Publications in 2023

  1. Learning to Do or Learning While Doing: Reinforcement Learning and Bayesian Optimisation for Online Continuous Tuning: Jan Kaiser et al., arXiv, doi: 10.48550/arXiv.2306.03739
  2. Predictive Maintenance for the Optical Synchronization System of the European XFEL: A Systematic Literature Survey: Arne Grünhagen et al., BTW2023 - Datenbanksysteme für Business, Technologie und Web, doi: 10.18420/BTW2023-70
  3. Anomaly detection at the European X-ray Free Electron Laser using a parity-space-based method: Annika Eichler et al., Physical Review Accelerators and Beams, doi: 10.1103/PhysRevAccelBeams.26.012801
  4. PickYOLO: Fast deep learning particle detector for annotation of cryo electron tomograms: Erik Genthe et al. et al., Journal of Structural Biology, doi: 10.1016/j.jsb.2023.107990

Publications in 2022

  1. Application of Machine Learning in Longitudinal Phase Space Prediction at the European XFEL: Zihan Zhu et al., Proceedings of the 40th International Free Electron Laser Conference (FEL2022), doi: https://indico.jacow.org/event/44/contributions/545/
  2. Convex Synthesis of Robust Distributed Controllers for the Optical Synchronization System at European XFEL: Maximilian Schütte et al., 2022 IEEE Conference on Control Technology and Applications (CCTA), doi: 10.1109/CCTA49430.2022.9966114
  3. Learning-based Optimisation of Particle Accelerators Under Partial Observability Without Real-World Training: Jan Kaiser et al., Proceedings of the 39th International Conference on Machine Learning, doi: https://proceedings.mlr.press/v162/kaiser22a.html
  4. Accelerating Linear Beam Dynamics Simulations for Machine Learning Applications: Oliver Stein et al., Proceedings of the 13th International Particle Accelerator Conference, doi: 10.18429/JACoW-IPAC2022-WEPOMS036
  5. Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data: Vladimir Starostin et al., npj Comput Mater, doi: 10.1038/s41524-022-00778-8
  6. The osteogenetic activities of mesenchymal stem cells in response to Mg2+ ions and inflammatory cytokines: a numerical approach using fuzzy logic controllers: Jalil Nourisa et al., PLoS Comput Biol, doi: 10.1371/journal.pcbi.1010482
  7. Causal connections between socioeconomic disparities and COVID-19 in the USA: Tannista Banerjee et al., Sci Rep, doi: 10.1038/s41598-022-18725-4
  8. Neural network analysis of neutron and X-ray reflectivity data: automated analysis using mlreflect, experimental errors and feature engineering: Alessandro Greco et al., J Appl Cryst, doi: 10.1107/S1600576722002230
  9. An active learning approach for the interactive and guided segmentation of tomography data: Bashir Kazimi et al., SPIE Optical Engineering + Applications, doi: 10.1117/12.2637973
  10. Machine learning the trilinear and light-quark Yukawa couplings from Higgs pair kinematic shapes: Lina Alasfar et al., arXiv:2207.13760 [cond-mat.mtrl-sci], doi: 10.48550/arXiv.2207.13760
  11. Neural network analysis of neutron and X-ray reflectivity data: automated analysis using mlreflect, experimental errors and feature engineering: Alessandro Greco et al., arXiv:2202.11609 [physics.data-an], doi: 10.48550/arXiv.2202.11609
  12. End-to-End Deep Learning Pipeline for Real-Time Processing of Surface Scattering Data at Synchrotron Facilities: Vladimir Starostin et al., Synchrotron Radiation News, doi: 10.1080/08940886.2022.2112499

