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New SAEM algorithm enhances radio interferometry imaging robustness

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new robust estimation scheme for linear state-space models, particularly addressing challenges in radio interferometry where radio-frequency interference (RFI) can cause heavy-tailed or outlier-prone measurement noise. The proposed method utilizes a Stochastic Approximation Expectation-Maximization (SAEM) algorithm, replacing the standard E-step with Monte Carlo sampling for latent states and noise texture. This approach enables tractable inference even with heavy-tailed likelihoods, significantly improving reconstruction fidelity and robustness to RFI compared to traditional Gaussian EM algorithms. AI

IMPACT This research could lead to more accurate and reliable imaging in radio astronomy and other fields affected by noisy data.

RANK_REASON The cluster contains a research paper detailing a new algorithm for state-space models.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New SAEM algorithm enhances radio interferometry imaging robustness

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The cluster contains a research paper detailing a new algorithm for state-space models.
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COVERAGE [2]

  1. arXiv stat.ML TIER_1 English(EN) · Nawel Arab, Mohammed Nabil El Korso, Isabelle Vin, Pascal Larzabal ·

    Stochastic Expectation Maximization for Robust State-Space Radio Interferometric Imaging

    arXiv:2606.23944v1 Announce Type: cross Abstract: State--space models provide a flexible framework for analyzing dynamical systems, yet they often rely on Gaussian assumptions that fail to capture heavy-tailed or outlier-prone measurement noise. We propose a robust estimation sch…

  2. arXiv stat.ML TIER_1 English(EN) · Pascal Larzabal ·

    Stochastic Expectation Maximization for Robust State-Space Radio Interferometric Imaging

    State--space models provide a flexible framework for analyzing dynamical systems, yet they often rely on Gaussian assumptions that fail to capture heavy-tailed or outlier-prone measurement noise. We propose a robust estimation scheme for linear state--space models subject to comp…

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