We present the first study of anti-isolated Upsilon decays to two muons (<math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><mrow><mi mathvariant="normal">ϒ</mi><mo stretchy="false">→</mo><msup><mrow><mi>μ</mi></mrow><mrow><mo>+</mo></mrow></msup><msup><mrow><mi>μ</mi></mrow><mrow><mo>−</mo></mrow></msup></mrow></math>) in proton-proton collisions at the Large Hadron Collider. Using a machine learning (ML)-based anomaly detection strategy, we “rediscover” the <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><mrow><mi mathvariant="normal">ϒ</mi></mrow></math> in 13 TeV CMS Open Data from 2016, despite overwhelming anti-isolated backgrounds. We elevate the signal significance to <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><mrow><mn>6.4</mn><mi>σ</mi></mrow></math> using these methods, starting from <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><mrow><mn>1.6</mn><mi>σ</mi></mrow></math> using the dimuon mass spectrum alone. Moreover, we demonstrate improved sensitivity from using an ML-based estimate of the multifeature likelihood compared to traditional “cut-and-count” methods. This is the first ever detection of anti-isolated Upsilons, which can be useful in the study of heavy-flavor fragmentation in quantum chromodynamics. Our Letter demonstrates that it is possible and practical to find real signals in experimental collider data using ML-based anomaly detection, and we distill a readily accessible benchmark dataset from the CMS Open Data to facilitate future anomaly detection developments.

United States

Rikab Gambhir

Radha Mastandrea

Benjamin Nachman

Jesse Thaler

Isolating Unisolated Upsilons with Anomaly Detection in CMS Open Data

Energy correlators have recently come to the forefront of jet substructure studies at colliders due to their remarkable properties: they naturally separate physics at different scales, are robust to contamination from soft radiation, and offer a direct connection with quantum field theory. The current parametrization used for energy correlators, however, is based on redundant pairwise angles with complex phase space restrictions. In this Letter, we introduce a new parametrization of energy correlators that features a simpler phase space structure and preserves information about the orientation of jet constituents. Further, our parametrization drastically reduces the computational cost to compute energy correlators on experimental data; whereas the time to compute a traditional projected <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><mi>N</mi></math>-point energy correlator scales as <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><msup><mi>M</mi><mi>N</mi></msup><mo>/</mo><mi>N</mi><mo>!</mo></math> on a jet with <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><mi>M</mi></math> particles, our new parametrization achieves a scaling of <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><msup><mi>M</mi><mn>2</mn></msup><mi>ln</mi><mi>M</mi></math>, remarkably independently of <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><mi>N</mi></math>. Even for <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><mi>N</mi><mo>=</mo><mn>3</mn></math>, this improved scaling is particularly important for studies of heavy ion collisions, and higher values of <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><mi>N</mi></math> will enable new qualitative understanding of gauge theories. Theoretical calculations for our new energy correlators differ from those of traditional parametrizations only at next-to-next-to-leading logarithmic accuracy and beyond, and we expect that our simpler phase space structure will simplify those calculations. We also discuss how to extend our parametrization to resolved <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><mi>N</mi></math>-point energy correlators that encode angular distances between greater numbers of particles, yielding intuitive visualizations of jet substructure that are qualitatively different for different jet samples. We propose two possible generalizations for probing multiprong jets and testing jet scaling behavior.

Samuel Alipour-fard

Netherlands

Ankita Budhraja

Wouter J. Waalewijn

New Angles on Energy Correlators

The Cabibbo-Kobayashi-Maskawa (CKM) matrix, which controls flavor mixing between the three generations of quark fermions, is a key input to the standard model of particle physics. In this paper, we identify a surprising connection between quantum entanglement and the degree of quark mixing. Focusing on a specific limit of <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><mn>2</mn><mo stretchy="false">→</mo><mn>2</mn></math> quark scattering mediated by electroweak bosons, we find that the quantum entanglement generated by scattering is minimized when the CKM matrix is almost (but not exactly) diagonal, in qualitative agreement with observation. With the discovery of neutrino masses and mixings, additional angles are needed to parametrize the Pontecorvo-Maki-Nakagawa-Sakata (PMNS) matrix in the lepton sector. Applying the same logic, we find that quantum entanglement is minimized when the PMNS matrix features two large angles and a smaller one, again in qualitative agreement with observation, plus a hint for suppressed <math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><mi>C</mi><mi>P</mi></math> violation. We speculate on the (unlikely but tantalizing) possibility that minimization of quantum entanglement might be a fundamental principle that determines particle physics input parameters.

