Zobrazeno 1 - 10
of 9 186
pro vyhledávání: '"Suren, A."'
Autor:
Tartakovsky, Simon, Adler, Alexandre E., Austermann, Jason E., Benton, Steven J., Bihary, Rick, Durking, Malcolm, Duff, Shannon M., Filippini, Jeffrey P., Fraisse, Aurelien A., Gascard, Thomas J. L. J., Gibbs, Sho M., Gourapura, Suren, Gudmundsson, Jon E., Hartley, John W., Hubmayr, Johannes, Jones, William C., Li, Steven, May, Jared L., Nagy, Johanna M., Okun, Kate, Padilla, Ivan L., Romualdez, L. Javier, Vissers, Michael R.
Publikováno v:
SPIE 2024 Conference 13094, Paper 13094-112
We describe the cryogenic system being developed for Taurus: a super-pressure balloon-borne microwave polarimeter scheduled to fly in 2027. The Taurus cryogenic system consists of a 660L liquid helium cryostat which achieves a base temperature of <10
Externí odkaz:
http://arxiv.org/abs/2410.18150
Inverse Synthetic Aperture Radar (ISAR) imaging presents a formidable challenge when it comes to small everyday objects due to their limited Radar Cross-Section (RCS) and the inherent resolution constraints of radar systems. Existing ISAR reconstruct
Externí odkaz:
http://arxiv.org/abs/2410.10085
Publikováno v:
Optics Express 30, 40854-40870 (2022)
Images captured from a long distance suffer from dynamic image distortion due to turbulent flow of air cells with random temperatures, and thus refractive indices. This phenomenon, known as image dancing, is commonly characterized by its refractive-i
Externí odkaz:
http://arxiv.org/abs/2408.16623
Autor:
May, Jared L., Adler, Alexandre E., Austermann, Jason E., Benton, Steven J., Bihary, Rick, Durkin, Malcolm, Duff, Shannon M., Filippini, Jeffrey P., Fraisse, Aurelien A., Gascard, Thomas J. L. J., Gibbs, Sho M., Gourapura, Suren, Gudmundsson, Jon E., Hartley, John W., Hubmayr, Johannes, Jones, William C., Li, Steven, Nagy, Johanna M., Okun, Kate, Padilla, Ivan L., Romualdez, L. Javier, Tartakovsky, Simon, Vissers, Michael R.
Taurus is a balloon-borne cosmic microwave background (CMB) experiment optimized to map the E-mode polarization and Galactic foregrounds at the largest angular scales ($\ell$ $\lt$ 30) and improve measurements of the optical depth to reionization ($\
Externí odkaz:
http://arxiv.org/abs/2407.01438
"Garbage In Garbage Out" is a universally agreed quote by computer scientists from various domains, including Artificial Intelligence (AI). As data is the fuel for AI, models trained on low-quality, biased data are often ineffective. Computer scienti
Externí odkaz:
http://arxiv.org/abs/2406.19256
Autor:
Adler, Alexandre E., Austermann, Jason E., Benton, Steven J., Duff, Shannon M., Filippini, Jeffrey P., Fraisse, Aurelien A., Gascard, Thomas, Gibbs, Sho M., Gourapura, Suren, Hubmayr, Johannes, Gudmundsson, Jon E., Jones, William C., May, Jared L., Nagy, Johanna M., Okun, Kate, Padilla, Ivan, Rooney, Christopher, Tartakovsky, Simon, Vissers, Michael R.
Publikováno v:
JCAP09(2024)061
We simulate a variety of optical systematics for Taurus, a balloon-borne cosmic microwave background (CMB) polarisation experiment, to assess their impact on large-scale E-mode polarisation measurements and constraints of the optical depth to reionis
Externí odkaz:
http://arxiv.org/abs/2406.11992
Given the rapid improvement of the detectors at high-energy physics experiments, the need for real-time data monitoring systems has become imperative. The significance of these systems lies in their ability to display experiment status, steer softwar
Externí odkaz:
http://arxiv.org/abs/2405.14397
Tackling image degradation due to atmospheric turbulence, particularly in dynamic environment, remains a challenge for long-range imaging systems. Existing techniques have been primarily designed for static scenes or scenes with small motion. This pa
Externí odkaz:
http://arxiv.org/abs/2404.13605
High-Performance Computing (HPC) systems excel in managing distributed workloads, and the growing interest in Artificial Intelligence (AI) has resulted in a surge in demand for faster methods of Machine Learning (ML) model training and inference. In
Externí odkaz:
http://arxiv.org/abs/2404.10386
Artificial Intelligence (AI) applications critically depend on data. Poor quality data produces inaccurate and ineffective AI models that may lead to incorrect or unsafe use. Evaluation of data readiness is a crucial step in improving the quality and
Externí odkaz:
http://arxiv.org/abs/2404.05779