Abstract
Experiments at a future e+e− collider will be able to search for new particles with masses below the nominal centre-of-mass energy by analyzing collisions with initial-state radiation (radiative return). We show that machine learning methods that use imperfect or missing training labels can achieve sensitivity to generic new particle production in radiative return events. In addition to presenting an application of the classification without labels (CWoLa) search method in e+e− collisions, our study combines weak supervision with variable-dimensional information by deploying a deep sets neural network architecture. We have also investigated some of the experimental aspects of anomaly detection in radiative return events and discuss these in the context of future detector design.
References
J. Button, G. R. Kalbfleisch, G. R. Lynch, B. C. Maglić, A. H. Rosenfeld and M. L. Stevenson, Pion-pion interaction in the reaction barp + p → 2π+ + 2π− + nπ0, Phys. Rev. 126 (1962) 1858 [INSPIRE].
ATLAS collaboration, Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC, Phys. Lett. B 716 (2012) 1 [arXiv:1207.7214] [INSPIRE].
CMS collaboration, Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC, Phys. Lett. B 716 (2012) 30 [arXiv:1207.7235] [INSPIRE].
R. T. D’Agnolo and A. Wulzer, Learning new physics from a machine, Phys. Rev. D 99 (2019) 015014 [arXiv:1806.02350] [INSPIRE].
J. H. Collins, K. Howe and B. Nachman, Anomaly detection for resonant new physics with machine learning, Phys. Rev. Lett. 121 (2018) 241803 [arXiv:1805.02664] [INSPIRE].
J. H. Collins, K. Howe and B. Nachman, Extending the search for new resonances with machine learning, Phys. Rev. D 99 (2019) 014038 [arXiv:1902.02634] [INSPIRE].
R. T. D’Agnolo, G. Grosso, M. Pierini, A. Wulzer and M. Zanetti, Learning multivariate new physics, Eur. Phys. J. C 81 (2021) 89 [arXiv:1912.12155] [INSPIRE].
M. Farina, Y. Nakai and D. Shih, Searching for new physics with deep autoencoders, Phys. Rev. D 101 (2020) 075021 [arXiv:1808.08992] [INSPIRE].
T. Heimel, G. Kasieczka, T. Plehn and J. M. Thompson, QCD or what?, SciPost Phys. 6 (2019) 030 [arXiv:1808.08979] [INSPIRE].
T. S. Roy and A. H. Vijay, A robust anomaly finder based on autoencoders, arXiv:1903.02032 [INSPIRE].
O. Cerri, T. Q. Nguyen, M. Pierini, M. Spiropulu and J.-R. Vlimant, Variational autoencoders for new physics mining at the Large Hadron Collider, JHEP 05 (2019) 036 [arXiv:1811.10276] [INSPIRE].
A. Blance, M. Spannowsky and P. Waite, Adversarially-trained autoencoders for robust unsupervised new physics searches, JHEP 10 (2019) 047 [arXiv:1905.10384] [INSPIRE].
J. Hajer, Y.-Y. Li, T. Liu and H. Wang, Novelty detection meets collider physics, Phys. Rev. D 101 (2020) 076015 [arXiv:1807.10261] [INSPIRE].
A. De Simone and T. Jacques, Guiding new physics searches with unsupervised learning, Eur. Phys. J. C 79 (2019) 289 [arXiv:1807.06038] [INSPIRE].
A. Casa and G. Menardi, Nonparametric semisupervised classification for signal detection in high energy physics, arXiv:1809.02977 [INSPIRE].
B. M. Dillon, D. A. Faroughy and J. F. Kamenik, Uncovering latent jet substructure, Phys. Rev. D 100 (2019) 056002 [arXiv:1904.04200] [INSPIRE].
A. Andreassen, B. Nachman and D. Shih, Simulation assisted likelihood-free anomaly detection, Phys. Rev. D 101 (2020) 095004 [arXiv:2001.05001] [INSPIRE].
