Search Results for author: Sydney Otten

Found 5 papers, 2 papers with code

Inferring astrophysical X-ray polarization with deep learning

no code implementations16 May 2020 Nikita Moriakov, Ashwin Samudre, Michela Negro, Fabian Gieseke, Sydney Otten, Luc Hendriks

We investigate the use of deep learning in the context of X-ray polarization detection from astrophysical sources as will be observed by the Imaging X-ray Polarimetry Explorer (IXPE), a future NASA selected space-based mission expected to be operative in 2021.

Differentiable Strong Lensing: Uniting Gravity and Neural Nets through Differentiable Probabilistic Programming

no code implementations14 Oct 2019 Marco Chianese, Adam Coogan, Paul Hofma, Sydney Otten, Christoph Weniger

The careful analysis of strongly gravitationally lensed radio and optical images of distant galaxies can in principle reveal DM (sub-)structures with masses several orders of magnitude below the mass of dwarf spheroidal galaxies.

Cosmology and Nongalactic Astrophysics Astrophysics of Galaxies Instrumentation and Methods for Astrophysics High Energy Physics - Phenomenology

Constraining the Parameters of High-Dimensional Models with Active Learning

1 code implementation19 May 2019 Sascha Caron, Tom Heskes, Sydney Otten, Bob Stienen

Constraining the parameters of physical models with $>5-10$ parameters is a widespread problem in fields like particle physics and astronomy.

Active Learning Astronomy +1

Event Generation and Statistical Sampling for Physics with Deep Generative Models and a Density Information Buffer

1 code implementation3 Jan 2019 Sydney Otten, Sascha Caron, Wieske de Swart, Melissa van Beekveld, Luc Hendriks, Caspar van Leeuwen, Damian Podareanu, Roberto Ruiz de Austri, Rob Verheyen

We present a study for the generation of events from a physical process with deep generative models.

High Energy Physics - Phenomenology High Energy Physics - Experiment Data Analysis, Statistics and Probability

Machine Learning in High Energy Physics Community White Paper

no code implementations8 Jul 2018 Kim Albertsson, Piero Altoe, Dustin Anderson, John Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano, Laurent Basara, Adrian Bevan, Wahid Bhimji, Daniele Bonacorsi, Bjorn Burkle, Paolo Calafiura, Mario Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Yi-fan Chen, Taylor Childers, Yann Coadou, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Andrea De Simone, Javier Duarte, Martin Erdmann, Jonas Eschle, Amir Farbin, Matthew Feickert, Nuno Filipe Castro, Conor Fitzpatrick, Michele Floris, Alessandra Forti, Jordi Garra-Tico, Jochen Gemmler, Maria Girone, Paul Glaysher, Sergei Gleyzer, Vladimir Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ulrich Heintz, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemysław Karpiński, Zahari Kassabov, Gaurav Kaul, Dorian Kcira, Thomas Keck, Alexei Klimentov, Jim Kowalkowski, Luke Kreczko, Alexander Kurepin, Rob Kutschke, Valentin Kuznetsov, Nicolas Köhler, Igor Lakomov, Kevin Lannon, Mario Lassnig, Antonio Limosani, Gilles Louppe, Aashrita Mangu, Pere Mato, Narain Meenakshi, Helge Meinhard, Dario Menasce, Lorenzo Moneta, Seth Moortgat, Mark Neubauer, Harvey Newman, Sydney Otten, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel Perdue, Uzziel Perez, Attilio Picazio, Jim Pivarski, Harrison Prosper, Fernanda Psihas, Alexander Radovic, Ryan Reece, Aurelius Rinkevicius, Eduardo Rodrigues, Jamal Rorie, David Rousseau, Aaron Sauers, Steven Schramm, Ariel Schwartzman, Horst Severini, Paul Seyfert, Filip Siroky, Konstantin Skazytkin, Mike Sokoloff, Graeme Stewart, Bob Stienen, Ian Stockdale, Giles Strong, Wei Sun, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Justin Vasel, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Kun Yang, Omar Zapata

In this document we discuss promising future research and development areas for machine learning in particle physics.

BIG-bench Machine Learning Vocal Bursts Intensity Prediction

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