Lukas Heinrich

According to our database, Lukas Heinrich authored at least 25 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Hierarchical Neural Simulation-Based Inference Over Event Ensembles.

Lukas Heinrich, Siddharth Mishra-Sharma, Chris Pollard, Philipp Windischhofer

Trans. Mach. Learn. Res., 2024

Masked particle modeling on sets: towards self-supervised high energy physics foundation models.

Tobias Golling, Lukas Heinrich, Michael Kagan, Samuel Klein, Matthew Leigh, Margarita Osadchy, John Andrew Raine

Mach. Learn. Sci. Technol., 2024

Is Tokenization Needed for Masked Particle Modelling?

Matthew Leigh, Samuel Klein, François Charton, Tobias Golling, Lukas Heinrich, Michael Kagan, Inês Ochoa, Margarita Osadchy

CoRR, 2024

Scalable ATLAS pMSSM computational workflows using containerised REANA reusable analysis platform.

Marco Donadoni, Matthew Feickert, Lukas Heinrich, Yang Liu, Audrius Mecionis, Vladyslav Moisieienkov, Tibor Simko, Giordon Stark, Marco Vidal García

CoRR, 2024

Combined track finding with GNN & CKF.

Lukas Heinrich, Benjamin Huth, Andreas Salzburger, Tilo Wettig

CoRR, 2024

Finetuning Foundation Models for Joint Analysis Optimization.

Matthias Vigl, Nicole Hartman, Lukas Heinrich

CoRR, 2024

2023

Set-conditional set generation for particle physics.

Nathalie Soybelman, Nilotpal Kakati, Lukas Heinrich, Francesco Armando Di Bello, Etienne Dreyer, Sanmay Ganguly, Eilam Gross, Marumi Kado, Jonathan Shlomi

Mach. Learn. Sci. Technol., December, 2023

Potential of the Julia Programming Language for High Energy Physics Computing.

Jonas Eschle, Tamás Gál, Mosè Giordano, Philippe Gras, Benedikt Hegner, Lukas Heinrich, Uwe Hernandez Acosta, Stefan Kluth, Jerry (Jiahong) Ling, Pere Mato, Mikhail Mikhasenko, Alexander Moreno Briceño, Jim Pivarski, Konstantinos Samaras-Tsakiris, Oliver Schulz, Graeme Andrew Stewart, Jan Strube, Vassil Vassilev

Comput. Softw. Big Sci., December, 2023

Configurable calorimeter simulation for AI applications.

Anton Charkin-Gorbulin, Kyle Cranmer, Francesco Armando Di Bello, Etienne Dreyer, Sanmay Ganguly, Eilam Gross, Lukas Heinrich, Marumi Kado, Nilotpal Kakati, Patrick Rieck, Lorenzo Santi, Matteo Tusoni

Mach. Learn. Sci. Technol., September, 2023

Branches of a Tree: Taking Derivatives of Programs with Discrete and Branching Randomness in High Energy Physics.

Michael Kagan, Lukas Heinrich

CoRR, 2023

2022

Survey of Open Data Concepts Within Fundamental Physics: An Initiative of the PUNCH4NFDI Consortium.

Harry Enke, Andreas Haungs, Thomas Schörner-Sadenius, Kilian Schwarz, Markus Demleitner, Achim Geiser, Lukas Heinrich, Michael Kramer, Gernot Maier, Dominik Schwarz, Hendrik Seitz-Moskaliuk, Hubert Simma, Michael Sterzik, Stefan Typel

Comput. Softw. Big Sci., December, 2022

FAIR for AI: An interdisciplinary, international, inclusive, and diverse community building perspective.

Eliu A. Huerta, Ben Blaiszik, L. Catherine Brinson, Kristofer E. Bouchard, Daniel Diaz, Caterina Doglioni, Javier M. Duarte, Murali Emani, Ian T. Foster, Geoffrey C. Fox, Philip C. Harris, Lukas Heinrich, Shantenu Jha, Daniel S. Katz, Volodymyr V. Kindratenko, Christine R. Kirkpatrick, Kati Lassila-Perini, Ravi K. Madduri, Mark S. Neubauer, Fotis E. Psomopoulos, Avik Roy, Oliver Rübel, Zhizhen Zhao, Ruike Zhu

CoRR, 2022

neos: End-to-End-Optimised Summary Statistics for High Energy Physics.

Nathan Simpson, Lukas Heinrich

CoRR, 2022

Differentiable Matrix Elements with MadJax.

Lukas Heinrich, Michael Kagan

CoRR, 2022

2021

pyhf: pure-Python implementation of HistFactory statistical models.

Lukas Heinrich, Matthew Feickert, Giordon Stark, Kyle Cranmer

J. Open Source Softw., 2021

Scalable Declarative HEP Analysis Workflows for Containerised Compute Clouds.