B. Nachman and D. Shih, Anomaly detection with density estimation, Phys. Rev. D 101 (2020) 075042 [arXiv:2001.04990] [INSPIRE].
J. A. Aguilar-Saavedra, J. H. Collins and R. K. Mishra, A generic anti-QCD jet tagger, JHEP 11 (2017) 163 [arXiv:1709.01087] [INSPIRE].
M. Romão Crispim, N. F. Castro, R. Pedro and T. Vale, Transferability of deep learning models in searches for new physics at colliders, Phys. Rev. D 101 (2020) 035042 [arXiv:1912.04220] [INSPIRE].
M. Crispim Romão, N. F. Castro, J. G. Milhano, R. Pedro and T. Vale, Use of a generalized energy Mover’s distance in the search for rare phenomena at colliders, Eur. Phys. J. C 81 (2021) 192 [arXiv:2004.09360] [INSPIRE].
O. Knapp, O. Cerri, G. Dissertori, T. Q. Nguyen, M. Pierini and J.-R. Vlimant, Adversarially learned anomaly detection on CMS open data: re-discovering the top quark, Eur. Phys. J. Plus 136 (2021) 236 [arXiv:2005.01598] [INSPIRE].
ATLAS collaboration, Dijet resonance search with weak supervision using \( \sqrt{s} \) = 13 TeV pp collisions in the ATLAS detector, Phys. Rev. Lett. 125 (2020) 131801 [arXiv:2005.02983] [INSPIRE].
B. M. Dillon, D. A. Faroughy, J. F. Kamenik and M. Szewc, Learning the latent structure of collider events, JHEP 10 (2020) 206 [arXiv:2005.12319] [INSPIRE].
M. Crispim Romão, N. F. Castro and R. Pedro, Finding new physics without learning about it: anomaly detection as a tool for searches at colliders, Eur. Phys. J. C 81 (2021) 27 [Erratum ibid. 81 (2021) 1020] [arXiv:2006.05432] [INSPIRE].
O. Amram and C. M. Suarez, Tag N’ Train: a technique to train improved classifiers on unlabeled data, JHEP 01 (2021) 153 [arXiv:2002.12376] [INSPIRE].
T. Cheng, J.-F. Arguin, J. Leissner-Martin, J. Pilette and T. Golling, Variational autoencoders for anomalous jet tagging, arXiv:2007.01850 [INSPIRE].
C. K. Khosa and V. Sanz, Anomaly awareness, arXiv:2007.14462 [INSPIRE].
J. A. Aguilar-Saavedra, F. R. Joaquim and J. F. Seabra, Mass Unspecific Supervised Tagging (MUST) for boosted jets, JHEP 03 (2021) 012 [Erratum ibid. 04 (2021) 133] [arXiv:2008.12792] [INSPIRE].
K. Benkendorfer, L. L. Pottier and B. Nachman, Simulation-assisted decorrelation for resonant anomaly detection, Phys. Rev. D 104 (2021) 035003 [arXiv:2009.02205] [INSPIRE].
A. A. Pol, V. Berger, G. Cerminara, C. Germain and M. Pierini, Anomaly detection with conditional variational autoencoders, in Eighteenth international conference on machine learning and applications, (2020) [arXiv:2010.05531] [INSPIRE].
V. Mikuni and F. Canelli, Unsupervised clustering for collider physics, Phys. Rev. D 103 (2021) 092007 [arXiv:2010.07106] [INSPIRE].
M. van Beekveld et al., Combining outlier analysis algorithms to identify new physics at the LHC, JHEP 09 (2021) 024 [arXiv:2010.07940] [INSPIRE].
S. E. Park, D. Rankin, S.-M. Udrescu, M. Yunus and P. Harris, Quasi anomalous knowledge: searching for new physics with embedded knowledge, JHEP 06 (2021) 030 [arXiv:2011.03550] [INSPIRE].
D. A. Faroughy, Uncovering hidden new physics patterns in collider events using Bayesian probabilistic models, PoS ICHEP2020 (2021) 238 [arXiv:2012.08579] [INSPIRE].
