A5020982327- Particle physics theoretical and experimental studies
- High-Energy Particle Collisions Research
- Particle Detector Development and Performance
- Quantum Chromodynamics and Particle Interactions
- Dark Matter and Cosmic Phenomena
- Computational Physics and Python Applications
- Distributed and Parallel Computing Systems
- Cosmology and Gravitation Theories
- Black Holes and Theoretical Physics
- Particle Accelerators and Free-Electron Lasers
- Superconducting Materials and Applications
- Neutrino Physics Research
- Algorithms and Data Compression
- Astrophysics and Cosmic Phenomena
- International Science and Diplomacy
- Radiomics and Machine Learning in Medical Imaging
- Gaussian Processes and Bayesian Inference
- Radio Astronomy Observations and Technology
- Scientific Computing and Data Management
- Radiation Detection and Scintillator Technologies
- Medical Imaging Techniques and Applications
- Anomaly Detection Techniques and Applications
- Biofield Effects and Biophysics
- Radiation Effects in Electronics
- Atomic and Subatomic Physics Research
Heidelberg University
2016-2025
Karlsruhe Institute of Technology
2022-2023
Heidelberg Institute for Theoretical Studies
2010-2023
Heidelberg University
2023
Durham University
2017
University of Edinburgh
2006-2013
Vrije Universiteit Brussel
2013
International Solvay Institutes
2013
Scottish Universities Physics Alliance
2007-2010
Institut d'Astrophysique de Paris
2010
In the light of LHC, we revisit implications a fourth generation chiral matter. We identify specific ensemble particle masses and mixings that are in agreement with all current experimental bounds as well minimize contributions to electroweak precision observables. Higgs between 115-315 (115-750) GeV allowed by data at 68% 95% CL. Within this parameter space, there dramatic effects on phenomenology: production rates enhanced, weak-boson-fusion channels suppressed, angular distributions...
At the LHC associated top quark and Higgs boson production with a decay to bottom quarks has long been heavily disputed search channel. Recently, it found not be viable. We show how can observed by tagging massive bosons jets. For this purpose we construct boosted taggers for standard-model processes in complex QCD environment.
In this paper, we review recent theoretical progress and the latest experimental results in jet substructure from Tevatron LHC. We status of outlook for calculation simulation tools studying substructure. Following up on report Boost 2010 workshop, present a new set benchmark comparisons techniques, focusing variables grooming methods that are collectively known as 'top taggers'. To facilitate further exploration, have attempted to collect, harmonize publish software implementations these techniques.
We present state-of-the-art cross section predictions for the production of supersymmetric squarks and gluinos at upcoming LHC run with a centre-of-mass energy $$\sqrt{s} = 13$$ $$14$$ TeV, potential future $$pp$$ colliders operating 33$$ $$100$$ TeV. The results are based on calculations which include resummation soft-gluon emission next-to-leading logarithmic accuracy, matched to order QCD corrections. Furthermore, we provide an estimate theoretical uncertainty due variation...
At the LHC combinatorics make it unlikely that we will be able to observe stop pair production with a decay semi-leptonic top and missing energy for generic supersymmetric mass spectra. Using Standard-Model tagger on fully hadronic decays can not only extract signal but also measure momentum. To illustrate promise of tagging tops moderate boost include detailed discussion our HEPTopTagger algorithm.
Autoencoder networks, trained only on QCD jets, can be used to search for anomalies in jet-substructure. We show how, based either images or 4-vectors, they identify jets from decays of arbitrary heavy resonances. To control the backgrounds and underlying systematics we de-correlate jet mass using an adversarial network. Such autoencoder allows a general at same time easily controllable new physics. Ideally, it applied data phase space region, allowing us efficiently physics un-supervised learning.
This report of the BOOST2012 workshop presents results four working groups that studied key aspects jet substructure. We discuss potential first-principle QCD calculations to yield a precise description substructure jets and study accuracy state-of-the-art Monte Carlo tools. Limitations experiments' ability resolve are evaluated, with focus on impact additional (pile-up) proton collisions performance in future LHC operating scenarios. A final section summarizes lessons learnt from analyses...
