Learning New Physics from a Machine
TL;DR: In this paper, the authors use 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, using a likelihood-ratio hypothesis test.
Abstract: 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.
Citations
More filters
TL;DR: A comprehensive review of state-of-the-art theoretical and machine learning developments in jet substructure is provided in this article, which is meant both as a pedagogical introduction and as a comprehensive reference for experts.
340 citations
TL;DR: In this article, a neural network architecture based on the human physical reasoning process is proposed for scientific discovery from experimental data without making prior assumptions about the system, which can help to gain conceptual insights.
Abstract: Despite the success of neural networks at solving concrete physics problems, their use as a general-purpose tool for scientific discovery is still in its infancy. Here, we approach this problem by modeling a neural network architecture after the human physical reasoning process, which has similarities to representation learning. This allows us to make progress towards the long-term goal of machine-assisted scientific discovery from experimental data without making prior assumptions about the system. We apply this method to toy examples and show that the network finds the physically relevant parameters, exploits conservation laws to make predictions, and can help to gain conceptual insights, e.g., Copernicus' conclusion that the solar system is heliocentric.
328 citations
TL;DR: It is shown how ANODE can enhance the significance of a dijet bump hunt by up to a factor of 7 with a 10\% accuracy on the background prediction, and is robust against systematic differences between signal region and sidebands, giving it broader applicability than other methods.
Abstract: Author(s): Nachman, B; Shih, D | Abstract: We leverage recent breakthroughs in neural density estimation to propose a new unsupervised ANOmaly detection with Density Estimation (ANODE) technique. By estimating the conditional probability density of the data in a signal region and in sidebands, and interpolating the latter into the signal region, a fully data-driven likelihood ratio of data versus background can be constructed. This likelihood ratio is broadly sensitive to overdensities in the data that could be due to localized anomalies. In addition, a unique potential benefit of the ANODE method is that the background can be directly estimated using the learned densities. Finally, ANODE is robust against systematic differences between signal region and sidebands, giving it broader applicability than other methods. We demonstrate the power of this new approach using the LHC Olympics 2020 RaD dataset. We show how ANODE can enhance the significance of a dijet bump hunt by up to a factor of 7 with a 10% accuracy on the background prediction. While the LHC is used as the recurring example, the methods developed here have a much broader applicability to anomaly detection in physics and beyond.
207 citations
TL;DR: A potentially powerful new method of searching for new physics at the LHC, using autoencoders and unsupervised deep learning, which opens up the exciting possibility of training directly on actual data to discover new physics with no prior expectations or theory prejudice.
Abstract: 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 R-parity violating (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 contain 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
207 citations
[...]
TL;DR: This work shows how adversarial autoencoder networks, trained only on QCD jets, identify jets from decays of arbitrary heavy resonances using 4-vectors, allowing for a general and at the same time easily controllable search for new physics.
Abstract: Autoencoder networks, trained only on QCD jets, can be used to search foranomalies in jet-substructure. We show how, based either on images or on4-vectors, they identify jets from decays of arbitrary heavy resonances. Tocontrol the backgrounds and the underlying systematics we can de-correlate thejet mass using an adversarial network. Such an adversarial autoencoder allowsfor 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.
176 citations
References
More filters
TL;DR: This book covers a broad range of topics for regular factorial designs and presents all of the material in very mathematical fashion and will surely become an invaluable resource for researchers and graduate students doing research in the design of factorial experiments.
Abstract: (2007). Pattern Recognition and Machine Learning. Technometrics: Vol. 49, No. 3, pp. 366-366.
18,802 citations
TL;DR: It is rigorously established that standard multilayer feedforward networks with as few as one hidden layer using arbitrary squashing functions are capable of approximating any Borel measurable function from one finite dimensional space to another to any desired degree of accuracy, provided sufficiently many hidden units are available.
18,794 citations
TL;DR: This biennial Review summarizes much of particle physics, using data from previous editions.
12,798 citations
TL;DR: It is demonstrated that finite linear combinations of compositions of a fixed, univariate function and a set of affine functionals can uniformly approximate any continuous function ofn real variables with support in the unit hypercube.
Abstract: In this paper we demonstrate that finite linear combinations of compositions of a fixed, univariate function and a set of affine functionals can uniformly approximate any continuous function ofn real variables with support in the unit hypercube; only mild conditions are imposed on the univariate function. Our results settle an open question about representability in the class of single hidden layer neural networks. In particular, we show that arbitrary decision regions can be arbitrarily well approximated by continuous feedforward neural networks with only a single internal, hidden layer and any continuous sigmoidal nonlinearity. The paper discusses approximation properties of other possible types of nonlinearities that might be implemented by artificial neural networks.
12,286 citations
TL;DR: The review as discussed by the authors summarizes much of particle physics and cosmology using data from previous editions, plus 3,283 new measurements from 899 Japers, including the recently discovered Higgs boson, leptons, quarks, mesons and baryons.
Abstract: The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 3,283 new measurements from 899 Japers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as heavy neutrinos, supersymmetric and technicolor particles, axions, dark photons, etc. All the particle properties and search limits are listed in Summary Tables. We also give numerous tables, figures, formulae, and reviews of topics such as Supersymmetry, Extra Dimensions, Particle Detectors, Probability, and Statistics. Among the 112 reviews are many that are new or heavily revised including those on: Dark Energy, Higgs Boson Physics, Electroweak Model, Neutrino Cross Section Measurements, Monte Carlo Neutrino Generators, Top Quark, Dark Matter, Dynamical Electroweak Symmetry Breaking, Accelerator Physics of Colliders, High-Energy Collider Parameters, Big Bang Nucleosynthesis, Astrophysical Constants and Cosmological Parameters.
7,337 citations