G. Stein, U. Seljak and B. Dai, Unsupervised in-distribution anomaly detection of new physics through conditional density estimation, in 34th conference on neural information processing systems, (2020) [arXiv:2012.11638] [INSPIRE].
G. Kasieczka et al., The LHC olympics 2020 a community challenge for anomaly detection in high energy physics, Rept. Prog. Phys. 84 (2021) 124201 [arXiv:2101.08320] [INSPIRE].
A. Blance and M. Spannowsky, Unsupervised event classification with graphs on classical and photonic quantum computers, JHEP 08 (2021) 170 [arXiv:2103.03897] [INSPIRE].
B. Bortolato, B. M. Dillon, J. F. Kamenik and A. Smolkovič, Bump hunting in latent space, arXiv:2103.06595 [INSPIRE].
J. H. Collins, P. Martín-Ramiro, B. Nachman and D. Shih, Comparing weak- and unsupervised methods for resonant anomaly detection, Eur. Phys. J. C 81 (2021) 617 [arXiv:2104.02092] [INSPIRE].
B. M. Dillon, T. Plehn, C. Sauer and P. Sorrenson, Better latent spaces for better autoencoders, SciPost Phys. 11 (2021) 061 [arXiv:2104.08291] [INSPIRE].
T. Finke, M. Krämer, A. Morandini, A. Mück and I. Oleksiyuk, Autoencoders for unsupervised anomaly detection in high energy physics, JHEP 06 (2021) 161 [arXiv:2104.09051] [INSPIRE].
O. Atkinson, A. Bhardwaj, C. Englert, V. S. Ngairangbam and M. Spannowsky, Anomaly detection with convolutional graph neural networks, JHEP 08 (2021) 080 [arXiv:2105.07988] [INSPIRE].
A. Kahn, J. Gonski, I. Ochoa, D. Williams and G. Brooijmans, Anomalous jet identification via sequence modeling, 2021 JINST 16 P08012 [arXiv:2105.09274] [INSPIRE].
T. Aarrestad et al., The dark machines anomaly score challenge: benchmark data and model independent event classification for the Large Hadron Collider, SciPost Phys. 12 (2022) 043 [arXiv:2105.14027] [INSPIRE].
T. Dorigo, M. Fumanelli, C. Maccani, M. Mojsovska, G. C. Strong and B. Scarpa, RanBox: anomaly detection in the Copula space, arXiv:2106.05747 [INSPIRE].
S. Caron, L. Hendriks and R. Verheyen, Rare and different: anomaly scores from a combination of likelihood and out-of-distribution models to detect new physics at the LHC, SciPost Phys. 12 (2022) 077 [arXiv:2106.10164] [INSPIRE].
E. Govorkova, E. Puljak, T. Aarrestad, M. Pierini, K. A. Woźniak and J. Ngadiuba, LHC physics dataset for unsupervised new physics detection at 40 MHz, arXiv:2107.02157 [INSPIRE].
G. Kasieczka, B. Nachman and D. Shih, New methods and datasets for group anomaly detection from fundamental physics, in Conference on knowledge discovery and data mining, (2021) [arXiv:2107.02821] [INSPIRE].
M. Feickert and B. Nachman, A living review of machine learning for particle physics, arXiv:2102.02770 [INSPIRE].
D. Shih, M. R. Buckley, L. Necib and J. Tamanas, Via Machinae: searching for stellar streams using unsupervised machine learning, Mon. Not. Roy. Astron. Soc. 509 (2021) 5992 [arXiv:2104.12789] [INSPIRE].
FCC collaboration, FCC physics opportunities: Future Circular Collider conceptual design report volume 1, Eur. Phys. J. C 79 (2019) 474 [INSPIRE].
FCC collaboration, FCC-ee: the lepton collider. Future Circular Collider conceptual design report volume 2, Eur. Phys. J. ST 228 (2019) 261 [INSPIRE].
