Disentangling quarks and gluons in CMS open data
暂无分享,去创建一个
[1] Yen-Jie Lee,et al. Data-driven extraction of the substructure of quark and gluon jets in proton-proton and heavy-ion collisions , 2022, 2204.00641.
[2] I. Stewart,et al. Pure quark and gluon observables in collinear drop , 2022, Journal of High Energy Physics.
[3] P. Komiske,et al. Analyzing N-Point Energy Correlators inside Jets with CMS Open Data. , 2022, Physical review letters.
[4] Katy Craig,et al. Which metric on the space of collider events? , 2021, Physical Review D.
[5] M. Campanelli,et al. Publishing unbinned differential cross section results , 2021, Journal of Instrumentation.
[6] S. M. Etesami,et al. Study of quark and gluon jet substructure in Z+jet and dijet events from pp collisions , 2021, Journal of High Energy Physics.
[7] Armenia,et al. Measurement of Lepton-Jet Correlation in Deep-Inelastic Scattering with the H1 Detector Using Machine Learning for Unfolding. , 2021, Physical review letters.
[8] Benjamin Nachman,et al. Scaffolding Simulations with Deep Learning for High-dimensional Deconvolution , 2021, ArXiv.
[9] Katy Craig,et al. Linearized optimal transport for collider events , 2020, Physical Review D.
[10] Andrew P. Turner,et al. Data-driven quark- and gluon-jet modification in heavy-ion collisions , 2020, 2008.08596.
[11] J. Kamenik,et al. Learning the latent structure of collider events , 2020, Journal of High Energy Physics.
[12] N. Castro,et al. Use of a generalized energy Mover’s distance in the search for rare phenomena at colliders , 2020, The European Physical Journal C.
[13] C. Cesarotti,et al. A robust measure of event isotropy at colliders , 2020, Journal of High Energy Physics.
[14] Patrick T. Komiske,et al. The hidden geometry of particle collisions , 2020, Journal of High Energy Physics.
[15] E. Alvarez,et al. Topic model for four-top at the LHC , 2019, Journal of High Energy Physics.
[16] Patrick T. Komiske,et al. OmniFold: A Method to Simultaneously Unfold All Observables. , 2019, Physical review letters.
[17] Patrick T. Komiske,et al. Exploring the space of jets with CMS open data , 2019, Physical Review D.
[18] Joel Nothman,et al. SciPy 1.0-Fundamental Algorithms for Scientific Computing in Python , 2019, ArXiv.
[19] Hoang Dai Nghia Nguyen,et al. Properties of jet fragmentation using charged particles measured with the ATLAS detector in $pp$ collisions at $\sqrt{s}=13$ TeV , 2019, 1906.09254.
[20] E. Metodiev,et al. A theory of quark vs. gluon discrimination , 2019, Journal of High Energy Physics.
[21] Jernej F. Kamenik,et al. Uncovering latent jet substructure , 2019, Physical Review D.
[22] Jesse Thaler,et al. Metric Space of Collider Events. , 2019, Physical review letters.
[23] Benjamin Nachman,et al. Investigating the topology dependence of quark and gluon jets , 2018, Journal of High Energy Physics.
[24] Patrick T. Komiske,et al. Energy flow networks: deep sets for particle jets , 2018, Journal of High Energy Physics.
[25] P. Komiske,et al. An operational definition of quark and gluon jets , 2018, Journal of High Energy Physics.
[26] M. Campanelli,et al. Jet substructure at the Large Hadron Collider , 2018, Reviews of Modern Physics.
[27] E. Metodiev,et al. Jet Topics: Disentangling Quarks and Gluons at Colliders. , 2018, Physical review letters.
[28] Gilles Blanchard,et al. Decontamination of Mutual Contamination Models , 2017, J. Mach. Learn. Res..
[29] B. Nachman,et al. Classification without labels: learning from mixed samples in high energy physics , 2017, 1708.02949.
[30] C. Collaboration,et al. Particle-flow reconstruction and global event description with the CMS detector , 2017, 1706.04965.
[31] M. Williams,et al. A novel approach to the bias-variance problem in bump hunting , 2017, 1705.03578.
[32] D. Kar,et al. Systematics of quark/gluon tagging , 2017, 1704.03878.
