The use of Boosted Decision Trees for Energy Reconstruction in JUNO experiment

The Jiangmen Underground Neutrino Observatory (JUNO) is a neutrino experiment with a broad physical program. The main goals of JUNO are the determination of the neutrino mass ordering and high precision investigation of neutrino oscillation properties. The precise reconstruction of the event energy is crucial for the success of the experiment. JUNO is equiped with 17 612 + 25 600 PMT channels of two kind which provide both charge and hit time information. In this work we present a fast Boosted Decision Trees model using small set of aggregated features. The model predicts event energy deposition. We describe the motivation and the details of our feature engineering and feature selection procedures. We demonstrate that the proposed aggregated approach can achieve a reconstruction quality that is competitive with the quality of much more complex models like Convolution Neural Networks (ResNet, VGG and GNN).

Highlights from GERDA: Probing the Majorana Neutrino Mass at 100 meV

Since 2010, the Gerda experiment at Laboratori Nazionali del Gran Sasso (LNGS) operates searching for neutrinoless double beta decay ( 0 ν β β ) of 76 Ge to the ground and excited states of 76 Se. 0 ν β β is an ultra-rare process whose detection would directly establish the Majorana nature of the neutrino and provide a direct measurement of its mass. Since the apparatus upgrade in 2013–2015, the collaboration released the third update of the achieved results at the Neutrino 2018 Conference. The hardware upgrade and the fine tuning of the powerful analysis tools to reconstruct the event energy and to discriminate the background allowed the achievement of the energy resolution of 3 keV and 3.6 keV for Broad Energy Germanium (BEGe) and Coaxial Germanium (Coax) detectors, respectively, and an unprecedented low background index of 0.6

Supergalactic Structure of Multiplets with the Telescope Array Surface Detector

Evidence of supergalactic structure of multiplets has been found for ultra-high energy cosmic rays (UHECR) with energies above 1019 eV using 7 years of data from the Telescope Array (TA) surface detector. The tested hypothesis is that UHECR sources, and intervening magnetic fields, may be correlated with the supergalactic plane, as it is a fit to the average matter density within the GZK horizon. This structure is measured by the average behavior of the strength of intermediate-scale correlations between event energy and position (multiplets). These multiplets are measured in wedge-like shapes on the spherical surface of the fieldof-view to account for uniform and random magnetic fields. The evident structure found is consistent with toy-model simulations of a supergalactic magnetic sheet and the previously published Hot/Coldspot results of TA. The post-trial probability of this feature appearing by chance, on an isotropic sky, is found by Monte Carlo simulation to be ~4.5σ.

Rainfall erosivity estimation based on rainfall data collected over a range of temporal resolutions

Rainfall erosivity is the power of rainfall to cause soil erosion by water. The rainfall erosivity index for a rainfall event (energy-intensity values – EI30) is calculated from the total kinetic energy and maximum 30 min intensity of individual events. However, these data are often unavailable in many areas of the world. The purpose of this study was to develop models based on commonly available rainfall data resolutions, such as daily or monthly totals, to calculate rainfall erosivity. Eleven stations with 1 min temporal resolution rainfall data collected from 1961 through 2000 in the eastern half of China were used to develop and calibrate 21 models. Seven independent stations, also with 1 min data, were utilized to validate those models, together with 20 previously published equations. The models in this study performed better or similar to models from previous research to estimate rainfall erosivity for these data. Using symmetric mean absolute percentage errors and Nash–Sutcliffe model efficiency coefficients, we can recommend 17 of the new models that had model efficiencies ≥ 0.59. The best prediction capabilities resulted from using the finest resolution rainfall data as inputs at a given erosivity timescale and by summing results from equations for finer erosivity timescales where possible. Results from this study provide a number of options for developing erosivity maps using coarse resolution rainfall data.

Fuzzy Analytic Hierarchy Process-based Chinese Resident Best Fitness Behavior Method Research

With explosive development in Chinese economy and science and technology, people’s pursuit of health becomes more and more intense, therefore Chinese resident sports fitness activities have been rapidly developed. However, different fitness events popularity degrees and effects on body energy consumption are different, so bases on this, the paper researches on fitness behaviors and gets Chinese residents sports fitness behaviors exercise guide, which provides guidance for propelling to national fitness plan’s implementation and improving Chinese resident fitness scientization. The paper starts from the perspective of energy consumption, it mainly adopts experience method, determines Chinese resident favorite sports fitness event energy consumption through observing all kinds of fitness behaviors energy consumption, and applies fuzzy analytic hierarchy process to make evaluation on bicycle riding, shadowboxing practicing, swimming, rope skipping, jogging, running, aerobics these seven fitness events. By calculating fuzzy rate model’s membership and comparing their sizes, it gets fitness behaviors that are more helpful for resident health, more effective and popular. Finally, it gets conclusions that swimming is a best exercise mode and its membership is the highest. Besides, the memberships of running, rope skipping and shadowboxing practicing are also relative higher. It should go in for bodybuilding by synthesizing above several kinds of fitness events according to different physical conditions; different living conditions so that can better achieve the purpose of fitness exercises.

Temporal energy partitions of Florida extreme sea level events as a function of Atlantic multidecadal oscillation

An energy-conservative metric based on the discrete wavelet transform is applied to assess the relative energy distribution of extreme sea level events across different temporal scales. The metric is applied to coastal events at Key West and Pensacola Florida as a function of two Atlantic Multidecadal Oscillation (AMO) regimes. Under AMO warm conditions there is a small but significant redistribution of event energy from nearly static into more dynamic (shorter duration) timescales at Key West, while at Pensacola the AMO-dependent changes in temporal event behaviour are less pronounced. Extreme events with increased temporal dynamics might be consistent with an increase in total energy of event forcings which may be a reflection of more energetic storm events during AMO warm phases. As dynamical models mature to the point of providing regional climate index predictability, coastal planners may be able to consider such temporal change metrics in planning scenarios.