J. Ellis, Outstanding questions: physics beyond the Standard Model. Philos. Trans. R. Soc. Lond. A 370, 818–830 (2012)
ADS
MathSciNet
MATH
Google Scholar
V.M. Abazov et al., A Quasi model independent search for new physics at large transverse momentum. Phys. Rev. D 64, 012004 (2001)
ADS
Article
Google Scholar
D0 Collaboration, Quasi-model-independent search for new high ptphysics at d0. Phys. Rev. Lett. 86(17), 3712–3717 (2001)
CDF Collaboration, Model-independent and quasi-model-independent search for new physics at cdf. Phys. Rev. D 78(1), 012002 (2008)
CDF Collaboration, Global search for new physics with 2.0 fb\(^{-1}\) at cdf. Phys. Rev. D 79(1), 011101 (2009)
H1 Collaboration, A General search for new phenomena in ep scattering at HERA. Phys. Lett. B 602, 14–30 (2004)
H1 Collaboration, A General Search for New Phenomena at HERA. Phys. Lett. B 674, 257–268 (2009)
ATLAS Collaboration, A strategy for a general search for new phenomena using data-derived signal regions and its application within the atlas experiment. Eur. Phys. J. C 79(2), 120 (2019)
CMS Collaboration, Music: a model unspecific search for new physics in proton–proton collisions at \(\sqrt{s} = \) 13 TeV (2020). arXiv:2010.02984
M. Rom ao Crispim, N.F. Castro, R. Pedro, T. Vale, Transferability of deep learning models in searches for new physics at colliders. Phys. Rev. D 101(3), 035042 (2020)
J. Collins, K. Howe, B. Nachman, Anomaly detection for resonant new physics with machine learning. Phys. Rev. Lett. 121(24), 241803 (2018)
ADS
Article
Google Scholar
E.M. Metodiev, B. Nachman, J. Thaler, Classification without labels: learning from mixed samples in high energy physics. J. High Energy Phys. 2017(10), 174 (2017)
ADS
Article
Google Scholar
A. De Simone, T. Jacques, Guiding new physics searches with unsupervised learning. Eur. Phys. J. C 79(4), 1–15 (2019)
Article
Google Scholar
R.T. D’Agnolo, A. Wulzer, Learning new physics from a machine. Phys. Rev. D 99(1), (2019)
O. Cerri, T.Q. Nguyen, M. Pierini, M. Spiropulu, J.R. Vlimant, Variational autoencoders for new physics mining at the large hadron collider. J. High Energy Phys. 2019(5), (2019)
M. Farina, Y. Nakai, D. Shih, Searching for new physics with deep autoencoders. Phys. Rev. D 101(7), (2020)
A. Blance, M. Spannowsky, P. Waite, Adversarially-trained autoencoders for robust unsupervised new physics searches. J. High Energy Phys. 2019(10), (2019)
J. Hajer, Y. Li, T. Liu, H. Wang, Novelty detection meets collider physics. Phys. Rev. D 101(7), (2020)
B. Nachman, D. Shih, Anomaly detection with density estimation. Phys. Rev. D 101(7), (2020)
A. Andreassen, B. Nachman, D. Shih, Simulation assisted likelihood-free anomaly detection. Phys. Rev. D 101(9), (2020)
J.A. Aguilar-Saavedra, J. Collins, R.K. Mishra, A generic anti-QCD jet tagger. J. High Energy Phys. 2017(11), 163 (2017)
ADS
Article
Google Scholar
T. Heimel, G. Kasieczka, T. Plehn, J.M. Thompson, QCD or what. Sci. Post Phys. 6(030), 1808–08979 (2019)
Google Scholar
B.M. Dillon, D.A. Faroughy, J.F. Kamenik, Uncovering latent jet substructure. Phys. Rev. D 100(5), 056002 (2019)
ADS
Article
Google Scholar
R.T. d’Agnolo, G. Grosso, M. Pierini, A. Wulzer, M. Zanetti, Learning multivariate new physics (2019). arXiv:1912.12155
J.H. Collins, K. Howe, B. Nachman, Extending the bump hunt with machine learning (2019). arXiv:1902.02634
O. Amram, C.M. Suarez, Tag n’train: a technique to train improved classifiers on unlabeled data (2020). arXiv:2002.12376
B.M. Dillon, D.A. Faroughy, J.F. Kamenik, M. Szewc, Learning the latent structure of collider events (2020). arXiv:2005.12319
ATLAS Collaboration, G Aad, et al. Dijet resonance search with weak supervision using sqrt(s)= 13 tev pp collisions in the atlas detector. Phys. Rev. Lett. 125(13):131801 (2020). https://doi.org/10.1103/PhysRevLett.125.131801
O. Knapp, G. Dissertori, O. Cerri, T.Q. Nguyen, J.-R. Vlimant, M. Pierini, Adversarially learned anomaly detection on cms open data: re-discovering the top quark (2020). arXiv:2005.01598
M. Goldstein, A. Dengel, Histogram-based outlier score (hbos): a fast unsupervised anomaly detection algorithm (2012)
F.T. Liu, K. M. Ting, Z. Zhou, Isolation forest. In Proceedings of the 2008 Eighth IEEE International Conference on Data Mining, ICDM ’08 (IEEE Computer Society, 2008), pp. 413–422
L. Ruff et al. Deep one-class classification. In Jennifer Dy and Andreas Krause, editors, Proceedings of the 35th International Conference on Machine Learning, volume 80 of Proceedings of Machine Learning Research (Stockholmsmässan, Stockholm, 2018), pp. 4393–4402