[33] Alexander J. Smola,et al. Deep Sets , 2017, 1703.06114.
[34] Khachatryan,et al. Jet energy scale and resolution in the CMS experiment in pp collisions at 8 TeV , 2016, 1607.03663.
[35] Scoap. Measurement of the charged-particle multiplicity inside jets from s=8 TeV pp collisions with the ATLAS detector , 2016 .
[36] M. P. Casado,et al. Measurement of the charged-particle multiplicity inside jets from $$\sqrt{s}=8$$s=8$${\mathrm{TeV}}$$TeV pp collisions with the ATLAS detector , 2016, The European physical journal. C, Particles and fields.
[37] M. D. Pietra,et al. Measurement of jet charge in dijet events from √s = 8 TeV p p collisions with the ATLAS detector , 2016 .
[38] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[39] W. Waalewijn,et al. Gaining (mutual) information about quark/gluon discrimination , 2014, Journal of High Energy Physics.
[40] J. T. Childers,et al. Light-quark and gluon jet discrimination in pp collisions at √s=7 TeV with the ATLAS detector , 2014, 1405.6583.
[41] J. T. Childers,et al. Light-quark and gluon jet discrimination in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$pp$$\end{document}pp colli , 2014, The European Physical Journal C.
[42] D. Neill,et al. Jet shapes with the broadening axis , 2014, 1401.2158.
[43] Ben Taskar,et al. The Tangent Earth Mover's Distance , 2013, GSI.
[44] G. Salam,et al. Energy correlation functions for jet substructure , 2013, 1305.0007.
[45] M. Cacciari,et al. FastJet user manual , 2011, 1111.6097.
[46] R. Field,et al. Min-Bias and the Underlying Event at the LHC , 2011, 1202.0901.
[47] J. Thaler,et al. Maximizing boosted top identification by minimizing N-subjettiness , 2011, 1108.2701.
[48] C. Collaboration,et al. Determination of Jet Energy Calibration and Transverse Momentum Resolution in CMS , 2011, 1107.4277.
[49] J. Thaler,et al. Identifying boosted objects with N-subjettiness , 2010, 1011.2268.
[50] S. D. Ellis,et al. Jet shapes and jet algorithms in SCET , 2010, 1001.0014.
[51] Michael Werman,et al. A Linear Time Histogram Metric for Improved SIFT Matching , 2008, ECCV.
[52] João Paulo Teixeira,et al. The CMS experiment at the CERN LHC , 2008 .
[53] M. Cacciari,et al. The anti-$k_t$ jet clustering algorithm , 2008, 0802.1189.
[54] M. Cacciari,et al. The Catchment Area of Jets , 2008, 0802.1188.
[55] J. Varela,et al. The CMS trigger system , 2004, 1609.02366.
[56] Francesco Camastra,et al. Data dimensionality estimation methods: a survey , 2003, Pattern Recognit..
[57] S. Mrenna,et al. Pythia 6.3 physics and manual , 2003, hep-ph/0308153.
[58] Leonidas J. Guibas,et al. The Earth Mover's Distance as a Metric for Image Retrieval , 2000, International Journal of Computer Vision.
[59] Leonidas J. Guibas,et al. A metric for distributions with applications to image databases , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).
[60] G. D'Agostini,et al. A Multidimensional unfolding method based on Bayes' theorem , 1995 .
[61] J. Pumplin,et al. How to tell quark jets from gluon jets. , 1991, Physical review. D, Particles and fields.
[62] Michael Werman,et al. A Unified Approach to the Change of Resolution: Space and Gray-Level , 1989, IEEE Trans. Pattern Anal. Mach. Intell..
[63] P. Grassberger,et al. Characterization of Strange Attractors , 1983 .
[64] H. Akaike. A new look at the statistical model identification , 1974 .
[65] V. Mikuni. Multi-differential Jet Substructure Measurement in High Q 2 DIS Events with HERA-II Data , 2022 .
[66] M. Kenward,et al. An Introduction to the Bootstrap , 2007 .
[67] A. Dell'Acqua,et al. Geant4 - A simulation toolkit , 2003 .
[68] Balázs Kégl,et al. Intrinsic Dimension Estimation Using Packing Numbers , 2002, NIPS.
[69] R. Dobrushin. Prescribing a System of Random Variables by Conditional Distributions , 1970 .