physicscosmic radiationdistributionerrorsmagnetic fieldsmomentummuonsquantitative analysisspectrometersThe systematic and random errors that are likely to occur in momentum spectrum measurements have been inves
of the target. For a 28 MeV (p,p′) reaction on36S for instance (Fig. 3), it can be observed that describing the target nucleus within the quasiparticle random phase approximation (QRPA) framework [13,14] enables to reproduce available experimental data for inelastic scattering off the 2...
The three types of experimental error are systematic, random, and blunders. Systematic errors are errors of precision as all measurements will be off due to things such as miscalibration or background interference. Random errors occur due to happenstance, such as fluctuations in temperature or pH....
Here, we review the development of low-coherence semiconductor light sources, including superluminescent diodes, highly multimode lasers, and random lasers, and the wide range of applications in which they have been deployed. We highlight how each of these applications benefsits from a lower degree...
Random numbers are necessary for many different applications, ranging from simulations to cryptography and fundamental physics tests, such as Bell tests1,2,3. Despite its common use, the certification of randomness is a complex task. Classical processes cannot generate genuine randomness because of the...
In subject area: Physics and Astronomy Gamma-ray sources refer to regions in space emitting high-energy gamma radiation, often discovered through satellite missions. Some of these sources remain unidentified and can be categorized based on their location, spectral properties, and association with known...
Learning process involving three ML algorithms (Extra Random Trees (XT), Gradient Boosting (GB), Extreme Gradient Boosting (XGB)) is conducted using these band centers. Cross-validation results indicated the superior accuracy of XT-model where its performance, is validated through density functional ...
The growing integration of renewable energy sources into grid-connected microgrids has created new challenges in power generation forecasting and energy management. This paper explores the use of advanced machine learning algorithms, specifically Support
Variance and predictors of phenological events To determine whether the distribution of available data for the main processes of ecosystem fluxes and wood formation affected our conclusions, we used random forest regression models to assess the relative importance of study year, site, species and biome...
(qS,debandqB,deb, respectively).c, SFDs from 3,000 random draws of 150 asteroids from aq = −1.45 population of about 1 × 106asteroids showing consistent slopes until about 130 m, owing to small-number statistics (N ≤ 7), the ‘sampling-bias cutoff’.d, Relationship...