Related papers: Pointing Accuracy Improvements for the South Pole …

The South Pole Telescope Summertime Line Intensity Mapper (SPT-SLIM) is a pathfinder experiment that will demonstrate the use of on-chip filter-bank spectrometers for mm-wave line intensity mapping (LIM). The SPT-SLIM focal plane consists…

We investigate the use of a point cloud measurement in terrain-aided navigation. Our goal is to aid an inertial navigation system, by exploring ways to generate a useful measurement innovation error for effective nonlinear state estimation.…

We investigate the cross-calibration of the Hinode/SOT-SP and SDO/HMI instrument meta-data, specifically the correspondence of the scaling and pointing information. Accurate calibration of these datasets gives the correspondence needed by…

We present the design and science goals of SPT-3G+, a new camera for the South Pole Telescope, which will consist of a dense array of 34100 kinetic inductance detectors measuring the cosmic microwave background (CMB) at 220 GHz, 285 GHz,…

The forecast on a time scale of 1 or 2 hours is crucial for all kind of new generation facilities (ELTs) instrumentation supported by the adaptive optics that will be mainly operated in Service Mode. In a recent study (Masciadri et al.…

Expanding from previous work we present weak lensing measurements for a total sample of 30 distant ($z_\mathrm{median}=0.93$) massive galaxy clusters from the South Pole Telescope Sunyaev-Zel'dovich (SPT-SZ) Survey, measuring galaxy shapes…

This overview paper details the findings from the Diving Deep: Forecasting Sea Surface Temperatures and Anomalies Challenge at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML…

We present the activities carried out to calibrate and characterise the performance of the elements of attitude control and measurement on board the Herschel spacecraft. The main calibration parameters and the evolution of the indicators of…

SPOrt is an ASI-funded experiment specifically designed to measure the sky polarization at 22, 32 and 90 GHz, which was selected in 1997 by ESA to be flown on the International Space Station. Starting in 2006 and for at least 18 months, it…

We showcase machine learning (ML) inspired target selection algorithms to determine which of all potential targets should be selected first for spectroscopic follow up. Efficient target selection can improve the ML redshift uncertainties as…

Even though the technology of adaptive optics (AO) is rapidly maturing, calibration of the resulting images remains a major challenge. The AO point-spread function (PSF) changes quickly both in time and position on the sky. In a typical…

Frequency-domain multiplexing (fMux) is an established technique for the readout of large arrays of transition edge sensor (TES) bolometers. Each TES in a multiplexing module has a unique AC voltage bias that is selected by a resonant…

The increasing volume of space objects in Earth's orbit presents a significant challenge for Space Situational Awareness (SSA). And in particular, accurate orbit prediction is crucial to anticipate the position and velocity of space…

Deep learning models for meteorological forecasting often fail in rare but high-impact events such as typhoons, where relevant data is scarce. Existing fine-tuning methods typically face a trade-off between overlooking these extreme events…

While self-supervised pretraining has proven beneficial for many computer vision tasks, it requires expensive and lengthy computation, large amounts of data, and is sensitive to data augmentation. Prior work demonstrates that models…

The performance of adaptive optics systems is partially dependant on the algorithms used within the real-time control system to compute wavefront slope measurements. We demonstrate use of a matched filter algorithm for the processing of…

We address the challenge of accurate 3D human pose and shape estimation from monocular images. The key to accuracy and robustness lies in high-quality training data. Existing training datasets containing real images with pseudo ground truth…

Machine learning can accelerate cosmological inferences that involve many sequential evaluations of computationally expensive data vectors. Previous works in this series have examined how machine learning architectures impact emulator…

Accurate astrometry and photometry of saturated and coronagraphic point spread functions (PSFs) are fundamental to both ground- and space-based high contrast imaging projects. For ground-based adaptive optics imaging, differential…

The evaluation of modelled or satellite-derived soil moisture (SM) estimates is usually dependent on comparisons against in-situ SM measurements. However, the inherent mismatch in spatial support (i.e., scale) necessitates a cautious…