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Simulation-based inference methods for particle physics

Oct 13, 2020
Johann Brehmer, Kyle Cranmer

* To appear in "Artificial Intelligence for Particle Physics", World Scientific Publishing Co 

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Flows for simultaneous manifold learning and density estimation

Mar 31, 2020
Johann Brehmer, Kyle Cranmer

* Code at https://github.com/johannbrehmer/manifold-flow 

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The frontier of simulation-based inference

Nov 14, 2019
Kyle Cranmer, Johann Brehmer, Gilles Louppe

* v2 fixed typos. 8 pages, 3 figures, proceedings for the Sackler Colloquia at the US National Academy of Sciences 

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Mining for Dark Matter Substructure: Inferring subhalo population properties from strong lenses with machine learning

Oct 17, 2019
Johann Brehmer, Siddharth Mishra-Sharma, Joeri Hermans, Gilles Louppe, Kyle Cranmer

* 23 pages, 6 figures, code available at https://github.com/smsharma/mining-for-substructure-lens; v2, minor changes to text, version accepted in ApJ 

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MadMiner: Machine learning-based inference for particle physics

Jul 24, 2019
Johann Brehmer, Felix Kling, Irina Espejo, Kyle Cranmer

* MadMiner is available at https://github.com/diana-hep/madminer 

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Effective LHC measurements with matrix elements and machine learning

Jun 04, 2019
Johann Brehmer, Kyle Cranmer, Irina Espejo, Felix Kling, Gilles Louppe, Juan Pavez

* Keynote at the 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2019) 

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Mining gold from implicit models to improve likelihood-free inference

Oct 09, 2018
Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer

* Code available at https://github.com/johannbrehmer/simulator-mining-example . v2: Fixed typos. v3: Expanded discussion, added Lotka-Volterra example 

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Likelihood-free inference with an improved cross-entropy estimator

Aug 02, 2018
Markus Stoye, Johann Brehmer, Gilles Louppe, Juan Pavez, Kyle Cranmer

* 8 pages, 3 figures 

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A Guide to Constraining Effective Field Theories with Machine Learning

Jul 26, 2018
Johann Brehmer, Kyle Cranmer, Gilles Louppe, Juan Pavez

* Phys. Rev. D 98, 052004 (2018) 
* See also the companion publication "Constraining Effective Field Theories with Machine Learning" at arXiv:1805.00013, a brief introduction presenting the key ideas. The code for these studies is available at https://github.com/johannbrehmer/higgs_inference . v2: Added references. v3: Improved description of algorithms, added references. v4: Clarified text, added references 

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Constraining Effective Field Theories with Machine Learning

Jul 26, 2018
Johann Brehmer, Kyle Cranmer, Gilles Louppe, Juan Pavez

* Phys. Rev. Lett. 121, 111801 (2018) 
* See also the companion publication "A Guide to Constraining Effective Field Theories with Machine Learning" at arXiv:1805.00020, an in-depth analysis of machine learning techniques for LHC measurements. The code for these studies is available at https://github.com/johannbrehmer/higgs_inference . v2: New schematic figure explaining the new algorithms, added references. v3, v4: Added references 

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