J. Alwall et al., The automated computation of tree-level and next-to-leading order differential cross sections, and their matching to parton shower simulations. JHEP 07, 079 (2014)
ADS
Article
Google Scholar
T. Sjöstrand et al., An Introduction to PYTHIA 8.2. Comput. Phys. Commun. 191, 159–177 (2015)
CMS Collaboration, Event generator tunes obtained from underlying event and multiparton scattering measurements. Eur. Phys. J. C 76(3), 155 (2016)
R.D. Ball et al., Parton distributions with LHC data. Nucl. Phys. B 867, 244–289 (2013)
ADS
Article
Google Scholar
J. de Favereau et al., DELPHES 3, a modular framework for fast simulation of a generic collider experiment. JHEP 02, 057 (2014)
Article
Google Scholar
M. Cacciari, G.P. Salam, G. Soyez, The anti-\(k_t\) jet clustering algorithm. JHEP 04, 063 (2008)
ADS
Article
Google Scholar
J.A. Aguilar-Saavedra, Identifying top partners at LHC. JHEP 11, 030 (2009)
ADS
Article
Google Scholar
J.P. Araque, N.F. Castro, J. Santiago, Interpretation of Vector-like Quark Searches: heavy Gluons in Composite Higgs Models. JHEP 11, 120 (2015)
ADS
Article
Google Scholar
G. Durieux, F. Maltoni, C. Zhang, Global approach to top-quark flavor-changing interactions. Phys. Rev. D 91(7), 074017 (2015)
ADS
Article
Google Scholar
ATLAS Collaboration, Search for pair and single production of vectorlike quarks in final states with at least one \(z\) boson decaying into a pair of electrons or muons in \(pp\) collision data collected with the atlas detector at \(\sqrt{s}=13 \rm TeV\). Phys Rev D 98, 112010 (2018)
CMS Collaboration, Search for vector-like quarks in events with two oppositely charged leptons and jets in proton-proton collisions at \(\sqrt{s}=13\) tev. Eur. Phys. J. C 79(4), 364 (2019)
ATLAS collaboration, Search for flavour-changing neutral current top-quark decays \(t\rightarrow qz\) in proton-proton collisions at \(\sqrt{s}=13\) tev with the atlas detector. JHEP 2018(7), 176 (2018)
CMS Collaboration, Search for associated production of a Z boson with a single top quark and for tZ flavour-changing interactions in pp collisions at \( \sqrt{s}=8 \) TeV. JHEP 07, 003 (2017)
M. Bahr et al., Herwig++ Physics and Manual. Eur. Phys. J. C 58, 639–707 (2008)
ADS
Article
Google Scholar
J. Bellm et al., Herwig 7.0/Herwig++ 3.0 release note. Eur. Phys. J. C 76(4), 196 (2016)
K. Hornik, M. Stinchcombe, H. White et al., Multilayer feedforward networks are universal approximators. Neural Netw. 2(5), 359–366 (1989)
Article
Google Scholar
G. Cybenko, Approximation by superpositions of a sigmoidal function. Math. Control Signals Syst. 2(4), 303–314 (1989)
MathSciNet
Article
Google Scholar
K. Hornik, Approximation capabilities of multilayer feedforward networks. Neural Netw. 4(2), 251–257 (1991)
MathSciNet
Article
Google Scholar
Z. Lu, H. Pu, F. Wang, Z. Hu, L. Wang, The expressive power of neural networks: A view from the width. In Advances in neural information processing systems, pp. 6231–6239 (2017)
Y. Zhao, Z. Nasrullah, Z. Li, Pyod: a python toolbox for scalable outlier detection. J. Mach. Learn. Res. 20(96), 1–7 (2019)
Google Scholar
F. Pedregosa et al., Scikit-learn: Machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)
MathSciNet
MATH
Google Scholar
M. Abadi et al., TensorFlow: large-scale machine learning on heterogeneous systems. Software available from tensorflow.org (20150
T. Akiba, S. Sano, T. Yanase, T. Ohta, M. Koyama, Optuna: a next-generation hyperparameter optimization framework. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2623–2631 (2019)
J.S. Bergstra, R. Bardenet, Y. Bengio, B. Kégl, Algorithms for hyper-parameter optimization. Adv. Neural Inf. Process. Syst., 2546–2554 (2011)
D.P. Kingma, J.Ba, Adam: a method for stochastic optimization (2014). arXiv:1412.6980
I. Loshchilov, F. Hutter, Decoupled weight decay regularization (2017). arXiv:1711.05101
J. Shlomi, P. Battaglia, J.-R. Vlimant, Graph neural networks in particle physics (2020). arXiv:2007.13681
D. Guest, J. Collado, P. Baldi, S.-C. Hsu, G. Urban, D. Whiteson, Jet flavor classification in high-energy physics with deep neural networks. Phys. Rev. D 94(11), 112002 (2016)
ADS
Article
Google Scholar
A.L. Read, Presentation of search results: The CL(s) technique. J. Phys. G 28, 2693–2704 (2002)
ADS
Article
Google Scholar
E. Busato, D. Calvet, T. Theveneaux-Pelzer, OpTHyLiC: an optimised tool for hybrid limits computation. Comput. Phys. Commun. 226, 136–150 (2018)
ADS
Article
Google Scholar