Search Results for author: Jack Y. Araz

Found 8 papers, 5 papers with code

Quantum-probabilistic Hamiltonian learning for generative modelling & anomaly detection

1 code implementation7 Nov 2022 Jack Y. Araz, Michael Spannowsky

This study investigates the possibility of learning and utilising a system's Hamiltonian and its variational thermal state estimation for data analysis techniques.

Anomaly Detection

Identifying magnetic antiskyrmions while they form with convolutional neural networks

no code implementations23 May 2022 Jack Y. Araz, Juan Carlos Criado, Michael Spannowsky

We use a Convolutional Neural Network (CNN) to identify the relevant features in the thermodynamical phases of chiral magnets, including (anti-)skyrmions, bimerons, and helical and ferromagnetic states.

Multi-Label Classification

Classical versus Quantum: comparing Tensor Network-based Quantum Circuits on LHC data

1 code implementation21 Feb 2022 Jack Y. Araz, Michael Spannowsky

Tensor Networks (TN) are approximations of high-dimensional tensors designed to represent locally entangled quantum many-body systems efficiently.

Tensor Networks

Quantum-inspired event reconstruction with Tensor Networks: Matrix Product States

1 code implementation15 Jun 2021 Jack Y. Araz, Michael Spannowsky

Tensor Networks are non-trivial representations of high-dimensional tensors, originally designed to describe quantum many-body systems.

Tensor Networks

Elvet -- a neural network-based differential equation and variational problem solver

1 code implementation26 Mar 2021 Jack Y. Araz, Juan Carlos Criado, Michael Spannowsky

We present Elvet, a Python package for solving differential equations and variational problems using machine learning methods.

BIG-bench Machine Learning

Precision SMEFT bounds from the VBF Higgs at high transverse momentum

no code implementations6 Nov 2020 Jack Y. Araz, Shankha Banerjee, Rick S. Gupta, Michael Spannowsky

We study the production of Higgs bosons at high transverse momenta via vector-boson fusion (VBF) in the Standard Model Effective Field Theory (SMEFT).

High Energy Physics - Phenomenology High Energy Physics - Experiment

Simplified fast detector simulation in MadAnalysis 5

3 code implementations16 Jun 2020 Jack Y. Araz, Benjamin Fuks, Georgios Polykratis

We introduce a new simplified fast detector simulator in the MadAnalysis 5 platform.

High Energy Physics - Phenomenology High Energy Physics - Experiment

Reinterpretation of LHC Results for New Physics: Status and Recommendations after Run 2

no code implementations17 Mar 2020 Waleed Abdallah, Shehu AbdusSalam, Azar Ahmadov, Amine Ahriche, Gaël Alguero, Benjamin C. Allanach, Jack Y. Araz, Alexandre Arbey, Chiara Arina, Peter Athron, Emanuele Bagnaschi, Yang Bai, Michael J. Baker, Csaba Balazs, Daniele Barducci, Philip Bechtle, Aoife Bharucha, Andy Buckley, Jonathan Butterworth, Haiying Cai, Claudio Campagnari, Cari Cesarotti, Marcin Chrzaszcz, Andrea Coccaro, Eric Conte, Jonathan M. Cornell, Louie Dartmoor Corpe, Matthias Danninger, Luc Darmé, Aldo Deandrea, Nishita Desai, Barry Dillon, Caterina Doglioni, Juhi Dutta, John R. Ellis, Sebastian Ellis, Farida Fassi, Matthew Feickert, Nicolas Fernandez, Sylvain Fichet, Jernej F. Kamenik, Thomas Flacke, Benjamin Fuks, Achim Geiser, Marie-Hélène Genest, Akshay Ghalsasi, Tomas Gonzalo, Mark Goodsell, Stefania Gori, Philippe Gras, Admir Greljo, Diego Guadagnoli, Sven Heinemeyer, Lukas A. Heinrich, Jan Heisig, Deog Ki Hong, Tetiana Hryn'ova, Katri Huitu, Philip Ilten, Ahmed Ismail, Adil Jueid, Felix Kahlhoefer, Jan Kalinowski, Deepak Kar, Yevgeny Kats, Charanjit K. Khosa, Valeri Khoze, Tobias Klingl, Pyungwon Ko, Kyoungchul Kong, Wojciech Kotlarski, Michael Krämer, Sabine Kraml, Suchita Kulkarni, Anders Kvellestad, Clemens Lange, Kati Lassila-Perini, Seung J. Lee, Andre Lessa, Zhen Liu, Lara Lloret Iglesias, Jeanette M. Lorenz, Danika MacDonell, Farvah Mahmoudi, Judita Mamuzic, Andrea C. Marini, Pete Markowitz, Pablo Martinez Ruiz del Arbol, David Miller, Vasiliki Mitsou, Stefano Moretti, Marco Nardecchia, Siavash Neshatpour, Dao Thi Nhung, Per Osland, Patrick H. Owen, Orlando Panella, Alexander Pankov, Myeonghun Park, Werner Porod, Darren Price, Harrison Prosper, Are Raklev, Jürgen Reuter, Humberto Reyes-González, Thomas Rizzo, Tania Robens, Juan Rojo, Janusz A. Rosiek, Oleg Ruchayskiy, Veronica Sanz, Kai Schmidt-Hoberg, Pat Scott, Sezen Sekmen, Dipan Sengupta, Elizabeth Sexton-Kennedy, Hua-Sheng Shao, Seodong Shin, Luca Silvestrini, Ritesh Singh, Sukanya Sinha, Jory Sonneveld, Yotam Soreq, Giordon H. Stark, Tim Stefaniak, Jesse Thaler, Riccardo Torre, Emilio Torrente-Lujan, Gokhan Unel, Natascia Vignaroli, Wolfgang Waltenberger, Nicholas Wardle, Graeme Watt, Georg Weiglein, Martin J. White, Sophie L. Williamson, Jonas Wittbrodt, Lei Wu, Stefan Wunsch, Tevong You, Yang Zhang, José Zurita

We report on the status of efforts to improve the reinterpretation of searches and measurements at the LHC in terms of models for new physics, in the context of the LHC Reinterpretation Forum.

High Energy Physics - Phenomenology High Energy Physics - Experiment

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