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Data-driven weather forecast based on machine learning (ML) has experienced rapid development and demonstrated superior performance in the global medium-range forecast compared to traditional physics-based dynamical models. However, most of…

Machine Learning · Computer Science 2024-08-19 Wanghan Xu , Kang Chen , Tao Han , Hao Chen , Wanli Ouyang , Lei Bai

We investigate correlations induced by gravitational lensing on simulated cosmic microwave background data of experiments with an incomplete sky coverage and their effect on inferences from the South Pole Telescope data. These correlations…

Cosmology and Nongalactic Astrophysics · Physics 2019-01-09 Pavel Motloch , Wayne Hu

In recent years, breakthroughs in methods and data have enabled gravitational time delays to emerge as a very powerful tool to measure the Hubble constant $H_0$. However, published state-of-the-art analyses require of order 1 year of expert…

We present the development of a data-driven, AI-based model of the Point Spread Function (PSF) that achieves higher accuracy than the current state-of-the-art approach, "PSF in the Full Field-of-View'' (PIFF). PIFF is widely used in leading…

Instrumentation and Methods for Astrophysics · Physics 2026-02-18 Dayana Andrea Henao Arbeláez , Pierre-François Léget , Andrés Alejandro Plazas Malagón

The Shear TEsting Programme (STEP) is a collaborative project to improve the accuracy and reliability of weak lensing measurement, in preparation for the next generation of wide-field surveys. We review sixteen current and emerging shear…

Parameter Efficient Fine Tuning (PEFT) techniques have recently experienced significant growth and have been extensively employed to adapt large vision and language models to various domains, enabling satisfactory model performance with…

Computer Vision and Pattern Recognition · Computer Science 2023-11-28 Mohamad Hasan Zahweh , Hasan Nasrallah , Mustafa Shukor , Ghaleb Faour , Ali J. Ghandour

The weak-lensing science of the LSST project drives the need to carefully model and separate the instrumental artifacts from the intrinsic lensing signal. The dominant source of the systematics for all ground based telescopes is the spatial…

Instrumentation and Methods for Astrophysics · Physics 2015-05-20 M. James Jee , J. Anthony Tyson

Novel methods for rapidly estimating single-photon source (SPS) quality have been promoted in recent literature to address the expensive and time-consuming nature of experimental validation via intensity interferometry. However, the…

Shack-Hartmann wavefront sensing relies on accurate spot centre measurement. Several algorithms were developed with this aim, mostly focused on precision, i.e. minimizing random errors. In the solar and extended scene community, the…

Instrumentation and Methods for Astrophysics · Physics 2018-02-07 Narsireddy Anugu , Paulo J. V. Garcia , Carlos M. Correia

The precise knowledge of the positions of space debris objects and in particular of defunct satellites is fundamental for satellite operations. Several studies showed that it is possible to improve the accuracy of the orbit determination…

Earth and Planetary Astrophysics · Physics 2019-03-04 E. Cordelli , P. Schlatter , P. Lauber , T. Schildknecht

Over the last few years increasing consideration has been given to the study of Laser Guide Stars (LGS) for the measurement of the disturbance introduced by the atmosphere in optical and near-infrared astronomical observations from the…

Instrumentation and Methods for Astrophysics · Physics 2015-05-13 L. Schreiber , I. Foppiani , C. Robert , E. Diolaiti , J. -M. Conan , M. Lombini

In rectangular-target pointing, movement angles towards targets are known to affect error rates. When designers determine target sizes, however, they would not know the frequencies of cursor-approaching directions for each target. Thus,…

Human-Computer Interaction · Computer Science 2023-02-08 Shota Yamanaka

We present a method to derive the relative pointing offsets for SPIRE Fourier-Transform Spectrometer (FTS) solar system object (SSO) calibration targets, which were observed regularly throughout the Herschel mission. We construct ratios of…

We address the problem of uncertainty calibration and introduce a novel calibration method, Parametrized Temperature Scaling (PTS). Standard deep neural networks typically yield uncalibrated predictions, which can be transformed into…

Machine Learning · Computer Science 2022-09-20 Christian Tomani , Daniel Cremers , Florian Buettner

The precision of Cosmic Microwave Background (CMB) experiments, specifically its lensing reconstruction, has reached the limit where non-linear corrections cannot be ignored. Neglecting these corrections results in biased constraints on…

Cosmology and Nongalactic Astrophysics · Physics 2025-02-27 Cynthia Trendafilova , Ali Rida Khalife , Silvia Galli

Flexible pick-and-place is a fundamental yet challenging task within robotics, in particular due to the need of an object model for a simple target pose definition. In this work, the robot instead learns to pick-and-place objects using…

Robotics · Computer Science 2020-06-16 Lars Berscheid , Pascal Meißner , Torsten Kröger

Accurate measurement of polarization in spectral lines is important for the reliable inference of magnetic fields on the Sun. For ground based observations, polarimetric precision is severely limited by the presence of Earth's atmosphere.…

Instrumentation and Detectors · Physics 2012-11-05 K. Nagaraju , Alex Feller

For certain manipulation tasks, object pose estimation from head-mounted cameras may not be sufficiently accurate. This is at least in part due to our inability to perfectly calibrate the coordinate frames of today's high degree of freedom…

Robotics · Computer Science 2022-04-12 Patrick Lancaster , Boling Yang , Joshua R. Smith

Temperature scaling is a popular technique for tuning the sharpness of a model distribution. It is used extensively for sampling likely generations and calibrating model uncertainty, and even features as a controllable parameter to many…

Machine Learning · Computer Science 2023-10-03 Andy Shih , Dorsa Sadigh , Stefano Ermon