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.
Recent works @cite_27 @cite_30 @cite_18 @cite_24 have investigated the use of machine-learning techniques to setup new strategies for BSM searches with minimal or no assumption on the specific new-physics scenario under investigation. In this work, we use variational autoencoders based on high-level features as a baseline. Previously, autoencoders have been used in collider physics for detector monitoring @cite_2 @cite_29 and event generation @cite_3 . Autoencoders have also been explored to define a jet tagger that would identify new physics events with anomalous jets @cite_32 @cite_25 , with a strategy similar to what we apply to the full event in this work.
@cite_30 @cite_18 @cite_29 @cite_32 @cite_3 @cite_24 @cite_27 @cite_2 @cite_25
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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. We propose a new scientific application of unsupervised learning techniques to boost our ability to search for new phenomena in data, by detecting discrepancies between two datasets. These could be, for example, a simulated standard-model background, and an observed dataset containing a potential hidden signal of New Physics. We build a statistical test upon a test statistic which measures deviations between two samples, using a Nearest Neighbors approach to estimate the local ratio of the density of points. The test is model-independent and non-parametric, requiring no knowledge of the shape of the underlying distributions, and it does not bin the data, thus retaining full information from the multidimensional feature space. As a proof-of-concept, we apply our method to synthetic Gaussian data, and to a simulated dark matter signal at the Large Hadron Collider. Even in the case where the background can not be simulated accurately enough to claim discovery, the technique is a powerful tool to identify regions of interest for further study. Autoencoder networks, trained only on QCD jets, can be used to search for anomalies in jet-substructure. We show how, based either on images or on 4-vectors, they identify jets from decays of arbitrary heavy resonances. To control the backgrounds and the underlying systematics we can de-correlate the jet mass using an adversarial network. Such an adversarial autoencoder allows for a general and at the same time easily controllable search for new physics. Ideally, it can be trained and applied to data in the same phase space region, allowing us to efficiently search for new physics using un-supervised learning. Detectors of High Energy Physics experiments, such as the ATLAS dectector [1] at the Large Hadron Collider [2], serve as cameras that take pictures of the particles produced in the collision events. One of the key detector technologies used for measuring the energy of particles are calorimeters. Particles will lose their energy in a cascade (called a shower) of electromagnetic and hadronic interactions with a dense absorbing material. The number of the particles produced in this showering process is subsequently measured across the sampling layers of the calorimeter. The deposition of energy in the calorimeter due to a developing shower is a stochastic process that can not be described from first principles and rather relies on a precise simulation of the detector response. It requires the modeling of particles interactions with matter at the microscopic level as implemented using the Geant4 toolkit [3]. This simulation process is inherently slow and thus presents a bottleneck in the ATLAS simulation pipeline [4]. The current work addresses this limitation. To meet the growing analysis demands, ATLAS already relies strongly on fast calorimeter simulation techniques based on thousands of individual parametrizations of the calorimeter response [5]. The algorithms currently employed for physics analyses by the ATLAS collaboration achieve a significant speedup over the full simulation of the detector response at the cost of accuracy. Current developments [6] [7] aim at improving the modeling of taus, jet-substructure-based boosted objects or wrongly identified objects in the calorimeter and will benefit from an improved detector description following data taking and a more detailed forward calorimeter geometry. Deep Learning techniques have been improving state of the art results in various science areas such as: astrophysics [8], cosmology [9] and medical imaging [10]. These techniques are able to describe complex data structures and scale well with highdimensionality problems. Generative models are powerful deep learning algorithms to map complex distributions into a lower dimensional space, to generate samples of higher dimensionality and to approximate the underlying probability densities. Among the most promising approaches are Variational Auto-Encoders [11] [12] and Generative Adversarial Networks [13]. In this context, the talk presents the first application of such models to the fast simulation of the calorimeter response in the ATLAS detector. This work [14] demonstrates the feasibility of using such algorithms for large scale high energy physics experiments in the future, and opens the possibility to complement current techniques. Novelty detection is the machine learning task to recognize data, which belong to an unknown pattern. Complementary to supervised learning, it allows to analyze data model-independently. We demonstrate the potential role of novelty detection in collider physics, using autoencoder-based deep neural network. Explicitly, we develop a set of density-based novelty evaluators, which are sensitive to the clustering of unknown-pattern testing data or new-physics signal events, for the design of detection algorithms. We also explore the influence of the known-pattern data fluctuations, arising from non-signal regions, on detection sensitivity. Strategies to address it are proposed. The algorithms are applied to detecting fermionic di-top partner and resonant di-top productions at LHC, and exotic Higgs decays of two specific modes at a @math future collider. With parton-level analysis, we conclude that potentially the new-physics benchmarks can be recognized with high efficiency. We propose using neural networks to detect data departures from a given reference model, with no prior bias on the nature of the new physics responsible for the discrepancy. The virtues of neural networks as unbiased function approximants make them particularly suited for this task. An algorithm that implements this idea is constructed, as a straightforward application of the likelihood-ratio hypothesis test. The algorithm compares observations with an auxiliary set of reference-distributed events, possibly obtained with a Monte Carlo event generator. It returns a p-value, which measures the compatibility of the reference model with the data. It also identifies the most discrepant phase-space region of the data set, to be selected for further investigation. The most interesting potential applications are model-independent new physics searches, although our approach could also be used to compare the theoretical predictions of different Monte Carlo event generators, or for data validation algorithms. In this work we study the performance of our algorithm on a few simple examples. The results confirm the model-independence of the approach, namely that it displays good sensitivity to a variety of putative signals. Furthermore, we show that the reach does not depend much on whether a favorable signal region is selected based on prior expectations. We identify directions for improvement towards applications to real experimental data sets. Reliable data quality monitoring is a key asset in delivering collision data suitable for physics analysis in any modern large-scale high energy physics experiment. This paper focuses on the use of artificial neural networks for supervised and semi-supervised problems related to the identification of anomalies in the data collected by the CMS muon detectors. We use deep neural networks to analyze LHC collision data, represented as images organized geographically. We train a classifier capable of detecting the known anomalous behaviors with unprecedented efficiency and explore the usage of convolutional autoencoders to extend anomaly detection capabilities to unforeseen failure modes. A generalization of this strategy could pave the way to the automation of the data quality assessment process for present and future high energy physics experiments. We introduce a potentially powerful new method of searching for new physics at the LHC, using autoencoders and unsupervised deep learning. The key idea of the autoencoder is that it learns to map "normal" events back to themselves, but fails to reconstruct "anomalous" events that it has never encountered before. The reconstruction error can then be used as an anomaly threshold. We demonstrate the effectiveness of this idea using QCD jets as background and boosted top jets and RPV gluino jets as signal. We show that a deep autoencoder can significantly improve signal over background when trained on backgrounds only, or even directly on data which contains a small admixture of signal. Finally we examine the correlation of the autoencoders with jet mass and show how the jet mass distribution can be stable against cuts in reconstruction loss. This may be important for estimating QCD backgrounds from data. As a test case we show how one could plausibly discover 400 GeV RPV gluinos using an autoencoder combined with a bump hunt in jet mass. This opens up the exciting possibility of training directly on actual data to discover new physics with no prior expectations or theory prejudice.