A bstract We provide a comprehensive global analysis of Run II top measurements at the LHC in terms dimension-6 operators. distinctive feature sector as compared to Higgs-electroweak is large number four-quark discuss detail how they can be tested and quadratic lead stable limit on each individual Wilson coefficient. Predictions for all observables are computed NLO QCD. Our SF itter framework features detailed error treatment, including correlations between uncertainties.
A critical question concerning generative networks applied to event generation in particle physics is if the generated events add statistical precision beyond training sample. We show for a simple example with increasing dimensionality how indeed amplify statistics. quantify their impact through an amplification factor or equivalent numbers of sampled events.
First-principle simulations are at the heart of high-energy physics research program. They link vast data output multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range applications modern machine learning to event generation simulation-based inference, including conceptional developments driven by specific requirements particle physics. New ideas tools developed interface will improve speed precision forward simulations, handle...
Following the growing success of generative neural networks in LHC simulations, crucial question is how to control and assign uncertainties their event output. We show Bayesian normalizing flow or invertible capture from training turn them into an uncertainty on weight. Fundamentally, interplay between density estimates indicates that these learn functions analogy parameter fits rather than binned counts.
Generative networks are opening new avenues in fast event generation for the LHC. We show how generative flow can reach percent-level precision kinematic distributions, they be trained jointly with a discriminator, and this discriminator improves generation. Our joint training relies on novel coupling of two which does not require Nash equilibrium. then estimate uncertainties through Bayesian network setup conditional data augmentation, while ensures that there no systematic inconsistencies...
Well-trained classifiers and their complete weight distributions provide us with a well-motivated practicable method to test generative networks in particle physics. We illustrate benefits for distribution-shifted jets, calorimeter showers, reconstruction-level events. In all cases, the classifier weights make powerful of network, identify potential problems density estimation, relate them underlying physics, tie comprehensive precision uncertainty treatment networks.
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, new methods ML-based unfolding. The performance these approaches are evaluated on the same two datasets. find that all techniques capable accurately reproducing particle-level spectra complex observables. Given conceptually diverse, they offer an exciting toolkit class measurements can probe Standard Model with...
We introduce two diffusion models and an autoregressive transformer for LHC physics simulations. Bayesian versions allow us to control the networks capture training uncertainties. After illustrating their different density estimation methods simple toy models, we discuss advantages Z plus jets event generation. While excel through precision, scales best with phase space dimensionality. Given evaluation speed, expect benefit from dedicated use cases normalizing flows, transformers.
Whenever invertible generative networks are needed for LHC physics, normalizing flows show excellent performance. In this work, we investigate their performance fast calorimeter shower simulations with increasing phase space dimension. We use and expressive coupling spline transformations applied to the CaloChallenge datasets. addition base flow architecture also employ a VAE compress dimensionality train network in latent space. evaluate our on several metrics, including high-level...
We analyze the production of charginos, neutralinos, and sleptons at hadron colliders Tevatron LHC in direct channels $p\overline{p}/pp\ensuremath{\rightarrow}{\stackrel{\ifmmode \tilde{}\else \~{}\fi{}}{\ensuremath{\chi}}}_{i}{\stackrel{\ifmmode \~{}\fi{}}{\ensuremath{\chi}}}_{j}+X$ $\stackrel{\ifmmode \~{}\fi{}}{\ensuremath{\ell}}{\stackrel{\stackrel{\ifmmode \~{}\fi{}}{\ensuremath{\ell}}}{\ifmmode\bar\else\textasciimacron\fi{}}}^{\ensuremath{'}}+X$. The cross sections for these reactions...
Higgs boson production via weak fusion at the CERN Large Hadron Collider has capability to determine dominant CP nature of a boson, tensor structure its coupling bosons. This information is contained in azimuthal angle distribution two outgoing forward tagging jets. The technique independent both mass and observed decay channel.