Tibor Simko, Lukas Alexander Heinrich, Clemens Lange, Adelina Eleonora Lintuluoto, Danika Marina MacDonell, Audrius Mecionis, Diego Rodríguez Rodríguez, Parth Shandilya, Marco Vidal García

Frontiers Big Data, 2021

Software Training in HEP.

Sudhir Malik, Samuel Meehan, Kilian Lieret, Meirin Oan Evans, Michel Hernández Villanueva, Daniel S. Katz, Graeme A. Stewart, Peter Elmer, Sizar Aziz, Matthew Bellis, Riccardo Maria Bianchi, Gianluca Bianco, Johan Sebastian Bonilla, Angela Burger, Jackson Burzynski, David Chamont, Matthew Feickert, Philipp Gadow, Bernhard Manfred Gruber, Daniel Guest, Stephan Hageboeck, Lukas Heinrich, Maximilian M. Horzela, Marc Huwiler, Clemens Lange, Konstantin Lehmann, Ke Li, Devdatta Majumder, Judita Mamuzic, Kevin Nelson, Robin Newhouse, Emery Nibigira, Scarlet Norberg, Arturo Sánchez Pineda, Mason Proffitt, Brendan Regnery, Amber Roepe, Stefan Roiser, Henry Schreiner, Oksana Shadura, Giordon Stark, Stephen Nicholas Swatman, Savannah Thais, Andrea Valassi, Stefan Wunsch, David Yakobovitch, Siqi Yuan

Comput. Softw. Big Sci., 2021

Distributed statistical inference with pyhf enabled through funcX.

Matthew Feickert, Lukas Heinrich, Giordon Stark, Ben Galewsky

CoRR, 2021

2019

Etalumis: bringing probabilistic programming to scientific simulators at scale.

Atilim Günes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Lawrence Meadows, Jialin Liu, Andreas Munk, Saeid Naderiparizi, Bradley Gram-Hansen, Gilles Louppe, Mingfei Ma, Xiaohui Zhao, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood

Proceedings of the International Conference for High Performance Computing, 2019

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model.

Atilim Gunes Baydin, Lei Shao, Wahid Bhimji, Lukas Heinrich, Saeid Naderiparizi, Andreas Munk, Jialin Liu, Bradley Gram-Hansen, Gilles Louppe, Lawrence Meadows, Philip H. S. Torr, Victor W. Lee, Kyle Cranmer, Prabhat, Frank Wood

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model.

Atilim Gunes Baydin, Lukas Heinrich, Wahid Bhimji, Bradley Gram-Hansen, Gilles Louppe, Lei Shao, Prabhat, Kyle Cranmer, Frank D. Wood

CoRR, 2018

Machine Learning in High Energy Physics Community White Paper.

Kim Albertsson, Piero Altoe, Dustin Anderson, Michael Andrews, Juan Pedro Araque Espinosa, Adam Aurisano, Laurent Basara, Adrian Bevan, Wahid Bhimji, Daniele Bonacorsi, Paolo Calafiura, Mario Campanelli, Louis Capps, Federico Carminati, Stefano Carrazza, Taylor Childers, Elias Coniavitis, Kyle Cranmer, Claire David, Douglas Davis, Javier M. 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 V. Gligorov, Tobias Golling, Jonas Graw, Lindsey Gray, Dick Greenwood, Thomas Hacker, John Harvey, Benedikt Hegner, Lukas Heinrich, Ben Hooberman, Johannes Junggeburth, Michael Kagan, Meghan Kane, Konstantin Kanishchev, Przemyslaw Karpinski, 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 S. Neubauer, Harvey B. Newman, Hans Pabst, Michela Paganini, Manfred Paulini, Gabriel N. 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 Andrew Stewart, Bob Stienen, Ian Stockdale, Giles Chatham Strong, Savannah Thais, Karen Tomko, Eli Upfal, Emanuele Usai, Andrey Ustyuzhanin, Martin Vala, Sofia Vallecorsa, Mauro Verzetti, Xavier Vilasís-Cardona, Jean-Roch Vlimant, Ilija Vukotic, Sean-Jiun Wang, Gordon Watts, Michael Williams, Wenjing Wu, Stefan Wunsch, Omar Zapata

CoRR, 2018

Search for Computational Workflow Synergies in Reproducible Research Data Analyses in Particle Physics and Life Sciences.

Tibor Simko, Kyle Cranmer, Michael R. Crusoe, Lukas Heinrich, Anton Khodak, Dinos Kousidis, Diego Rodriguez

Proceedings of the 14th IEEE International Conference on e-Science, 2018

2017

Improvements to Inference Compilation for Probabilistic Programming in Large-Scale Scientific Simulators.

Mario Lezcano Casado, Atilim Gunes Baydin, David Martínez-Rubio, Tuan Anh Le, Frank D. Wood, Lukas Heinrich, Gilles Louppe, Kyle Cranmer, Karen Ng, Wahid Bhimji, Prabhat

CoRR, 2017

HEPData: a repository for high energy physics data.

Eamonn Maguire, Lukas Heinrich, Graeme Watt

CoRR, 2017