Zobrazeno 1 - 10
of 2 868
pro vyhledávání: '"Smith Daniel P"'
Autor:
Cornish, Thomas M., Wardlow, Julie L., Greve, Thomas R., Chapman, Scott, Chen, Chian-Chou, Dannerbauer, Helmut, Goto, Tomotsugu, Gullberg, Bitten, Ho, Luis C., Jiang, Xue-Jian, Lagos, Claudia, Lee, Minju, Serjeant, Stephen, Shim, Hyunjin, Smith, Daniel J. B., Vijayan, Aswin, Wagg, Jeff, Zhou, Dazhi
Publikováno v:
Monthly Notices of the Royal Astronomical Society, Vol. 533, Issue 1 (2024) pp. 1032-1044
Measuring the environments of massive galaxies at high redshift is crucial to understanding galaxy evolution and the conditions that gave rise to the distribution of matter we see in the Universe today. While high-$z$ radio galaxies (H$z$RGs) and qua
Externí odkaz:
http://arxiv.org/abs/2407.21099
Autor:
Das, Soumyadeep, Smith, Daniel J. B., Haskell, Paul, Hardcastle, Martin J., Best, Philip N., Duncan, Kenneth J., Arnaudova, Marina I., Shenoy, Shravya, Kondapally, Rohit, Cochrane, Rachel K., Drake, Alyssa B., Gürkan, Gülay, Małek, Katarzyna, Morabito, Leah K., Prandoni, Isabella
Spectral energy distribution (SED) fitting has been extensively used to determine the nature of the faint radio source population. Recent efforts have combined fits from multiple SED-fitting codes to account for the host galaxy and any active nucleus
Externí odkaz:
http://arxiv.org/abs/2405.01624
Autor:
Liang, Weixin, Rajani, Nazneen, Yang, Xinyu, Ozoani, Ezinwanne, Wu, Eric, Chen, Yiqun, Smith, Daniel Scott, Zou, James
The rapid proliferation of AI models has underscored the importance of thorough documentation, as it enables users to understand, trust, and effectively utilize these models in various applications. Although developers are encouraged to produce model
Externí odkaz:
http://arxiv.org/abs/2402.05160
Autor:
Liang, Weixin, Zhang, Yuhui, Cao, Hancheng, Wang, Binglu, Ding, Daisy, Yang, Xinyu, Vodrahalli, Kailas, He, Siyu, Smith, Daniel, Yin, Yian, McFarland, Daniel, Zou, James
Expert feedback lays the foundation of rigorous research. However, the rapid growth of scholarly production and intricate knowledge specialization challenge the conventional scientific feedback mechanisms. High-quality peer reviews are increasingly d
Externí odkaz:
http://arxiv.org/abs/2310.01783
This paper presents a physics-informed neural network (PINN) approach for monitoring the health of diesel engines. The aim is to evaluate the engine dynamics, identify unknown parameters in a "mean value" model, and anticipate maintenance requirement
Externí odkaz:
http://arxiv.org/abs/2304.13799
Publikováno v:
Applied Intelligence, 2023
We develop a data-driven deep neural operator framework to approximate multiple output states for a diesel engine and generate real-time predictions with reasonable accuracy. As emission norms become more stringent, the need for fast and accurate mod
Externí odkaz:
http://arxiv.org/abs/2304.00567
Autor:
Wetzel, James, Blend, Dylan, Debbins, Paul, Hermann, Max, Koseyan, Ohannes Kamer, Kamaran, Gurkan, Onel, Yasar, Anderson, Thomas, Chigurupati, Nehal, Cox, Brad, Dubnowski, Max, Ledovskoy, Alexander, Perez-Lara, Carlos, Barbera, Thomas, Bostan, Nilay, Ford, Kiva, Jessop, Colin, Ruchti, Randal, Ruggiero, Daniel, Smith, Daniel, Vigneault, Mark, Wan, Yuyi, Wayne, Mitchell, Hu, Chen, Zhang, Liyuan, Zhu, Ren-Yuan
High performance calorimetry conducted at future hadron colliders, such as the FCC-hh, poses a significant challenge for applying current detector technologies due to unprecedented beam luminosities and radiation fields. Solutions include developing
Externí odkaz:
http://arxiv.org/abs/2303.05580
Autor:
Lehmann, Christian, Murphy, Michael T., Liu, Fan, Flynn, Chris, Smith, Daniel, Berke, Daniel A.
The Survey for Distant Solar Twins (SDST) aims to find stars very similar to the Sun at distances 1-4 kpc, several times more distant than any currently known solar twins and analogues. The goal is to identify the best stars with which to test whethe
Externí odkaz:
http://arxiv.org/abs/2302.00399
The increasingly wide usage of location aware sensors has made it possible to collect large volume of trajectory data in diverse application domains. Machine learning allows to study the activities or behaviours of moving objects (e.g., people, vehic
Externí odkaz:
http://arxiv.org/abs/2301.03134