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.
The dynamics of meteorological parameters in Siberia and the Altai–Sayan region in the 20ᵗʰ–21ˢᵗ centuries are analyzed. Significant trends characterizing the dynamics of the average temperature, ...precipitation, and standardized precipitation evapotranspiration index (SPEI) are revealed. Growing wildfire frequency in the area under study since the end of the 20ᵗʰ century has been detected. The annual variation of wildfires has a phase coincidence with the dynamics of mean temperatures, positively correlates with climate dryness, and negatively correlates with averaged precipitation data. An abrupt increase in wildfire frequency has been observed in the late 20ᵗʰ–early 21ˢᵗ centuries. The spatial redistribution of wildfires in the Altai–Sayan region in the early 21ˢᵗ century is revealed.
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.
► The drought-caused birch mortality in east Siberia was discovered based on a satellite, on-ground and tree ring data. ► Tree mortality showed correlation with May–August/annual precipitation and ...temperatures. ► Period of extreme temperature/precipitation anomalies was about 27years; observed anomaly was severest since the 1900years. ► Data for some other Siberian sites indicated a positive climate impact on the tree growth and expansion.
The Trans-Baikal (or Zabailkal’e) region includes the forest-steppe ecotones south and east of Lake Baikal in Russia and has experienced drought for several years. The decline and mortality of birch (Betula pendula) stands within the forest-steppe ecotone Trans-Baikal region was studied based on a temporal series of satellite data, ground measurements, and tree ring analysis. During the first decade of the 21st century birch stands decline and mortality were observed on about 5% of the total area of stands within our 1250km2 study area. Birch forest decline and mortality occurs mainly at the margins of stands, within the forest-steppe ecotone on slopes with direct insolation. During the first decade of the 21st century summer (June–August) precipitation was about 25% below normal. Soil water content measurements were lowest within dead stands and highest within healthy stands and intermediate within damaged stands. Drought impact on stands was amplified by an increase in summer air temperatures (+0.9°C) in comparison with the previous decade. Tree ring data of “surviving” and “dead” tree groups showed a positive correlation with summer/annual precipitation and negative correlation with summer air temperatures. Temperature and precipitation extreme anomalies tend to occur in the region with a period of about 27years. The observed anomaly was the most severe since the beginning of meteorological observations in the year 1900. Data for the other sites showed a positive climate impact on the growth and expansion of Siberian forests. That is, the same species (B. pendula) showed considerable increase (1.4 times both in height and stem volume) during 20th–21st centuries as temperature increased but precipitation remained at adequate levels.
Wildfire frequency, relative area burned, and fire return intervals (FRI) have been studied in larchdominated forests along the transect from the southern (45° N) to the northern (73° N) distribution ...limits of larch stands based on analysis of satellite imagery (NOAA/AVHRR, Terra/MODIS; 1996–2015) and collection of tree cross cuts with fire scars. A significant increasing trend in fire extent (
R
2
= 0.50,
p
< 0.05) has been revealed. Histograms of fire extent and frequency are bimodal in the southern and middle taiga (with peaks in spring–summer and late summer–autumn periods) but become unimodal toward the north (>55° N). The length of FRI increases from 80 years at 62° N to ~200 years at the Arctic Circle and reaches ~300 years near the northern limit of larch stands, showing a significant inverse correlation with the length of fire season (
r
=–0.69). In turn, the length of fire season, area burned and FRI are closely correlated with latitudinal variation in solar irradiance (
r
= 0.97, 0.81, and –0.95, respectively).
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.
The forest biomass dynamics in boreal forests has a significant effect on global carbon cycles. Biomass estimates provide insight into the carbon balance of forest vegetation in Siberia. This paper ...discusses the methods used in modern studies (2010–2021) to estimate aboveground forest biomass on the basis of radar remote sensing data. Biomass estimation methodologies are described, including field data collection, data preprocessing, and modeling of relationships between remote sensing (RS) data and biomass. In terms of forest biomass estimation, radar sensing has limited capabilities determined by the characteristics of the survey equipment and parameters of studied forest stands. Modern studies combine optical and radar RS data to estimate forest biomass more accurately using regression models, machine learning, and special techniques (BIOMASAR, SWCM, and MaxEnt). Vegetation optical depth values estimated on the basis of microwave surveys make it possible to solve the saturation problem hindering the estimation of large amounts of biomass. It is difficult to compare the accuracy of biomass estimation methods due to the lack of uniform approaches to experimental and error computation procedures. Errors in biomass estimates produced on the basis of optical and radar data vary considerably (~25% on average). The small amount of reference field data complicates biomass estimations in boreal forests of Siberia. It is believed that the application of machine learning algorithms to remote sensing data collected by the Sentinel-1 and ALOS-PALSAR satellites will make it possible to estimate the biomass of boreal forests with a high spatial resolution.
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.
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.
We study the renormalization group flow in strongly interacting field theories in the fermion sector corresponding to the transition from theories without a Lorentz invariance at high energies to ...theories with an approximate Lorentz invariance in the infrared limit. We use the holographic description of the strong coupling. We give special attention to the emergence of chiral fermions in the low-energy limit.
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