Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.41052

Machine Learning Sensor Fusion for Water Vapor Radiometry based Tropospheric Delay Calibration

T. K. Sridharan·Yoshiharu Asaki·Brian E. Svoboda
View original source ↗Read source document ↗Export metadata ↓

Abstract

The next-generation Very Large Array (ngVLA) intends to use 22 GHz water vapor radiometry (WVR) to correct for tropospheric phase fluctuations in its high-frequency bands. However, current delay-retrieval methods do not reach the required accuracy and reliability. As a remedy, we propose a machine-learning based retrieval concept and a preliminary architecture, framed as heterogeneous, multi-rate sensor fusion. Atmospheric reanalysis profiles and surface weather set the context in which per-antenna WVR spectra are used to estimate excess path by a Kalman filter whose parameters are learned from high-cadence calibrator phases. The method is being developed, with planned first validation on the 183 GHz WVR system of the Atacama Large Millimeter/submillimeter Array (ALMA).

Explore related subjects

astro-ph.IMphysics.ao-phphysics.data-anphysics.ins-det

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

T. K. Sridharan, Yoshiharu Asaki, Brian E. Svoboda. 2026-10-29. Machine Learning Sensor Fusion for Water Vapor Radiometry based Tropospheric Delay Calibration. https://arxiv.org/abs/2610.41052

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Geometrical and Optical Design of the GUEST Laser-Ranged Test Masses for the Detection of Gravitational Waves in the $μ$Hz range

The GUEST (Gravitational Universe Exploration with Satellite Tracking) mission aims at detecting gravitational waves in the yet almost unexplored $μ$Hz frequency range through high-precision orbital tracking. The unprecedented scientific objectives of this experiment demand equally advanced optical solutions: the highly eccentric orbit imposes dual measurement regimes; a low-velocity, long-range scenario at apogee and a high-velocity, short-range scenario at perigee. The substantial disparity between these conditions is addressed through a novel optical architecture incorporating two distinct types of Corner-Cube Retroreflectors (CCRs) with 1" and 2" apertures (CCR1 and CCR2, respectively). The proposed layout has been designed to ensure isotropy and homogeneity of the optical response while maintaining efficient packing over the spherical surface of the spacecraft. The resulting Optical Cross-Section (OCS) has been evaluated using a custom reduced-order model, which captures the behavior of individual CCRs under varying illumination conditions. This model has been calibrated by reconstructing the optical response of the LARES-2 satellite and has enabled a detailed assessment of the contributions of each CCR type along the GUEST orbit. Finally, sensitivity analyses have been conducted to investigate the impact of key design parameters, including the ratio between CCR types, laser wavelength, spacecraft diameter, Dihedral Angle Offsets (DAOs), and mechanical configurations.

astro-ph.IM↗
arXiv2026

COSMIO: A Benchmark for Cross-Survey Modality Imputation

Large-scale astronomical surveys define a heterogeneous, partially observed multi-survey learning setting: each object is observed by only a subset of surveys, while survey observations differ in resolutions, wavelength coverage, noise characteristics, and footprints. This setting gives rise to a natural machine learning problem: given observations of an object in some surveys, how to infer its appearance in other surveys. We formalize this as the problem of cross-survey modality imputation and introduce COSMIO, a multi-survey benchmark of aligned observations obtained by forward-rendered shared astrophysical scenes across LSST, Euclid, and Roman under survey-specific instrument characteristics. The benchmark enables systematic evaluation of imputation across source-target survey combinations and missing-survey patterns. We evaluate a broad range of machine learning methods for imputation, which reveal a clear domain gap: methods developed for existing imputation settings transfer poorly to the survey setting. We identify rare-event imputation as a central challenge. This regime arises in the early stages of new surveys, when rare but scientifically valuable phenomena may be available only from prior surveys. We use strong gravitational lensing, where light from a background galaxy is gravitationally deflected by a foreground galaxy, as a representative rare-event setting. We evaluate model performance on zero-shot imputation of gravitational lenses and on downstream scientific tasks using the imputed lens observations. On our constructed evaluation set of galaxy-galaxy strong lenses, performance degrades under rare-event distribution shift, and this degradation further propagates to the downstream lens-detection task. Code: https://github.com/fw-ic/COSMIO.

astro-ph.IM↗
arXiv2026

Photometric calibration of the 4m ILMT data

The International Liquid Mirror Telescope (ILMT) is a 4-meter survey telescope continuously observing towards the zenith in the SDSS g', r', and i' bands. This survey telescope is designed to detect various astrophysical transients (for example, supernovae) and very faint objects like multiply-imaged quasars and low surface brightness galaxies. Ensuring accurate and stable calibration of supernova (SN) and quasar measurements is essential for studying their intrinsic variability and long-term brightness evolution. In this work, we present a dedicated calibration approach tailored for ILMT point-like object observations obtained in Time Delayed Integration (TDI) mode. Using ZTF reference sources cross-matched with the Pan-STARRS1 catalog, we derive robust photometric zero-points along the ILMT scan. This calibration procedure enables the production of consistent, high-precision SN and quasar light curves, suitable for variability studies, structure-function analysis, and future reverberation-mapping applications.

astro-ph.IM↗