Publications in 2021

  1. Decentralized Output Feedback Control using Sparsity Invariance with Application to Synchronization at European XFEL: Maximilian Schütte et al., 2021 60th IEEE Conference on Decision and Control (CDC), doi: 10.1109/CDC45484.2021.9683027
  2. Fault Analysis of the Beam Acceleration Control System at the European XFEL using Data Mining: Arne Grünhagen et al., 2021 IEEE 30th Asian Test Symposium (ATS), doi: 10.1109/ATS52891.2021.00023
  3. First Steps Toward an Autonomous Accelerator, a Common Project Between DESY and KIT: Annika Eichler et al., Proceedings of the 12th International Particle Accelerator Conference, doi: 10.18429/JACoW-IPAC2021-TUPAB298
  4. Machine Learning Based Spatial Light Modulator Control for the Photoinjector Laser at FLUTE: Chenran Xu et al., Proceedings of the 12th International Particle Accelerator Conference, doi: 10.18429/JACoW-IPAC2021-WEPAB289
  5. High-Fidelity Prediction of Megapixel Longitudinal Phase-Space Images of Electron Beams Using Encoder-Decoder Neural Networks: J. Zhu et al., Phys. Rev. Applied, doi: https://doi.org/10.1103/PhysRevApplied.16.024005
  6. PhaseGAN: a deep-learning phase-retrieval approach for unpaired datasets: Yuhe Zhang et al., Opt. Express, doi: 10.1364/OE.423222
  7. Machine learning denoising of high-resolution X-ray nanotomography data: Silja Flenner et al., J Synchrotron Radiat, doi: 10.1107/S1600577521011139
  8. X-ray screening identifies active site and allosteric inhibitors of SARS-CoV-2 main protease: Sebastian Günther et al., Science, doi: 10.1126/science.abf7945
  9. Measurement of the CP-violating phase ϕs in the Bs0→J/ψϕ(1020)→μ+μ−K+K− channel in proton-proton collisions at s=13TeV: A.M. Sirunyan et al., Physics Letters B, doi: 10.1016/j.physletb.2021.136188
  10. High-resolution ex vivo analysis of the degradation and osseointegration of Mg-xGd implant screws in 3D: Diana Krüger et al., Bioactive Materials, doi: 10.1016/j.bioactmat.2021.10.041
  11. Model-based feed-forward control for time-varying systems with an example for SRF cavities: Sven Pfeiffer et al., IFAC-PapersOnLine, doi: 10.1016/j.ifacol.2020.12.1868
  12. Scaling the U-net: segmentation of biodegradable bone implants in high-resolution synchrotron radiation microtomograms: Ivo M. Baltruschat et al., Sci Rep, doi: 10.1038/s41598-021-03542-y
  13. Suppression of thermal nanoplasma emission in clusters strongly ionized by hard x-rays: Yoshiaki Kumagai et al., J. Phys. B: At. Mol. Opt. Phys., doi: 10.1088/1361-6455/abd878
  14. Estimation of Thermodynamic Observables in Lattice Field Theories with Deep Generative Models: Kim A. Nicoli et al., Phys. Rev. Lett., doi: 10.1103/PhysRevLett.126.032001
  15. Measurement of the Higgs boson production rate in association with top quarks in final states with electrons, muons, and hadronically decaying tau leptons at √s= 13 TeV: A. M. Sirunyan et al., Eur. Phys. J. C, doi: 10.1140/epjc/s10052-021-09014-x
  16. Resurrecting bb¯h with kinematic shapes: Christophe Grojean et al., J. High Energ. Phys., doi: 10.1007/JHEP04(2021)139
  17. Physics Validation of Novel Convolutional 2D Architectures for Speeding Up High Energy Physics Simulations: Florian Rehm et al., EPJ Web Conf., doi: 10.1051/epjconf/202125103042
  18. Reduced model of plasma evolution in hydrogen discharge capillary plasmas: G. J. Boyle et al., Phys. Rev. E, doi: 10.1103/PhysRevE.104.015211
  19. Getting High: High Fidelity Simulation of High Granularity Calorimeters with High Speed: Erik Buhmann et al., Comput Softw Big Sci, doi: 10.1007/s41781-021-00056-0
  20. The XBI BioLab for life science experiments at the European XFEL: Huijong Han et al., J Appl Cryst, doi: 10.1107/S1600576720013989
  21. Online Detuning Computation and Quench Detection for Superconducting Resonators: Andrea Bellandi et al., IEEE Trans. Nucl. Sci., doi: 10.1109/TNS.2021.3067598
  22. Few-fs resolution of a photoactive protein traversing a conical intersection: A. Hosseinizadeh et al., Nature, doi: 10.1038/s41586-021-04050-9

