Dissemination

Papers, conference contributions, internal notes and other publications.

• CMS Collaboration, Extraction of CKM matrix elements in single top quark t-channel events in proton-proton collisions at √s=13 TeV, arXiv:2004.12181, 2020-04-25, Phys. Lett. B 808 (2020) 135609; CMS-PAS-TOP-17-012, 2019-11-13 [based on CMS AN-2017/180, listed below]
• L. Layer, Automatic log analysis with NLP for the CMS workflow handling, oral talk at CHEP 2019, 24^th, International Conference on Computing in High Energy and Nuclear Physics, 4-8 November 2019, Adelaide, Australia
• M. Andrews, J. Alison, S. An, P. Bryant, B. Burkle, S. Gleyzer, M. Narain, M. Paulini, B. Poczos, E. Usai,  End-to-End Jet Classification of Quarks and Gluons with the CMS Open Data, arXiv:1902.08276 [hep-ex]
• T. Chwalek, N. Faltermann, A. De Iorio, A. O. M. Iorio, W. A. Khan, L. Layer, L. Lista, T. Muller, P. Ott, T. Pambor, Extraction of CKM matrix elements in the single top t-channel events at 13 TeV , CMS AN-2017/180 (2019), (access reserved to CMS collaboration members)
  
• L. Brenner, P. Verschuuren, G. Cowan, et al., Comparison of unfolding methods using RooFitUnfold, International Journal of Modern Physics A, Vol. 35, No. 24, 2050145 (2020); e-print arXiv:1910.14654
• Pim Verschuuren, Frequentist-Bayes Hybrid Covariance Estimation for Unfolding Problems, arXiv: 2110.09382 (2021)
• Lukas Layer et al., Automatic log analysis with NLP for the CMS workflow handling,, EPJ Web of Conferences 245, 03006 (2020)
• A. Golovatiuk, G. De Lellis, A. Ustyuzhanin, Deep learning for Directional Dark Matter search, Journal of Physics: Conference Series, vol. 1525, 012108 (2020). https://doi.org/10.1088/1742-6596/1525/1/012108
• Caldwell, P. Eller, V. Hafych, R. Schick, O. Schulz, M. Szalay. Integration with an adaptive harmonic mean algorithm. International Journal of Modern Physics A, 35 (24), 2050142).  He also developed a method for parallelizing Markov Chain Monte Carlo (MCMC)
• V. Hafych, P. Eller, O. Schulz, A. Caldwell, Parallelizing MCMC Sampling via Space Partitioning, submitted to: Springer — Statistics and Computing, arXiv:2008.03098
• Rahul Balasubramanian, Carsten Burgard, Wouter Verkerke, Effective Lagrangian Morphing, arXiv:2202.13612 (2022)
• Integrating Agent-Based Modelling with Copula Theory: Preliminary Insights and Open Problems; Fratrič, Sileno, Klous, van Engers; Proceedings of International Conference on Computational Science 2020 (pp.212-225)
• Manipulation of the Bitcoin Market: An Agent-based Study; Fratrič, Sileno, Klous, van Engers; Financial Innovation, June 22
• N. Simpson et al., Neos-Neural End-to-End Optimised Summary Statistics, to appear in ACAT 2021 proceedings, IOP Conference series, arxiv.org/abs/2203.05570
• Victor Ananyev, Alexander Lincoln Read, Approximating the mode of the non-central chi-squared distribution, arXiv:2106.12267 [math.CA] (2021)
• (Xiangyang Ju, Sitong An et al., Graph neural networks for particle reconstruction in high energy physics detectors, presented at NeurIPS 2019 Workshop “Machine Learning and the Physical Sciences”, arXiv:2003.11603 (2020)
• Paul Glaysher, Judith M Katzy, Sitong An, Iterative subtraction method for Feature Ranking, arXiv:1906.05718 (2019)
• Riccardo Di Sipio, Michele Faucci Giannelli, Sana Ketabchi Haghighat, Serena Palazzo, DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC, J. High Energ. Phys. (2019) 2019: 110, arXiv:1903.02433
• O. Schulz, F. Beaujean, A. Caldwell, C. Grunwald, V. Hafych, K. Kröninger, S. La Cagnina, L. Röhrig, L. Shtembari. BAT. jl: A Julia-Based Tool for Bayesian Inference. SN Computer Science 2, (3), 1-17
• Kim Albertsson, Sitong An, Lorenzo Moneta, Stefan Wunsch, Luca Zampieri, Fast Inference for Machine Learning in ROOT/TMVA, Contribution to 24th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2019), EPJ Web of Conferences, 245, 06008 (2020)
• Sitong An, Lorenzo Moneta, C++ Code Generation for Fast Inference of Deep Learning Models in ROOT/TMVA, contribution to ICHEP 2021, EPJ Web Conf., 251 (2021) 03040.
• Claudia Glasman, Pim Verschuuren et al., ATL-COM-PHYS-2021-187 https://cds.cern.ch/record/2765035
• Sirunyan, A.M. and Tumasyan, A. and Lukas Layer et.al, CMS Collaboration, Measurement of CKM matrix elements in single top quark t-channel production in proton-proton collisions at s=13 TeV, arXiv:2004.12181, 2020, Physics Letters B.; DOI:10.1016/j.physletb.2020.135609
• J. Kieseler, G. C. Strong, F. Chiandotto, T. Dorigo, and L. Layer. Calorimetric Measurement of Multi-TeV Muons via Deep Regression, European Physical Journal C 82:1, 2022. doi: 10.1140/epjc/s10052-022-09993-5
