The technique for constructing conformally invariant theories within the coset space construction is developed. It reproduces all consequences of the conformal invariance and Lagrangians of widely ...known conformal field theories. The method of induced representations, which plays the key role in the construction, allows us to reveal a special role of the Nambu-Goldstone fields for special conformal transformations. Namely, their dependence on the coordinates turns out to be fixed by the symmetries. This results in the appearance of the constraints on possible forms of Lagrangians, which ensure that discrete symmetries are indeed symmetries of the theory.
We establish a classical analog of the Nambu–Goldstone theorem for spontaneous breaking of spacetime symmetries. It provides a counting rule for independent Nambu–Goldstone fields and states which of ...them are gapped. We demonstrate that only those symmetry group generators give rise to independent Nambu–Goldstone fields that act nontrivially on a vacuum at the origin of coordinates. Other generators give rise to auxiliary fields that must be excluded from a theory by the means of inverse Higgs constraints. The physical meaning of the inverse Higgs phenomenon and an application of our results to theories of massive gravity are discussed.
We introduce a novel model of affine gravity, which implements the no-scale scenario. Namely, the Planck mass and Hubble constant emerge dynamically in our model, through the mechanism of spontaneous ...breaking of scale invariance. This naturally gives rise to inflation, thus introducing a new inflationary mechanism. Moreover, the time direction and nondegenerate metric emerge dynamically as well, which allows considering the usual General Relativity as an effective theory. We show that our model is phenomenologically viable, both from the perspective of the direct tests of gravity and from the standpoint of cosmological evolution.
Abstract
We introduce a novel method for identifying the mass composition of ultra-high-energy cosmic rays using deep learning. The key idea of the method is to use a chain of two neural networks. ...The first network predicts the type of a primary particle for individual events, while the second infers the mass composition of an ensemble of events. We apply this method to the Monte-Carlo data for the Telescope Array Surface Detectors readings, on which it yields an unprecedented low error of 7% for 4-component approximation. We also discuss the problems of applying the developed method to the experimental data, and the way they can be resolved.
We establish a mathematically rigorous way to construct effective theories resulting from the spontaneous breaking of conformal invariance. We show that the Namby-Goldstone field corresponding to ...spontaneously broken generators of special conformal transformations is always a nondynamical degree of freedom. We prove that the developed approach and the standard approach including application of the inverse Higgs mechanism are equivalent.
Abstract
Baikal-GVD is a large (∼ 1 km
3
) underwater neutrino
telescope installed in the fresh waters of Lake Baikal. The deep
lake water environment is pervaded by background light, which is
...detectable by Baikal-GVD's photosensors. We introduce a neural
network for an efficient separation of these noise hits from the
signal ones, stemmng from the propagation of relativistic particles
through the detector. The model has a U-Net-like architecture and
employs temporal (causal) structure of events. The neural network's
metrics reach up to 99% signal purity (precision) and 96% survival
efficiency (recall) on Monte-Carlo simulated dataset. We compare the
developed method with the algorithmic approach to rejecting the
noise and discuss other possible architectures of neural networks,
including graph-based ones.
Abstract
Baikal-GVD is a gigaton-scale underwater neutrino telescope currently under construction in Lake Baikal. Its principal components are optical modules, registering photons propagating through ...the telescope’s working volume. Part of the activations of the optical modules are due to the natural luminescence of the water, and thus appear as noise in the data. We present a neural network, which efficiently rejects this background and reaches 97% signal purity (precision) and 99% survival efficiency (recall) on the Monte-Carlo data. The neural network has a U-net like architecture based on the temporal structure of optical modules activations.
The Baikal-GVD experiment is a neutrino telescope located in Lake Baikal, Russia. As of 2022, it has an effective volume of 0.5 km
3
, which makes it the largest neutrino telescope in the Northern ...Hemisphere and the second largest in the world. This article presents an overview of machine learning methods developed to analyze data from the Baikal-GVD experiment. Specifically, we discuss neural networks developed to (1) suppress noise responses of optical modules, (2) identify neutrino-induced events and estimate their flux, and (3) recover the neutrino arrival angle. It is shown that neural networks are comparable or superior in accuracy to standard algorithmic event reconstruction procedures for similar problems.
We report on the development of neural networks for generating readings from Telescope Array’s surface detectors with the largest registered integral signal. To achieve this goal, we implemented ...generative Wasserstein adversarial networks with the gradient penalty. The data used to train the model was generated using the Monte Carlo method. We obtained visually similar data which are consistent with the physics of the underlying processes. The anomaly search method can be employed to identify discrepancies between real and simulated data, as well as to introduce a quantitative measure of similarity between the real detector readings and those generated by the neural network’s readings.
Baikal-GVD is a large (
1 km
) underwater neutrino telescope located in Lake Baikal, Russia. In this report, we present two machine learning techniques developed for its data analysis. First, we ...introduce a neural network for an efficient rejection of noise hits, emerging due to natural water luminescence. Second, we develop a neural network for distinguishing muon- and neutrino-induced events. By choosing an appropriate classification threshold, we preserve
of neutrino-induced events, while muon-induced events are suppressed by a factor of
. Both of the developed neural networks employ the causal structure of events and surpass the precision of standard algorithmic approaches.
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