Sampling using SU ( N ) gauge equivariant flows

Discoveries of new phenomena often involve a dedicated search for a hypothetical physics signature. Recently, novel deep learning techniques have emerged for anomaly detection in the absence of a signal prior. However, by ignoring signal priors, the sensitivity of these approaches is significantly reduced. We present a new strategy dubbed Quasi Anomalous Knowledge (QUAK), whereby we introduce alternative signal priors that capture some of the salient features of new physics signatures, allowing for the recovery of sensitivity even when the alternative signal is incorrect. This approach can be applied to a broad range of physics models and neural network architectures. In this paper, we apply QUAK to anomaly detection of new physics events at the CERN Large Hadron Collider utilizing variational autoencoders with normalizing flow.

Section: Normalizing Flowsmentioning

confidence: 99%

Quasi anomalous knowledge: searching for new physics with embedded knowledge

Park

Rankin

Udrescu

et al. 2021

“…3 In fact, it is somewhat surprising that autoencoders appear to perform worse on the nominally easier task of a bump hunt in leptons than on the superficially much more complicated task of jet image recognition and classification, since leptons live on a phase space of fixed dimension. The increasing prominence of "physics-inspired neural networks" -where networks with important symmetry principles (such as gauge equivariance and Lorentz symmetry) hard-coded into the network architecture perform better than networks which are forced to learn these principles from scratch [54][55][56] -suggests that knowledge of the topology may in fact be necessary to appropriately interpret the autoencoder performance. We illustrate this point with the low-dimensional examples described above, and speculate on how these principles might be applied in the context of phase space.…”

Section: Jhep04(2021)280mentioning

confidence: 99%

Topological obstructions to autoencoding

Batson¹,

Haaf

Kahn

et al. 2021

Autoencoders have been proposed as a powerful tool for model-independent anomaly detection in high-energy physics. The operating principle is that events which do not belong to the space of training data will be reconstructed poorly, thus flagging them as anomalies. We point out that in a variety of examples of interest, the connection between large reconstruction error and anomalies is not so clear. In particular, for data sets with nontrivial topology, there will always be points that erroneously seem anomalous due to global issues. Conversely, neural networks typically have an inductive bias or prior to locally interpolate such that undersampled or rare events may be reconstructed with small error, despite actually being the desired anomalies. Taken together, these facts are in tension with the simple picture of the autoencoder as an anomaly detector. Using a series of illustrative low-dimensional examples, we show explicitly how the intrinsic and extrinsic topology of the dataset affects the behavior of an autoencoder and how this topology is manifested in the latent space representation during training. We ground this analysis in the discussion of a mock “bump hunt” in which the autoencoder fails to identify an anomalous “signal” for reasons tied to the intrinsic topology of n-particle phase space.

“…It employs multiple layered Artificial Neural Networks to learn higher dimensional correlations in the data. Machine learning and Deep Learning methods have been widely used both in theory [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] and in experimental high energy physics [49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68]. Previous studies [18,20] on identifying the QCD phase transitions have shown that Convolutional Neural Network (CNN) based models can accurately classify the underlying equation of state from a hydrodynamic evolution using the p tφ spectra of pions (differential transverse and angular distributions in the transverse plane).…”

Section: Pointnet For Classifying the Eosmentioning

confidence: 99%

An equation-of-state-meter for CBM using PointNet

Kuttan

Zhou

Steinheimer

et al. 2021

A novel method for identifying the nature of QCD transitions in heavy-ion collision experiments is introduced. PointNet based Deep Learning (DL) models are developed to classify the equation of state (EoS) that drives the hydrodynamic evolution of the system created in Au-Au collisions at 10 AGeV. The DL models were trained and evaluated in different hypothetical experimental situations. A decreased performance is observed when more realistic experimental effects (acceptance cuts and decreased resolutions) are taken into account. It is shown that the performance can be improved by combining multiple events to make predictions. The PointNet based models trained on the reconstructed tracks of charged particles from the CBM detector simulation discriminate a crossover transition from a first order phase transition with an accuracy of up to 99.8%. The models were subjected to several tests to evaluate the dependence of its performance on the centrality of the collisions and physical parameters of fluid dynamic simulations. The models are shown to work in a broad range of centralities (b=0–7 fm). However, the performance is found to improve for central collisions (b=0–3 fm). There is a drop in the performance when the model parameters lead to reduced duration of the fluid dynamic evolution or when less fraction of the medium undergoes the transition. These effects are due to the limitations of the underlying physics and the DL models are shown to be superior in its discrimination performance in comparison to conventional mean observables.