FCC collaboration, FCC-hh: the hadron collider. Future Circular Collider conceptual design report volume 3, Eur. Phys. J. ST 228 (2019) 755 [INSPIRE].
T. Behnke et al. eds., The International Linear Collider technical design report — volume 1: executive summary, arXiv:1306.6327 [INSPIRE].
H. Baer et al. eds., The International Linear Collider technical design report — volume 2: physics, arXiv:1306.6352 [INSPIRE].
C. Adolphsen et al. eds., The International Linear Collider technical design report — volume 3.I: accelerator & in the technical design phase, arXiv:1306.6353 [INSPIRE].
C. Adolphsen et al. eds., The International Linear Collider technical design report — volume 3.II: accelerator baseline design, arXiv:1306.6328 [INSPIRE].
H. Abramowicz et al., The International Linear Collider technical design report — volume 4: detectors, arXiv:1306.6329 [INSPIRE].
International Linear Collider International Development Team collaboration, Proposal for the ILC preparatory laboratory (pre-lab), arXiv:2106.00602 [INSPIRE].
CEPC Study Group collaboration, CEPC conceptual design report: volume 1 — accelerator, arXiv:1809.00285 [INSPIRE].
CEPC Study Group collaboration, CEPC conceptual design report: volume 2 — physics & detector, arXiv:1811.10545 [INSPIRE].
L. Linssen, A. Miyamoto, M. Stanitzki and H. Weerts eds., Physics and detectors at CLIC: CLIC conceptual design report, CERN yellow report CERN-2012-003, CERN, Geneva, Switzerland (2012) [arXiv:1202.5940] [INSPIRE].
CLICdp and CLIC collaborations, The Compact Linear Collider (CLIC) — 2018 summary report, arXiv:1812.06018 [INSPIRE].
P. T. Komiske, E. M. Metodiev, B. Nachman and M. D. Schwartz, Learning to classify from impure samples with high-dimensional data, Phys. Rev. D 98 (2018) 011502 [arXiv:1801.10158] [INSPIRE].
A. Denig, The radiative return: a review of experimental results, Nucl. Phys. B Proc. Suppl. 162 (2006) 81 [hep-ex/0611024] [INSPIRE].
W. Kluge, Initial state radiation: a success story, Nucl. Phys. B Proc. Suppl. 181-182 (2008) 280 [arXiv:0805.4708] [INSPIRE].
H. Czyz, A. Grzelinska and J. H. Kühn, Narrow resonances studies with the radiative return method, Phys. Rev. D 81 (2010) 094014 [arXiv:1002.0279] [INSPIRE].
V. P. Druzhinin, S. I. Eidelman, S. I. Serednyakov and E. P. Solodov, Hadron production via e+ e− collisions with initial state radiation, Rev. Mod. Phys. 83 (2011) 1545 [arXiv:1105.4975] [INSPIRE].
M. Karliner, M. Low, J. L. Rosner and L.-T. Wang, Radiative return capabilities of a high-energy, high-luminosity e+ e− collider, Phys. Rev. D 92 (2015) 035010 [arXiv:1503.07209] [INSPIRE].
G. Li, Z. Li, Y. Wang and Y. Wang, Improving the measurement of the Higgs boson-gluon coupling using convolutional neural networks at e+ e− colliders, Phys. Rev. D 100 (2019) 116013 [arXiv:1901.09391] [INSPIRE].
L. Li, Y.-Y. Li, T. Liu and S.-J. Xu, Learning physics at future e− e+ colliders with machine, JHEP 10 (2020) 018 [arXiv:2004.15013] [INSPIRE].
M. Zaheer et al., Deep sets, in Proceedings of the 31st international conference on neural information processing systems, NIPS’17, Red Hook, NY, U.S.A., Curran Associates Inc., U.S.A. (2017), p. 3394.
P. T. Komiske, E. M. Metodiev and J. Thaler, Energy flow networks: deep sets for particle jets, JHEP 01 (2019) 121 [arXiv:1810.05165] [INSPIRE].
