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.

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Publication Details

Journal
Scientific Reports
Published
2025-10-02
DOI
https://doi.org/10.1038/s41598-025-16732-9
Citations
2
Primary Topic
Radio Astronomy Observations and Technology
Type
article
Field-Weighted Citation Impact
2.31
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article

Radiometer calibration using machine learning

Eloy de Lera Acedo, I. L. V. Roque, P. Daniel Meerburg, Jonathan R. Pritchard et al.
2 citations
Scientific Reports
Radio Astronomy Observations and Technology
2.31
article

Radiometer calibration using machine learning

Eloy de Lera Acedo, I. L. V. Roque, P. Daniel Meerburg, Jonathan R. Pritchard, Anastasia Fialkov, E. Shen, Riccardo Chiello, G. Bernardi, John Cumner, Dirk I. L. de Villiers, Saurabh Pegwal, C. Kirkham, Peter Sims, Dominic Anstey, S. A. K. Leeney, Anchal Saxena, N. Razavi‐Ghods, K. H. Scheutwinkel, Paul F. Scott, T. Gessey-Jones, Dávid Molnár, Shikhar Mittal, Ewald Puchwein, M. Spinelli, H. T. J. Bevins, G. Kulkarni, Rutul Patel, Will Handley, Jean Cavillot, Alessio Magro, W. Croukamp, Joe H. N. Pattison, Kaan Artuc, Steven Carey, Jin Zhu, J. A. Ely, C. Pieterse, M. Bucher
article en
2 citations

Abstract

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.

Scientific ReportsVol. 15(1)
Tata Institute of Fundamental Research (IN), University of Groningen (NL), University of Malta (MT), Université Paris Cité (FR), University of Cambridge (GB), Stellenbosch University (ZA), Leibniz Institute for Astrophysics Potsdam (DE), Laboratoire AstroParticule et Cosmologie (FR), University of Oxford (GB), Istituto di Radioastronomia di Bologna (IT), South African Radio Astronomy Observatory (ZA), Institute of Astronomy (RU), Observatoire de la Côte d’Azur (FR), Science Oxford (GB), University of Chinese Academy of Sciences (CN), Imperial College London (GB), Arizona State University (US), UCLouvain (BE)
Openalex Percentile: Top 10%
Radio Astronomy Observations and Technology
2.31
DOIOpen Access PDFOpenAlex
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