Publications in 2020

  1. A deep neural network to search for new long-lived particles decaying to jets: cms-collaboration et al., Mach. Learn.: Sci. Technol., doi: 10.1088/2632-2153/ab9023
  2. Dijet resonance search with weak supervision using √s=13 TeV pp collisions in the ATLAS detector: ATLAS Collaboration et al., Phys. Rev. Lett., doi: 10.1103/PhysRevLett.125.131801
  3. Automation and control of laser wakefield accelerators using Bayesian optimization: R. J. Shalloo et al., Nat Commun, doi: 10.1038/s41467-020-20245-6
  4. Identification of heavy, energetic, hadronically decaying particles using machine-learning techniques: The CMS Collaboration et al., J. Inst., doi: 10.1088/1748-0221/15/06/P06005
  5. Teaching machine learning with an application in collider particle physics: L. Benato et al., J. Inst., doi: 10.1088/1748-0221/15/09/C09011
  6. Tomographic reconstruction with a generative adversarial network: Xiaogang Yang et al., J Synchrotron Radiat, doi: 10.1107/S1600577520000831
  7. Integration of machine learning with phase field method to model the electromigration induced Cu6Sn5 IMC growth at anode side Cu/Sn interface: Anil Kunwar et al., Journal of Materials Science & Technology, doi: 10.1016/j.jmst.2020.04.046
  8. Cosmic ray spectrum from 250 TeV to 10 PeV using IceTop: The IceCube Collaboration et al., Phys. Rev. D, doi: 10.1103/PhysRevD.102.122001
  9. Pushing the temporal resolution in absorption and Zernike phase contrast nanotomography: enabling fast in situ experiments: Silja Flenner et al., J Synchrotron Radiat, doi: 10.1107/S1600577520007407
  10. Background rejection in atmospheric Cherenkov telescopes using recurrent convolutional neural networks: R. D. Parsons et al., Eur. Phys. J. C, doi: 10.1140/epjc/s10052-020-7953-3
  11. Higgs self-coupling measurements using deep learning in the bb¯bb¯ final state: Jacob Amacker et al., J. High Energ. Phys., doi: 10.1007/JHEP12(2020)115
  12. Physics-based deep neural networks for beam dynamics in charged particle accelerators: Andrei Ivanov et al., Phys. Rev. Accel. Beams, doi: 10.1103/PhysRevAccelBeams.23.074601
  13. Data-driven Detection of Multimessenger Transients: Iftach Sadeh et al., ApJ, doi: 10.3847/2041-8213/ab8b5f
  14. Self-Learning Method for Construction of Analytical Interatomic Potentials to Describe Laser-Excited Materials: Bernd Bauerhenne et al., Phys. Rev. Lett., doi: 10.1103/PhysRevLett.124.085501

Publications in 2019

  1. Distributed Model Predictive Control for Linear Systems With Adaptive Terminal Sets: Georgios Darivianakis et al., IEEE Trans. Automat. Contr., doi: 10.1109/TAC.2019.2916774
  2. Semantic segmentation of synchrotron tomography of multiphase Al-Si alloys using a convolutional neural network with a pixel-wise weighted loss function: Tobias Strohmann et al., Sci Rep, doi: 10.1038/s41598-019-56008-7
  3. Inflation as an information bottleneck: a strategy for identifying universality classes and making robust predictions: Mafalda Dias et al., J. High Energ. Phys., doi: 10.1007/JHEP05(2019)065
  4. A load frame for in situ tomography at PETRA III: Julian Moosmann et al., Proceedings Volume 11113, Developments in X-Ray Tomography XII, doi: 10.1117/12.2530445
  5. West-Life: A Virtual Research Environment for structural biology: Chris Morris et al., Journal of Structural Biology: X, doi: 10.1016/j.yjsbx.2019.100006
  6. The Full Event Interpretation: T. Keck et al., Comput Softw Big Sci, doi: 10.1007/s41781-019-0021-8

Older publications

  1. Russian–German Astroparticle Data Life Cycle Initiative: Igor Bychkov et al., Data, doi: 10.3390/data3040056
  2. Evidence for associated production of a Higgs boson with a top quark pair in final states with electrons, muons, and hadronically decaying τ leptons at √s= 13 TeV: CMS Collaboration et al., J. High Energ. Phys., doi: 10.1007/JHEP08(2018)066
  3. Silver clusters shape determination from in-situ XANES data: Janis Timoshenko et al., Radiation Physics and Chemistry, doi: 10.1016/j.radphyschem.2018.11.003
  4. FPGA-Based RF and Piezocontrollers for SRF Cavities in CW Mode: Radoslaw Rybaniec et al., IEEE Trans. Nucl. Sci., doi: 10.1109/TNS.2017.2687981
  5. Measurement of the νµ energy spectrum with IceCube-79: M. G. Aartsen et al., Eur. Phys. J. C, doi: 10.1140/epjc/s10052-017-5261-3
  6. Accurate prediction of X-ray pulse properties from a free-electron laser using machine learning: A. Sanchez-Gonzalez et al., Nat Commun, doi: 10.1038/ncomms15461
  7. Improved γ/hadron separation for the detection of faint γ -ray sources using boosted decision trees: Maria Krause et al., Astroparticle Physics, doi: 10.1016/j.astropartphys.2017.01.004
  8. Performance and optimization of support vector machines in high-energy physics classification problems: M.Ö. Sahin et al., Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, doi: 10.1016/j.nima.2016.09.017