Search for New Physics Involving Top Quarks at ATLAS

Two searches for new phenomena involving top quarks are presented: a search for a top partner in ttbar events with large missing transverse momentum, and a search for ttbar resonances in proton-proton collisions at a center-of-mass energy of 7 TeV. The measurements are based on 35 pb^-1 and 200 pb^-1 of data collected with the ATLAS detector at the LHC in 2010 and 2011, respectively. No evidence for a signal is observed. The first limits from the LHC are established on the mass of a top partner, excluding a mass of 275 GeV for a neutral particle mass less than 50 GeV and a mass of 300 GeV for a neutral particle mass less than 10 GeV. Using the reconstructed ttbar mass spectrum, limits are set on the production cross-section times branching ratio to ttbar for narrow and wide resonances. For narrow Z' models, the observed 95% C.L. limits range from approximately 38 pb to 3.2 pb for masses going from m_Z' = 500 GeV to m_Z' = 1300 GeV. In Randall-Sundrum models, Kaluza-Klein gluons with masses below 650 GeV are excluded at 95% C.L.Comment: 8 pages, 8 figures, 1 table, proceedings of the Meeting of the Division of Particles and Fields of the American Physical Society, August 9-13, 2011, Brown University, Providence, Rhode Island, to be published electronically on the SLAC Electronic Proceedings repositor

Charming the Higgs

We show that current Higgs data permit a significantly enhanced Higgs coupling to charm pairs, comparable to the Higgs to bottom pairs coupling in the Standard Model, without resorting to additional new physics sources in Higgs production. With a mild level of the latter current data even allow for the Higgs to charm pairs to be the dominant decay channel. An immediate consequence of such a large charm coupling is a significant reduction of the Higgs signal strengths into the known final states as in particular into bottom pairs. This might reduce the visible vector-boson associated Higgs production rate to a level that could compromise the prospects of ever observing it. We however demonstrate that a significant fraction of this reduced signal can be recovered by jet-flavor-tagging targeted towards charm-flavored jets. Finally we argue that an enhanced Higgs to charm pairs coupling can be obtained in various new physics scenarios in the presence of only a mild accidental cancellation between various contributions.Comment: 8 pages, 3 figure

Flows for Flows: Training Normalizing Flows Between Arbitrary Distributions with Maximum Likelihood Estimation

Normalizing flows are constructed from a base distribution with a known density and a diffeomorphism with a tractable Jacobian. The base density of a normalizing flow can be parameterised by a different normalizing flow, thus allowing maps to be found between arbitrary distributions. We demonstrate and explore the utility of this approach and show it is particularly interesting in the case of conditional normalizing flows and for introducing optimal transport constraints on maps that are constructed using normalizing flows

Decorrelation using Optimal Transport

Being able to decorrelate a feature space from protected attributes is an area of active research and study in ethics, fairness, and also natural sciences. We introduce a novel decorrelation method using Convex Neural Optimal Transport Solvers (Cnots), that is able to decorrelate continuous feature space against protected attributes with optimal transport. We demonstrate how well it performs in the context of jet classification in high energy physics, where classifier scores are desired to be decorrelated from the mass of a jet. The decorrelation achieved in binary classification approaches the levels achieved by the state-of-the-art using conditional normalising flows. When moving to multiclass outputs the optimal transport approach performs significantly better than the state-of-the-art, suggesting substantial gains at decorrelating multidimensional feature spaces

FETA: Flow-Enhanced Transportation for Anomaly Detection

Resonant anomaly detection is a promising framework for model-independent searches for new particles. Weakly supervised resonant anomaly detection methods compare data with a potential signal against a template of the Standard Model (SM) background inferred from sideband regions. We propose a means to generate this background template that uses a flow-based model to create a mapping between high-fidelity SM simulations and the data. The flow is trained in sideband regions with the signal region blinded, and the flow is conditioned on the resonant feature (mass) such that it can be interpolated into the signal region. To illustrate this approach, we use simulated collisions from the Large Hadron Collider (LHC) Olympics Dataset. We find that our flow-constructed background method has competitive sensitivity with other recent proposals and can therefore provide complementary information to improve future searches.Comment: 13 pages, 11 figure

ν2\nu^2-Flows: Fast and improved neutrino reconstruction in multi-neutrino final states with conditional normalizing flows

In this work we introduce $\nu^2$-Flows, an extension of the $\nu$-Flows method to final states containing multiple neutrinos. The architecture can natively scale for all combinations of object types and multiplicities in the final state for any desired neutrino multiplicities. In $t\bar{t}$ dilepton events, the momenta of both neutrinos and correlations between them are reconstructed more accurately than when using the most popular standard analytical techniques, and solutions are found for all events. Inference time is significantly faster than competing methods, and can be reduced further by evaluating in parallel on graphics processing units. We apply $\nu^2$-Flows to $t\bar{t}$ dilepton events and show that the per-bin uncertainties in unfolded distributions is much closer to the limit of performance set by perfect neutrino reconstruction than standard techniques. For the chosen double differential observables $\nu^2$-Flows results in improved statistical precision for each bin by a factor of 1.5 to 2 in comparison to the Neutrino Weighting method and up to a factor of four in comparison to the Ellipse approach.Comment: 20 pages, 16 figures, 5 table

\nu-Flows: Conditional Neutrino Regression

We present $\nu$-Flows, a novel method for restricting the likelihood space of neutrino kinematics in high energy collider experiments using conditional normalizing flows and deep invertible neural networks. This method allows the recovery of the full neutrino momentum which is usually left as a free parameter and permits one to sample neutrino values under a learned conditional likelihood given event observations. We demonstrate the success of $\nu$-Flows in a case study by applying it to simulated semileptonic $t\bar{t}$ events and show that it can lead to more accurate momentum reconstruction, particularly of the longitudinal coordinate. We also show that this has direct benefits in a downstream task of jet association, leading to an improvement of up to a factor of 1.41 compared to conventional methods.Comment: 26 pages, 15 figure

Topological Reconstruction of Particle Physics Processes using Graph Neural Networks

We present a new approach, the Topograph, which reconstructs underlying physics processes, including the intermediary particles, by leveraging underlying priors from the nature of particle physics decays and the flexibility of message passing graph neural networks. The Topograph not only solves the combinatoric assignment of observed final state objects, associating them to their original mother particles, but directly predicts the properties of intermediate particles in hard scatter processes and their subsequent decays. In comparison to standard combinatoric approaches or modern approaches using graph neural networks, which scale exponentially or quadratically, the complexity of Topographs scales linearly with the number of reconstructed objects. We apply Topographs to top quark pair production in the all hadronic decay channel, where we outperform the standard approach and match the performance of the state-of-the-art machine learning technique.Comment: 25 pages, 24 figures, 8 table

PC-Droid: Faster diffusion and improved quality for particle cloud generation

Building on the success of PC-JeDi we introduce PC-Droid, a substantially improved diffusion model for the generation of jet particle clouds. By leveraging a new diffusion formulation, studying more recent integration solvers, and training on all jet types simultaneously, we are able to achieve state-of-the-art performance for all types of jets across all evaluation metrics. We study the trade-off between generation speed and quality by comparing two attention based architectures, as well as the potential of consistency distillation to reduce the number of diffusion steps. Both the faster architecture and consistency models demonstrate performance surpassing many competing models, with generation time up to two orders of magnitude faster than PC-JeDi and three orders of magnitude faster than Delphes.Comment: 21 pages, 8 tables, 13 figure