Search Results for author: Yonatan Kahn

Found 6 papers, 2 papers with code

Scaling Laws in Jet Classification

1 code implementation4 Dec 2023 Joshua Batson, Yonatan Kahn

We demonstrate the emergence of scaling laws in the benchmark top versus QCD jet classification problem in collider physics.

Classification

A facility to Search for Hidden Particles at the CERN SPS: the SHiP physics case

no code implementations19 Apr 2015 Sergey Alekhin, Wolfgang Altmannshofer, Takehiko Asaka, Brian Batell, Fedor Bezrukov, Kyrylo Bondarenko, Alexey Boyarsky, Nathaniel Craig, Ki-Young Choi, Cristóbal Corral, David Curtin, Sacha Davidson, André de Gouvêa, Stefano Dell'Oro, Patrick deNiverville, P. S. Bhupal Dev, Herbi Dreiner, Marco Drewes, Shintaro Eijima, Rouven Essig, Anthony Fradette, Björn Garbrecht, Belen Gavela, Gian F. Giudice, Dmitry Gorbunov, Stefania Gori, Christophe Grojean, Mark D. Goodsell, Alberto Guffanti, Thomas Hambye, Steen H. Hansen, Juan Carlos Helo, Pilar Hernandez, Alejandro Ibarra, Artem Ivashko, Eder Izaguirre, Joerg Jaeckel, Yu Seon Jeong, Felix Kahlhoefer, Yonatan Kahn, Andrey Katz, Choong Sun Kim, Sergey Kovalenko, Gordan Krnjaic, Valery E. Lyubovitskij, Simone Marcocci, Matthew Mccullough, David McKeen, Guenakh Mitselmakher, Sven-Olaf Moch, Rabindra N. Mohapatra, David E. Morrissey, Maksym Ovchynnikov, Emmanuel Paschos, Apostolos Pilaftsis, Maxim Pospelov, Mary Hall Reno, Andreas Ringwald, Adam Ritz, Leszek Roszkowski, Valery Rubakov, Oleg Ruchayskiy, Jessie Shelton, Ingo Schienbein, Daniel Schmeier, Kai Schmidt-Hoberg, Pedro Schwaller, Goran Senjanovic, Osamu Seto, Mikhail Shaposhnikov, Brian Shuve, Robert Shrock, Lesya Shchutska, Michael Spannowsky, Andy Spray, Florian Staub, Daniel Stolarski, Matt Strassler, Vladimir Tello, Francesco Tramontano, Anurag Tripathi, Sean Tulin, Francesco Vissani, Martin W. Winkler, Kathryn M. Zurek

We demonstrate that the SHiP experiment has a unique potential to discover new physics and can directly probe a number of solutions of beyond the Standard Model puzzles, such as neutrino masses, baryon asymmetry of the Universe, dark matter, and inflation

High Energy Physics - Phenomenology High Energy Physics - Experiment

First Results from ABRACADABRA-10 cm: A Search for Sub-$μ$eV Axion Dark Matter

no code implementations29 Oct 2018 Jonathan L. Ouellet, Chiara P. Salemi, Joshua W. Foster, Reyco Henning, Zachary Bogorad, Janet M. Conrad, Joseph A. Formaggio, Yonatan Kahn, Joe Minervini, Alexey Radovinsky, Nicholas L. Rodd, Benjamin R. Safdi, Jesse Thaler, Daniel Winklehner, Lindley Winslow

To date, the available parameter space for axion and axion-like particle dark matter is relatively unexplored, particularly at masses $m_a\lesssim1\,\mu$eV.

High Energy Physics - Experiment Instrumentation and Detectors

Green Bank and Effelsberg Radio Telescope Searches for Axion Dark Matter Conversion in Neutron Star Magnetospheres

1 code implementation31 Mar 2020 Joshua W. Foster, Yonatan Kahn, Oscar Macias, Zhiquan Sun, Ralph P. Eatough, Vladislav I. Kondratiev, Wendy M. Peters, Christoph Weniger, Benjamin R. Safdi

Axion dark matter (DM) may convert to radio-frequency electromagnetic radiation in the strong magnetic fields around neutron stars.

Cosmology and Nongalactic Astrophysics High Energy Astrophysical Phenomena High Energy Physics - Phenomenology

Topological Obstructions to Autoencoding

no code implementations16 Feb 2021 Joshua Batson, C. Grace Haaf, Yonatan Kahn, Daniel A. Roberts

Using a series of illustrative low-dimensional examples, we show explicitly how the intrinsic and extrinsic topology of the dataset affects the behavior of an autoencoder and how this topology is manifested in the latent space representation during training.

Anomaly Detection Inductive Bias

Feature Learning and Generalization in Deep Networks with Orthogonal Weights

no code implementations11 Oct 2023 Hannah Day, Yonatan Kahn, Daniel A. Roberts

Fully-connected deep neural networks with weights initialized from independent Gaussian distributions can be tuned to criticality, which prevents the exponential growth or decay of signals propagating through the network.

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