Surface Impedance Measurements in Superconductors in DC Magnetic Fields: Challenges and Relevance to Particle Physics Experiments

2021 ◽  
Vol 24 (9) ◽  
pp. 12-20
Author(s):  
Andrea Alimenti ◽  
Nicola Pompeo ◽  
Kostiantyn Torokhtii ◽  
Enrico Silva
2019 ◽  
Vol 29 (5) ◽  
pp. 1-4 ◽  
Author(s):  
Andrea Alimenti ◽  
Nicola Pompeo ◽  
Kostiantyn Torokhtii ◽  
Tiziana Spina ◽  
Rene Flukiger ◽  
...  

2003 ◽  
Vol 74 (10) ◽  
pp. 4436-4441 ◽  
Author(s):  
Tetsuo Hanaguri ◽  
Keishi Takaki ◽  
Yoshishige Tsuchiya ◽  
Atsutaka Maeda

1994 ◽  
Vol 7 (2) ◽  
pp. 453-458 ◽  
Author(s):  
Steven M. Anlage ◽  
Dong -Ho Wu ◽  
Jian Mao ◽  
Sining Mao ◽  
X. X. Xi ◽  
...  

1977 ◽  
Vol 140 (3) ◽  
pp. 549-552 ◽  
Author(s):  
E.D. Platner ◽  
A. Etkin ◽  
K.J. Foley ◽  
J.H. Goldman ◽  
W.A. Love ◽  
...  

2004 ◽  
Vol 13 (10) ◽  
pp. 2355-2359 ◽  
Author(s):  
JONATHAN L. FENG ◽  
ARVIND RAJARAMAN ◽  
FUMIHIRO TAKAYAMA

The gravitational interactions of elementary particles are suppressed by the Planck scale M*~1018 GeV and are typically expected to be far too weak to be probed by experiments. We show that, contrary to conventional wisdom, such interactions may be studied by particle physics experiments in the next few years. As an example, we consider conventional supergravity with a stable gravitino as the lightest supersymmetric particle. The next-lightest supersymmetric particle (NLSP) decays to the gravitino through gravitational interactions after about a year. This lifetime can be measured by stopping NLSPs at colliders and observing their decays. Such studies will yield a measurement of Newton's gravitational constant on unprecedentedly small scales, shed light on dark matter, and provide a window on the early universe.


2020 ◽  
Vol 245 ◽  
pp. 06003
Author(s):  
Venkitesh Ayyar ◽  
Wahid Bhimji ◽  
Lisa Gerhardt ◽  
Sally Robertson ◽  
Zahra Ronaghi

The success of Convolutional Neural Networks (CNNs) in image classification has prompted efforts to study their use for classifying image data obtained in Particle Physics experiments. Here, we discuss our efforts to apply CNNs to 2D and 3D image data from particle physics experiments to classify signal from background. In this work we present an extensive convolutional neural architecture search, achieving high accuracy for signal/background discrimination for a HEP classification use-case based on simulated data from the Ice Cube neutrino observatory and an ATLAS-like detector. We demonstrate among other things that we can achieve the same accuracy as complex ResNet architectures with CNNs with less parameters, and present comparisons of computational requirements, training and inference times.


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