Beyond the BLUE I: the advantage ceiling - how much can any estimator beat the matched filter in mm/submm survey data?
Abstract
Papers claiming that convolutional neural networks outperform the matched filter are now common in astronomy. Since that filter is the best linear unbiased estimator (BLUE) for any noise of given covariance, and the minimum-variance unbiased estimator under Gaussian noise, such a claim requires that the noise covariance varies from image to image or the noise is non-Gaussian. We introduce the advantage ceiling eta >= 1, which quantifies both cases: the Fisher information for the amplitude in units of the matched filter's, computable from noise-only simulations before any network is trained, and a bound on the variance of any estimator unbiased at each true amplitude. We compute eta for a suite of millimeter/submillimeter survey noise models and for compact and extended source templates, using a variational score-matching ladder whose rungs are estimator classes of increasing statistical order, from linear to convolutional. With an instrumental white-noise floor, Gaussian noise returns eta = 0.94-1.06 (+-0.04); covariance mixtures give exact ceilings of 10.2 (spectral tilt, extended source), 2.1 (leaked components) and 1.1-1.8 otherwise; residual artifacts give eta <= 1.09. Source confusion gives the largest advantage, eta >= 3.8 (extended) and >= 2.4 (compact). Three unmodelled channels - a zeroed DC mode, a missing white floor and floating-point rounding - can manufacture spurious advantages of up to two orders of magnitude. A ResNet regressor is 6-15% less efficient than the matched filter on Gaussian noise once prior shrinkage is divided out, and realizes a factor 1.8 of the 10.2 available on the spectral-tilt mixture; a companion paper tests such regressors against these ceilings.
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BibTeX
@misc{basu2026beyond,
title = {{Beyond the BLUE I: the advantage ceiling - how much can any estimator beat the matched filter in mm/submm survey data?}},
author = {Kaustuv Basu},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2609.10475},
eprint = {2609.10475},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.10475},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Beyond the BLUE I: the advantage ceiling - how much can any estimator beat the matched filter in mm/submm survey data?
AU - Kaustuv Basu
PY - 2026
DA - 2026/10/15
PB - arXiv
DO - 10.48550/arxiv.2609.10475
UR - https://arxiv.org/abs/2609.10475
AB - Papers claiming that convolutional neural networks outperform the matched filter are now common in astronomy. Since that filter is the best linear unbiased estimator (BLUE) for any noise of given covariance, and the minimum-variance unbiased estimator under Gaussian noise, such a claim requires that the noise covariance varies from image to image or the noise is non-Gaussian. We introduce the advantage ceiling eta >= 1, which quantifies both cases: the Fisher information for the amplitude in units of the matched filter's, computable from noise-only simulations before any network is trained, and a bound on the variance of any estimator unbiased at each true amplitude. We compute eta for a suite of millimeter/submillimeter survey noise models and for compact and extended source templates, using a variational score-matching ladder whose rungs are estimator classes of increasing statistical order, from linear to convolutional. With an instrumental white-noise floor, Gaussian noise returns eta = 0.94-1.06 (+-0.04); covariance mixtures give exact ceilings of 10.2 (spectral tilt, extended source), 2.1 (leaked components) and 1.1-1.8 otherwise; residual artifacts give eta <= 1.09. Source confusion gives the largest advantage, eta >= 3.8 (extended) and >= 2.4 (compact). Three unmodelled channels - a zeroed DC mode, a missing white floor and floating-point rounding - can manufacture spurious advantages of up to two orders of magnitude. A ResNet regressor is 6-15% less efficient than the matched filter on Gaussian noise once prior shrinkage is divided out, and realizes a factor 1.8 of the 10.2 available on the spectral-tilt mixture; a companion paper tests such regressors against these ceilings.
ER -