Using variational autoencoders trained on known physics processes, we develop a one-sided threshold test to isolate previously unseen processes as outlier events. Since the autoencoder training does not depend on any specific new physics signature, the proposed procedure doesn't make specific assumptions on the nature of new physics. An event selection based on this algorithm would be complementary to classic LHC searches, typically based on model-dependent hypothesis testing. Such an algorithm would deliver a list of anomalous events, that the experimental collaborations could further scrutinize and even release as a catalog, similarly to what is typically done in other scientific domains. Event topologies repeating in this dataset could inspire new-physics model building and new experimental searches. Running in the trigger system of the LHC experiments, such an application could identify anomalous events that would be otherwise lost, extending the scientific reach of the LHC.
Model-independent searches for new physics have been performed at the Tevatron @cite_16 @cite_22 , at HERA @cite_12 , and the LHC @cite_31 @cite_15 . These searches are based on the comparison of a large set of binned distributions to the prediction from Monte Carlo simulation, in search for bins exhibiting a deviation larger than some predefined threshold. While the effectiveness of this strategy in establishing a discovery has been matter of discussion, a recent study by the ATLAS collaboration @cite_15 has rephrased this model-independent search strategy into a tool to identify interesting excesses, on which traditional analysis techniques could be performed on independent datasets (e.g., the data collected after running the model-independent analysis). This change of scope has the advantage of reducing the trial factor (i.e., the so-called look-elsewhere effect @cite_17 @cite_11 ), which washes out the significance of an observed excess.
@cite_11 @cite_22 @cite_15 @cite_31 @cite_16 @cite_12 @cite_17
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Data collected in run II of the Fermilab Tevatron are searched for indications of new electroweak-scale physics. Rather than focusing on particular new physics scenarios, CDF data are analyzed for discrepancies with the standard model prediction. A model-independent approach (VISTA) considers gross features of the data, and is sensitive to new large cross-section physics. Further sensitivity to new physics is provided by two additional algorithms: a Bump Hunter searches invariant mass distributions for "bumps'' that could indicate resonant production of new particles, and the SLEUTH procedure scans for data excesses at large summed transverse momentum. This combined global search for new physics in 2.0 fb(-1) of p (p) over bar collisions at root s = 1.96 TeV reveals no indication of physics beyond the standard model. This paper describes a strategy for a general search used by the ATLAS Collaboration to find potential indications of new physics. Events are classified according to their final state into many event classes. For each event class an automated search algorithm tests whether the data are compatible with the Monte Carlo simulated expectation in several distributions sensitive to the effects of new physics. The significance of a deviation is quantified using pseudo-experiments. A data selection with a significant deviation defines a signal region for a dedicated follow-up analysis with an improved background expectation. The analysis of the data-derived signal regions on a new dataset allows a statistical interpretation without the large look-elsewhere effect. The sensitivity of the approach is discussed using Standard Model processes and benchmark signals of new physics. As an example, results are shown for 3.2 fb −1 of proton–proton collision data at a centre-of-mass energy of 13 TeV collected with the ATLAS detector at the LHC in 2015, in which more than 700 event classes and more than 105 regions have been analysed. No significant deviations are found and consequently no data-derived signal regions for a follow-up analysis have been defined. The oldest and most robust technique to search for new particles is to look for bumps' in invariant mass spectra over smoothly falling backgrounds. This is a powerful technique, but only uses one-dimensional information. One can restrict the phase space to enhance a potential signal, but such tagging techniques require a signal hypothesis and training a classifier in simulation and applying it on data. We present a new method for using all of the available information (with machine learning) without using any prior knowledge about potential signals. Given the lack of new physics signals at the Large Hadron Collider (LHC), such model independent approaches are critical for ensuring full coverage to fully exploit the rich datasets from the LHC experiments. In addition to illustrating how the new method works in simple test cases, we demonstrate the power of the extended bump hunt on a realistic all-hadronic resonance search in a channel that would not be covered with existing techniques. A model--independent search for deviations from the Standard Model prediction is performed using the full @math data sample collected by the H1 experiment at HERA. All event topologies involving isolated electrons, photons, muons, neutrinos and jets with transverse momenta above 20 GeV are investigated in a single analysis. Events are assigned to exclusive classes according to their final state. A dedicated algorithm is used to search for deviations from the Standard Model in the distributions of the scalar sum of transverse momenta or the invariant mass of final state particles and to quantify their significance. Variables related to angular distributions and energy sharing between final state particles are also introduced to study the final state topologies. No significant deviation from the Standard Model expectation is observed in the phase space covered by this analysis.