• A. Golovatiuk, A. Alexandrov, G. De Lellis, A. Di Crescenzo, V. Tiukov, Directionality preservation of nuclear recoils in an emulsion detector for directional dark matter search. Journal of Cosmology and Astroparticle Physics 2021.04 (2021): 047
• A. Golovatiuk, Directional Dark Matter Search with the NEWSdm experiment, Journal of Physics: Conference Series, vol. 2156, 012044 (2021)
• V. Hafych, A. Caldwell, et al. (AWAKE Collaboration), Analysis of Proton Bunch Parameters in the AWAKE Experiment, JINST Journal of Instrumentation, 16.11 (2021): P11031
• The ATLAS Collaboration, 10.1103/PhysRevD.101.012002, Combined measurements of Higgs boson production and decay using up to 80 fb-1 of proton-proton collision data at √s = 13 TeV collected with the ATLAS experiment
• The ATLAS Collaboration, ATL-PHYS-PUB-2021-010, 20 March 2021, Combined Effective field theory interpretation of H->WW* and WW measurements using ATLAS data, http://cdsweb.cern.ch/record/2758785
• The ATLAS Collaboration, ATLAS-CONF-2021-053, 02 November 2021, Combined measurements of Higgs boson, production and decay using up to 139 fb−1 of proton–proton collision data at √s =13 TeV collected with the ATLAS experiment, https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/CONFNOTES/ATLAS-CONF-2021-053/
• ATLAS Internal Note ATL-COM-PHYS-2021-1035, https://cds.cern.ch/record/2791808
• Michael Andrews, Sitong An, et al., End-to-end jet classification of quarks and gluons with the CMS open data, NIM A, Volume 977, 11 October 2020, 164304
• John Blue, Sitong An et al., Machine Learning for Fast Mapping Between Parton and Reconstruction Level Jets, APS April Meeting Abstracts, 2021
• The ATLAS Collaboration, Measurements of top-quark pair single- and double-differential cross-sections in the all-hadronic channel in pp collisions at √s = 13 TeV using the ATLAS detector, Journal of High Energy Physics volume 2021, 33 (2021)
• Pim Verschuuren, Serena Palazzo, Tom Powell, Steve Sutton, Alfred Pilgrim, Michele Faucci Giannelli, Supervised machine learning techniques for data matching based on similarity metrics, arXiv:2007.04001 (2020)
• Lukas Layer et al., Clustering of experimental seismo-acoustic events using Self-Organizing Map (SOM) Frontiers in Earth Science, frontiersin.org/articles/10.3389/feart.2020.581742
• A. Caldwell, V. Hafych, O. Schulz, L. Shtembari. Infections and Identified Cases of COVID-19 from Random Testing Data. arXiv preprint: 2005.11277
• Computational discovery of transaction-based financial crime: the case of Ponzi schemes; Fratrič, Sileno, Klous, van Engers; submitted to Coine workshop at International Conference on Autonomous Agents and Multiagent Systems 2022
• Pim Verschuuren, Novel Unfolding Methods and Measurements of tt ̄ Differential Cross Sections with SMEFT interpretation using the ATLAS detector at the LHC, PhD thesis, Royal Holloway, University of London, 2021
• https://root.cern/doc/v620/release-notes.html, https://cds.cern.ch/record/2688585, https://indico.cern.ch/event/773049/contributions/3476168/, https://indico.cern.ch/event/773049/contributions/3476171/
• Artem Golovatiuk, Andrey Ustyuzhanin, Andrey Alexandrov and Giovanni De Lellis, Deep Learning for direct Dark Matter search with nuclear emulsions arxiv:2106.11995 (2021)
• ATLAS Collaboration, Interpretations of the combined measurement of Higgs boson production and decay, https://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/CONFNOTES/ATLAS-CONF-2020-053
• Nathan Simpson, contribution to PyHEP 2020, Physics Analysis as a Differentiable Program, https://youtu.be/3P4ZDkbleKs
• Nathan Simpson, Poster contribution at ACAT 2021, https://indico.cern.ch/event/855454/contributions/4596772
• The ATLAS collaboration., Aad, G., Abbott, B. et al. Measurements of top-quark pair single- and double-differential cross-sections in the all-hadronic channel in pp collisions at s√ = 13 TeV using the ATLAS detector. J. High Energ. Phys. 2021, 33 (2021). https://doi.org/10.1007/JHEP01(2021)033
• Tommaso Dorigo et al., RanBox: Anomaly Detection in the Copula Space, presentation at the 10th International Conference on New Frontiers in Physics (ICNFP 2021), https://indico.cern.ch/event/1025480/
• A. Golovatiuk et al., SHiP Collaboration, The SHiP experiment at the proposed CERN SPS Beam Dump Facility, European Physical Journal C82 (2022) 486
• NEWSdm Collaboration, Discovery potential for directional Dark Matter detection with nuclear emulsions, European Physical Journal C78 (2018) 578.