Sokratis Trifinopoulos

Flavor patterns of fundamental particles from quantum entanglement?

The Energy Mover’s Distance (EMD) has seen use in collider physics as a metric between events and as a geometric method of defining infrared and collinear safe observables. Recently, the Spectral Energy Mover’s Distance (SEMD) has been proposed as a more analytically tractable alternative to the EMD. In this work, we obtain a closed-form expression for the Riemannian-like $\textit{p}$ = 2 SEMD metric between events, eliminating the need to numerically solve an optimal transport problem. Additionally, we show how the SEMD can be used to define event and jet shape observables by minimizing the distance between events and parameterized energy flows (similar to the EMD), and we obtain closed-form expressions for several of these observables. We also present the Specter framework, an efficient and highly parallelized implementation of the SEMD metric and SEMD-derived shape observables as an analogue of the previously-introduced Shaper for EMD-based computations. We demonstrate that computing the SEMD with Specter can be up to a thousand times faster than computing the EMD with standard optimal transport libraries.

Andrew Larkoski

SPECTER: efficient evaluation of the spectral EMD

Deconvolving (“unfolding”) detector distortions is a critical step in the comparison of cross-section measurements with theoretical predictions in particle and nuclear physics. However, most existing approaches require histogram binning while many theoretical predictions are at the level of statistical moments. We develop a new approach to directly unfold distribution moments as a function of another observable without having to first discretize the data. Our moment unfolding technique uses machine learning and is inspired by Boltzmann weight factors and generative adversarial networks (GANs). We demonstrate the performance of this approach using jet substructure measurements in collider physics. With this illustrative example, we find that our moment unfolding protocol is more precise than bin-based approaches and is as or more precise than completely unbinned methods.

Krish Desai

Moment extraction using an unfolding protocol without binning

Many machine learning applications involve learning a latent representation of data, which is often high-dimensional and difficult to directly interpret. In this work, we propose “moment pooling,” a natural extension of deep sets networks which drastically decreases the latent space dimensionality of these networks while maintaining or even improving performance. Moment pooling generalizes the summation in deep sets to arbitrary multivariate moments, which enables the model to achieve a much higher effective latent dimensionality for a fixed learned latent space dimension. We demonstrate moment pooling on the collider physics task of quark/gluon jet classification by extending energy flow networks (EFNs) to moment EFNs. We find that moment EFNs with latent dimensions as small as 1 perform similarly to ordinary EFNs with higher latent dimension. This small latent dimension allows for the internal representation to be directly visualized and interpreted, which in turn enables the learned internal jet representation to be extracted in closed form.

Athis Osathapan

Streamlining latent spaces in machine learning using moment pooling

To maximize the discovery potential of high-energy colliders, experimental searches should be sensitive to unforeseen new physics scenarios. This goal has motivated the use of machine learning for unsupervised anomaly detection. In this paper, we introduce a new anomaly detection strategy called force: factorized observables for regressing conditional expectations. Our approach is based on the inductive bias of factorization, which is the idea that the physics governing different energy scales can be treated as approximately independent. Assuming factorization holds separately for signal and background processes, the appearance of nontrivial correlations between low- and high-energy observables is a robust indicator of new physics. Under the most restrictive form of factorization, a machine-learned model trained to identify such correlations will in fact converge to the optimal new physics classifier. We test force on a benchmark anomaly detection task for the Large Hadron Collider involving collimated sprays of particles called jets. By teasing out correlations between the kinematics and substructure of jets, our method can reliably extract percent-level signal fractions. This strategy for uncovering new physics adds to the growing toolbox of anomaly detection methods for collider physics with a complementary set of assumptions.

