmology today. Here, for the first time, we introduce and test a machine learning-based calibration framework capable of achieving the precision required for radiometric experiments aiming to detect the 21-cm line.

@article{leeney2025_2504.16791,
  title={ Radiometer Calibration using Machine Learning },
  author={ S. A. K. Leeney and H. T. J. Bevins and E. de Lera Acedo and W. J. Handley and C. Kirkham and R. S. Patel and J. Zhu and D. Molnar and J. Cumner and D. Anstey and K. Artuc and G. Bernardi and M. Bucher and S. Carey and J. Cavillot and R. Chiello and W. Croukamp and D. I. L. de Villiers and J. A. Ely and A. Fialkov and T. Gessey-Jones and G. Kulkarni and A. Magro and P. D. Meerburg and S. Mittal and J. H. N. Pattison and S. Pegwal and C. M. Pieterse and J. R. Pritchard and E. Puchwein and N. Razavi-Ghods and I. L. V. Roque and A. Saxena and K. H. Scheutwinkel and P. Scott and E. Shen and P. H. Sims and M. Spinelli },
  journal={arXiv preprint arXiv:2504.16791},
  year={ 2025 }
}