Tevatron greatest hits

The Tevatron collider led the World energy frontier program in particle physics during the late 20th and early 21st centuries. During this exciting period the standard model of particle physics was in its final stages of development and the search for physics beyond the standard model became one of the main research topics. In this review article we summarize the design and performance of the Tevatron collider and its two detectors, CDF and D0, as well as their evolution. Highlights of the Tevatron scientific results are provided, including the discovery of the top quark and measurements of its properties, studies and discoveries of the particles containing heavy quarks, precision studies of the strong and electroweak forces, searches for beyond the standard model particles and interactions, as well as the hunt for the Higgs boson.

Learning new physics efficiently with nonparametric methods

We present a machine learning approach for model-independent new physics searches. The corresponding algorithm is powered by recent large-scale implementations of kernel methods, nonparametric learning algorithms that can approximate any continuous function given enough data. Based on the original proposal by D'Agnolo and Wulzer (Phys Rev D 99(1):015014, 2019, arXiv:1806.02350 [hep-ph]), the model evaluates the compatibility between experimental data and a reference model, by implementing a hypothesis testing procedure based on the likelihood ratio. Model-independence is enforced by avoiding any prior assumption about the presence or shape of new physics components in the measurements. We show that our approach has dramatic advantages compared to neural network implementations in terms of training times and computational resources, while maintaining comparable performances. In particular, we conduct our tests on higher dimensional datasets, a step forward with respect to previous studies.

Learning new physics from an imperfect machine

We show how to deal with uncertainties on the Standard Model predictions in an agnostic new physics search strategy that exploits artificial neural networks. Our approach builds directly on the specific Maximum Likelihood ratio treatment of uncertainties as nuisance parameters for hypothesis testing that is routinely employed in high-energy physics. After presenting the conceptual foundations of our method, we first illustrate all aspects of its implementation and extensively study its performances on a toy one-dimensional problem. We then show how to implement it in a multivariate setup by studying the impact of two typical sources of experimental uncertainties in two-body final states at the LHC.

The LHC Olympics 2020 a community challenge for anomaly detection in high energy physics

A new paradigm for data-driven, model-agnostic new physics searches at colliders is emerging, and aims to leverage recent breakthroughs in anomaly detection and machine learning. In order to develop and benchmark new anomaly detection methods within this framework, it is essential to have standard datasets. To this end, we have created the LHC Olympics 2020, a community challenge accompanied by a set of simulated collider events. Participants in these Olympics have developed their methods using an R&D dataset and then tested them on black boxes: datasets with an unknown anomaly (or not). Methods made use of modern machine learning tools and were based on unsupervised learning (autoencoders, generative adversarial networks, normalizing flows), weakly supervised learning, and semi-supervised learning. This paper will review the LHC Olympics 2020 challenge, including an overview of the competition, a description of methods deployed in the competition, lessons learned from the experience, and implications for data analyses with future datasets as well as future colliders.

MUSiC: a model-unspecific search for new physics in proton-proton collisions at s = 13 TeV

Results of the Model Unspecific Search in CMS (MUSiC), using proton-proton collision data recorded at the LHC at a centre-of-mass energy of 13 TeV , corresponding to an integrated luminosity of 35.9 fb - 1 , are presented. The MUSiC analysis searches for anomalies that could be signatures of physics beyond the standard model. The analysis is based on the comparison of observed data with the standard model prediction, as determined from simulation, in several hundred final states and multiple kinematic distributions. Events containing at least one electron or muon are classified based on their final state topology, and an automated search algorithm surveys the observed data for deviations from the prediction. The sensitivity of the search is validated using multiple methods. No significant deviations from the predictions have been observed. For a wide range of final state topologies, agreement is found between the data and the standard model simulation. This analysis complements dedicated search analyses by significantly expanding the range of final states covered using a model independent approach with the largest data set to date to probe phase space regions beyond the reach of previous general searches.

Dijet Resonance Search with Weak Supervision Using sqrt[s]=13 TeV pp Collisions in the ATLAS Detector

This Letter describes a search for narrowly resonant new physics using a machine-learning anomaly detection procedure that does not rely on signal simulations for developing the analysis selection. Weakly supervised learning is used to train classifiers directly on data to enhance potential signals. The targeted topology is dijet events and the features used for machine learning are the masses of the two jets. The resulting analysis is essentially a three-dimensional search A→BC, for m_{A}∼O(TeV), m_{B},m_{C}∼O(100  GeV) and B, C are reconstructed as large-radius jets, without paying a penalty associated with a large trials factor in the scan of the masses of the two jets. The full run 2 sqrt[s]=13  TeV pp collision dataset of 139  fb^{-1} recorded by the ATLAS detector at the Large Hadron Collider is used for the search. There is no significant evidence of a localized excess in the dijet invariant mass spectrum between 1.8 and 8.2 TeV. Cross-section limits for narrow-width A, B, and C particles vary with m_{A}, m_{B}, and m_{C}. For example, when m_{A}=3  TeV and m_{B}≳200  GeV, a production cross section between 1 and 5 fb is excluded at 95% confidence level, depending on m_{C}. For certain masses, these limits are up to 10 times more sensitive than those obtained by the inclusive dijet search. These results are complementary to the dedicated searches for the case that B and C are standard model bosons.

Anomaly Detection for Resonant New Physics with Machine Learning

Despite extensive theoretical motivation for physics beyond the standard model (BSM) of particle physics, searches at the Large Hadron Collider have found no significant evidence for BSM physics. Therefore, it is essential to broaden the sensitivity of the search program to include unexpected scenarios. We present a new model-agnostic anomaly detection technique that naturally benefits from modern machine learning algorithms. The only requirement on the signal for this new procedure is that it is localized in at least one known direction in phase space. Any other directions of phase space that are uncorrelated with the localized one can be used to search for unexpected features. This new method is applied to the dijet resonance search to show that it can turn a modest 2σ excess into a 7σ excess for a model with an intermediate BSM particle that is not currently targeted by a dedicated search.

Search for high-mass resonances decaying to emu in pp collisions at square root = 1.69 TeV

We describe a general search for resonances decaying to a neutral emu final state in pp collisions at a center-of-mass energy of 1.96 TeV. Using a data sample representing 344 pb(-1) of integrated luminosity recorded by the Collider Detector at Fermilab II experiment, we compare standard model predictions with the number of observed events for invariant masses between 50 and 800 GeV/c2. Finding no significant excess (5 events observed vs 7.7 +/- 0.8 expected for M(emu) > 100 GeV/c2 ), we set limits on sneutrino and Z' masses as functions of lepton family number violating couplings.

Search for new physics using QUAERO: a general interface to D0 event data

We describe QUAERO, a method that (i) enables the automatic optimization of searches for physics beyond the standard model, and (ii) provides a mechanism for making high energy collider data generally available. We apply QUAERO to searches for standard model WW, ZZ, and t t macro production, to searches for these objects produced through a new heavy resonance, and to the first direct search for W'-->WZ. Through this interface, we make three data sets collected by the D0 experiment at square root of [s] = 1.8 TeV publicly available.

Quasi-model-independent search for new high p(T) physics at D0

We apply a quasi-model-independent strategy ("Sleuth") to search for new high p(T) physics in approximately 100 pb(-1) of pp collisions at square root of (s) = 1.8 TeV collected by the D0 experiment during 1992-1996 at the Fermilab Tevatron. We systematically analyze many exclusive final states and demonstrate sensitivity to a variety of models predicting new phenomena at the electroweak scale. No evidence of new high p(T) physics is observed.