Eric M. Metodiev

Raymond Wynne

Anomaly detection in collider physics via factorized observables

The first measurement of two-particle angular correlations for charged particles with LEP-II data is presented. The study is performed using archived hadronic <math><msup><mrow><mi>e</mi></mrow><mrow><mo>+</mo></mrow></msup><msup><mrow><mi>e</mi></mrow><mrow><mo>−</mo></mrow></msup></math> data collected by ALEPH at center-of-mass energies up to 209 GeV, above the <math><msup><mrow><mi>W</mi></mrow><mrow><mo>+</mo></mrow></msup><msup><mrow><mi>W</mi></mrow><mrow><mo>−</mo></mrow></msup></math> production threshold, which provide access to unprecedented charged-particle multiplicities and more complex color-string configurations if compared to previous measurements at LEP-I energies. An intriguing long-range near-side excess is observed in the correlation function measured with respect to the thrust axis in the highest multiplicity interval (<math><msub><mrow><mi>N</mi></mrow><mrow><mi>trk</mi></mrow></msub><mo>≥</mo><mn>50</mn></math>). Such a structure is not predicted by the Monte-Carlo simulation. The harmonic anisotropy coefficients <math><msub><mrow><mi>v</mi></mrow><mrow><mi>n</mi></mrow></msub></math>, which result from the Fourier expansion of the two-particle correlation functions, were also measured for the first time in <math><msup><mrow><mi>e</mi></mrow><mrow><mo>+</mo></mrow></msup><msup><mrow><mi>e</mi></mrow><mrow><mo>−</mo></mrow></msup></math> data, and compared to pythia 6 predictions and to the results obtained in proton-proton collisions. The results presented in the Letter provide novel experimental constraints on the formation of collective phenomena in point-like <math><msup><mrow><mi>e</mi></mrow><mrow><mo>+</mo></mrow></msup><msup><mrow><mi>e</mi></mrow><mrow><mo>−</mo></mrow></msup></math> collisions.

Yu-Chen Chen

Yi Chen

Anthony Badea

Austin Baty

Gian Michele Innocenti

Italy

Marcello Maggi

Christopher McGinn

Michael Peters

Tzu-An Sheng

Yen-Jie Lee

Long-range near-side correlation in collisions at 183-209 GeV with ALEPH archived data

Infrared and collinear (IRC) safety has long been used a proxy for robustness when developing new jet substructure observables. This guiding philosophy has been carried into the deep learning era, where IRC-safe neural networks have been used for many jet studies. For graph-based neural networks, the most straightforward way to achieve IRC safety is to weight particle inputs by their energies. However, energy-weighting by itself does not guarantee that perturbative calculations of machine-learned observables will enjoy small nonperturbative corrections. In this paper, we demonstrate the sensitivity of IRC-safe networks to nonperturbative effects, by training an energy flow network (EFN) to maximize its sensitivity to hadronization. We then show how to construct Lipschitz energy flow networks (<math><mrow><mi>L</mi></mrow></math>-EFNs), which are both IRC safe and relatively insensitive to nonperturbative corrections. We demonstrate the performance of <math><mi>L</mi></math>-EFNs on generated samples of quark and gluon jets, and showcase fascinating differences between the learned latent representations of EFNs and <math><mi>L</mi></math>-EFNs.

Samuel Bright-Thonney

Infrared-safe energy weighting does not guarantee small nonperturbative effects

We propose extensions of the anti-<math><msub><mi>k</mi><mi>t</mi></msub></math> and Cambridge/Aachen hierarchical jet clustering algorithms that are designed to retain the exact jet kinematics of these algorithms, while providing an infrared-and-collinear-safe definition of jet flavor at any fixed order in perturbation theory. Central to our approach is a new technique called interleaved flavor neutralization (IFN), whereby the treatment of flavor is integrated with, but distinct from, the kinematic clustering. IFN allows flavor information to be meaningfully accessed at each stage of the clustering sequence, which enables a consistent assignment of flavor both to individual jets and to their substructure. We validate the IFN approach using a dedicated framework for fixed-order tests of infrared and collinear safety, which also reveals unanticipated issues in earlier approaches to flavored jet clustering. We briefly explore the phenomenological impact of IFN with anti-<math><msub><mi>k</mi><mi>t</mi></msub></math> jets for benchmark tasks at the Large Hadron Collider.

United Kingdom

Fabrizio Caola

Radosław Grabarczyk

Maxwell L. Hutt

Gavin P. Salam

Ludovic Scyboz

Flavored jets with exact anti-<math><msub><mi>k</mi><mi>t</mi></msub></math> kinematics and tests of infrared and collinear safety