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We propose a differentiable vertex fitting algorithm that can be used for secondary vertex fitting, and that can be seamlessly integrated into neural networks for jet flavour tagging.
1905
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H. M. Bücker, G. F. Corliss, P. D. Hovland, U. Naumann, and B. Norris, eds., Automatic Differentiation: Applications, Theory, and Implementations , Lecture Notes in Computational Science and Engineering (Springer, New York, NY, 2005)
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R. Fruhwirth, W. Waltenberger, and P. Vanlaer, Adaptive vertex fitting, J. Phys. G 34
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M. Cacciari, G. P. Salam, and G. Soyez, The anti-kt jet clustering algorithm, Journal of High Energy Physics 2008
2008
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G. Piacquadio, Identification of b-jets and investigation of the discovery potential of a Higgs boson in the W H → l ν b b ¯ WH\to l\nu b\bar{b} channel with the ATLAS experiment , Ph.D. thesis , Freiburg University (2010)
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H. Krantz, Steven amd Parks, The Implicit Function Theorem: History, Theory, and Applications (Birkhäuser, 2013)
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M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Jozefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. Tucker, V. Vanhoucke, V. Vasudevan, F. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng, TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems (2015), software available from tensorflow.org
2015
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D. Guest, J. Collado, P. Baldi, S.-C. Hsu, G. Urban, and D. Whiteson, Jet flavor classification in high-energy physics with deep neural networks, Phys. Rev. D 94
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S. Diamond and S. Boyd, CVXPY: A Python-embedded modeling language for convex optimization, JMLR (2016) , to appear
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K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)
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J. L. Ba, J. R. Kiros, and G. E. Hinton, Layer normalization (2016), arXiv:1607.06450 [stat.ML]
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ATLAS Collaboration, Identification of Jets Containing b b -Hadrons with Recurrent Neural Networks at the ATLAS Experiment, ATL-PHYS-PUB-2017-003 (2017)
2017
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. u. Kaiser, and I. Polosukhin, Attention is all you need, in Advances in Neural Information Processing Systems , Vol. 30, edited by I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett (Curran Associates, Inc., 2017)
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B. Amos and J. Z. Kolter, Optnet: Differentiable optimization as a layer in neural networks, in ICML , ICML’17 (JMLR.org, 2017) p. 136–145
2017
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CMS Collaboration, Performance of the DeepJet b tagging algorithm using 41.9/fb of data from proton-proton collisions at 13TeV with Phase 1 CMS detector, CMS-DP-2018-058 (2018b)
2018
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A. G. Baydin, B. A. Pearlmutter, A. A. Radul, and J. M. Siskind, Automatic differentiation in machine learning: a survey, JMLR 18
2018
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J. Shlomi, S. Ganguly, E. Gross, K. Cranmer, Y. Lipman, H. Serviansky, H. Maron, and N. Segol, Secondary vertex finding in jets with neural networks, EPJC 81
2021
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2021
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R. Früwirth and A. Strandlie, Pattern Recognition, Tracking and Vertex Reconstruction in Particle Detectors (Springer, 2021)
2021
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L. Heinrich, M. Feickert, G. Stark, and K. Cranmer, pyhf: pure-Python implementation of HistFactory statistical models, JOSS 6
2021
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S. Carrazza, J. M. Cruz-Martinez, and M. Rossi, Pdfflow: Parton distribution functions on gpu, Comp. Phys. Comm. 264
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J. Bradbury, R. Frostig, P. Hawkins, M. J. Johnson, C. Leary, D. Maclaurin, G. Necula, A. Paszke, J. VanderPlas, S. Wanderman-Milne, and Q. Zhang, JAX: composable transformations of Python+NumPy programs (2018)
2018
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ATLAS Collaboration, Atlas b-jet identification performance and efficiency measurement with $$t{ \ \backslash bar{t}}$$ events in pp collisions at $$ \ \backslash sqrt{s}=13$$ tev, The European Physical Journal C 79
2019
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A. Agrawal, B. Amos, S. Barratt, S. Boyd, S. Diamond, and J. Z. Kolter, Differentiable convex optimization layers, in NeurIPS , Vol. 32, edited by H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Curran Associates, Inc., 2019)
2019
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A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, PyTorch: An Imperative Style, High-Performance Deep Learning Library, in NeurIPS , edited by H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett (Curran Associates, Inc., 2019) pp. 8024–8035
2019
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ATLAS Collaboration, Deep Sets based Neural Networks for Impact Parameter Flavour Tagging in ATLAS, ATL-PHYS-PUB-2020-014 (2020)
2020
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CMS Collaboration, Identification of highly Lorentz-boosted heavy particles using graph neural networks and new mass decorrelation techniques, CMS-DP-2020-002 (2020)
2020
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E. A. Moreno, T. Q. Nguyen, J.-R. Vlimant, O. Cerri, H. B. Newman, A. Periwal, M. Spiropulu, J. M. Duarte, and M. Pierini, Interaction networks for the identification of boosted h → b b ¯ h\rightarrow b\overline{b} decays, Phys. Rev. D 102
2020
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H. Qu and L. Gouskos, Jet tagging via particle clouds, Phys. Rev. D 101
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2021
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ATLAS Collaboration, Graph Neural Network Jet Flavour Tagging with the ATLAS Detector, ATL-PHYS-PUB-2022-027 (2022)
2022
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H. Qu, C. Li, and S. Qian, Particle transformer for jet tagging, in International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA , Proceedings of Machine Learning Research, Vol. 162, edited by K. Chaudhuri, S. Jegelka, L. Song, C. Szepesvári, G. Niu, and S. Sabato (PMLR, 2022) pp. 18281–18292
2022
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M. Blondel, Q. Berthet, M. Cuturi, R. Frostig, S. Hoyer, F. Llinares-Lopez, F. Pedregosa, and J.-P. Vert, Efficient and modular implicit differentiation, in NeurIPS , Vol. 35, edited by S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Curran Associates, Inc., 2022) pp. 5230–5242
2022
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2022
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ATLAS Collaboration, Jet Flavour Tagging With GN1 and DL1d. Generator dependence, Run 2 and Run 3 data agreement studies, ATL-PLOT-FTAG-2023-01 (2023b)
2023
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ATLAS Collaboration, Transformer Neural Networks for Identifying Boosted Higgs Bosons decaying into b b ¯ b\bar{b} and c c ¯ c\bar{c} in ATLAS, ATL-PHYS-PUB-2023-021 (2023c)
2023
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K. Goto, T. Suehara, T. Yoshioka, M. Kurata, H. Nagahara, Y. Nakashima, N. Takemura, and M. Iwasaki, Development of a vertex finding algorithm using Recurrent Neural Network, NIM A 1047
2023
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2023
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L. Heinrich and M. Kagan, Differentiable matrix elements with madjax, J. Phy. Conf. Series 2438
2023
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