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 (2014) 079 [arXiv:1405.0301] [INSPIRE].
T. Sjöstrand et al., An introduction to PYTHIA 8.2, Comput. Phys. Commun. 191 (2015) 159 [arXiv:1410.3012] [INSPIRE].
M. Selvaggi, DELPHES 3: a modular framework for fast-simulation of generic collider experiments, J. Phys. Conf. Ser. 523 (2014) 012033 [INSPIRE].
ILD Concept Group collaboration, International Large Detector: interim design report, arXiv:2003.01116 [INSPIRE].
M. Cacciari, G. P. Salam and G. Soyez, The anti-kt jet clustering algorithm, JHEP 04 (2008) 063 [arXiv:0802.1189] [INSPIRE].
M. Cacciari and G. P. Salam, Dispelling the N 3 myth for the kt jet-finder, Phys. Lett. B 641 (2006) 57 [hep-ph/0512210] [INSPIRE].
M. Cacciari, G. P. Salam and G. Soyez, FastJet user manual, Eur. Phys. J. C 72 (2012) 1896 [arXiv:1111.6097] [INSPIRE].
T. Barklow et al., ILC operating scenarios, arXiv:1506.07830 [INSPIRE].
E. M. Metodiev, B. Nachman and J. Thaler, Classification without labels: learning from mixed samples in high energy physics, JHEP 10 (2017) 174 [arXiv:1708.02949] [INSPIRE].
J. Thaler and K. Van Tilburg, Identifying boosted objects with N -subjettiness, JHEP 03 (2011) 015 [arXiv:1011.2268] [INSPIRE].
J. Thaler and K. Van Tilburg, Maximizing boosted top identification by minimizing N -subjettiness, JHEP 02 (2012) 093 [arXiv:1108.2701] [INSPIRE].
F. Chollet et al., Keras, https://github.com/fchollet/keras, (2015).
M. Abadi et al., TensorFlow: large-scale machine learning on heterogeneous distributed systems, arXiv:1603.04467 [INSPIRE].
TensorFlow webpage, https://www.tensorflow.org/.
D. P. Kingma and J. Ba, Adam: a method for stochastic optimization, 12, 2014 [arXiv:1412.6980] [INSPIRE].
J. Duchi, E. Hazan and Y. Singer, Adaptive subgradient methods for online learning and stochastic optimization, J. Mach. Learn. Res. 12 (2011) 2121.
G. Hinton, N. Srivastava and K. Swersky, Neural networks for machine learning: lecture 6a, http://www.cs.toronto.edu/∼tijmen/csc321/slides/lecture_slides_lec6.pdf.
A. Banfi, G. P. Salam and G. Zanderighi, Phenomenology of event shapes at hadron colliders, JHEP 06 (2010) 038 [arXiv:1001.4082] [INSPIRE].
ALEPH collaboration, Studies of quantum chromodynamics with the ALEPH detector, Phys. Rept. 294 (1998) 1 [INSPIRE].
OPAL collaboration, QCD studies with e+ e− annihilation data at 130 GeV and 136 GeV, Z. Phys. C 72 (1996) 191 [INSPIRE].
ATLAS collaboration, Measurement of event shapes at large momentum transfer with the ATLAS detector in pp collisions at \( \sqrt{s} \) = 7 TeV, Eur. Phys. J. C 72 (2012) 2211 [arXiv:1206.2135] [INSPIRE].
CMS collaboration, Event shape variables measured using multijet final states in proton-proton collisions at \( \sqrt{s} \) = 13 TeV, JHEP 12 (2018) 117 [arXiv:1811.00588] [INSPIRE].
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Gonski, J., Lai, J., Nachman, B. et al. High-dimensional anomaly detection with radiative return in e+e− collisions. J. High Energ. Phys. 2022, 156 (2022). https://doi.org/10.1007/JHEP04(2022)156
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DOI: https://doi.org/10.1007/JHEP04(2022)156
Keywords
- Beyond Standard Model
- e +-e − Experiments
- Particle and Resonance Production