A model for the interaction of high-energy particles in straight and bent crystals implemented in Geant4

A model for the simulation of orientational effects in straight and bent periodic atomic structures is presented. The continuum potential approximation has been adopted.The model allows the manipulation of particle trajectories by means of straight and bent crystals and the scaling of the cross sections of hadronic and electromagnetic processes for channeled particles. Based on such a model, an extension of the Geant4 toolkit has been developed. The code has been validated against data from channeling experiments carried out at CERN

measurement of the enhancement of the nuclear interaction yield with crystalline targets at cyclotron energies

Ordered structures such as crystalline materials may help to enhance the nuclear interaction yield. Indeed, the aligned atoms act as a single entity on impinging charged particles, causing the trajectory to pass from its random motion to a deterministic one. In fact, Monte Carlo simulations suggested that specific crystal alignments allow for the enhancement of the production rate of nuclear inelastic reactions, because particles are forced to pass by the atomic nuclei more frequently than would happen in an amorphous material. Recent measurements we carried out at the AN2000 accelerator of the National Institute for Nuclear Physics Legnaro National Laboratories showed the experimental evidence of such an effect. A 643.5 keV collimated proton beam was used to induce the [Formula: see text]O(p,[Formula: see text]N reaction in an Al[Formula: see text]O[Formula: see text] substrate oriented along the axis. The capability of manipulating such an effect paves the way to studying innovative targets for the enhancement of the nuclear interaction yield with a constant density

Electromagnetic dipole moments of charged baryons with bent crystals at the LHC

We propose a unique program of measurements of electric and magnetic dipole moments of charm, beauty and strange charged baryons at the LHC, based on the phenomenon of spin precession of channeled particles in bent crystals. Studies of crystal channeling and spin precession of positively- and negatively-charged particles are presented, along with feasibility studies and expected sensitivities for the proposed experiment using a layout based on the LHCb detector.Comment: 19 pages, 13 figure

Experimental evidence of planar channeling in a periodically bent crystal

The usage of a Crystalline Undulator (CU) has been identified as a promising solution for generating powerful and monochromatic $\gamma$-rays. A CU was fabricated at SSL through the grooving method, i.e., by the manufacturing of a series of periodical grooves on the major surfaces of a crystal. The CU was extensively characterized both morphologically via optical interferometry at SSL and structurally via X-ray diffraction at ESRF. Then, it was finally tested for channeling with a 400 GeV/c proton beam at CERN. The experimental results were compared to Monte Carlo simulations. Evidence of planar channeling in the CU was firmly observed. Finally, the emission spectrum of the positron beam interacting with the CU was simulated for possible usage in currently existing facilities

The economic value of a climate service for water irrigation. A case study for Castiglione District, Emilia-Romagna, Italy

The use of climate services to support decision makers in incorporating climate change adaptation in their practices is well established and widely recognized. Their role is particularly relevant in a climate sensitive sector like agriculture where they can provide evidence for the adoption of transformative solutions from seasonal to multi-decadal time scales. Adaptation solutions are often expensive and irreversible in the short/medium run. Accordingly, end users should have a reliable reference to make decisions. Here, we propose and apply a methodology, co-developed with service developers and a representative potential user, to assess the value of the IRRICLIME climate service, whose information is used to support decisions on climate smart irrigation investment by water planners in a sub-irrigation district in Italy. We quantify the value of the information provided by the climate service, that we consider the intrinsic value of the service, or the value of adaptation. We demonstrate that under three different climate change scenarios, the maximum potential value of IRRICLIME could range between 2,985 €/ha and 7,480 €/ha

Steering efficiency of a ultrarelativistic proton beam in a thin bent crystal

Crystals with small thickness along the beam exhibit top performance for steering particle beams through planar channeling. For such crystals, the effect of nuclear dechanneling plays an important role because it affects their efficiency. We addressed the problem through experimental work carried out with 400 GeV/c protons at fixed-target facilities of CERN-SPS. The dependence of efficiency vs. curvature radius has been investigated and compared favourably to the results of modeling. A realistic estimate of the performance of a crystal designed for LHC energy including nuclear dechanneling has been achieved

Smart Climate Hydropower Tool: A Machine-Learning Seasonal Forecasting Climate Service to Support Cost–Benefit Analysis of Reservoir Management

This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. SCHT is technically designed to make use of information from state-of-art seasonal forecasts provided by the Copernicus Climate Data Store (CDS) combined with a range of different machine learning algorithms to perform the seasonal forecast of the accumulated inflow discharges to the reservoir of hydropower plants. The machine learning algorithms considered include support vector regression, Gaussian processes, long short-term memory, non-linear autoregressive neural networks with exogenous inputs, and a deep-learning neural networks model. Each machine learning model is trained over past decades datasets of recorded data, and forecast performances are validated and evaluated using separate test sets with reference to the historical average of discharge values and simpler multiparametric regressions. Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. Methods are tested for forecasting the accumulated seasonal river discharges up to six months in advance for two catchments in Colombia, South America. Results indicate that the machine learning algorithms that make use of a complex and/or recurrent architecture can better simulate the temporal dynamic behaviour of the accumulated river discharge inflow to both case study reservoirs, thus rendering SCHT a useful tool in providing information for water resource managers in better planning the allocation of water resources for different users and for hydropower plant managers when negotiating power purchase contracts in competitive energy markets

Smart climate hydropower tool: A machine-learning seasonal forecasting climate service to support cost–benefit analysis of reservoir management

This study proposes a climate service named Smart Climate Hydropower Tool (SCHT) and designed as a hybrid forecast system for supporting decision-making in a context of hydropower production. SCHT is technically designed to make use of information from state-of-art seasonal forecasts provided by the Copernicus Climate Data Store (CDS) combined with a range of different machine learning algorithms to perform the seasonal forecast of the accumulated inflow discharges to the reservoir of hydropower plants. The machine learning algorithms considered include support vector regression, Gaussian processes, long short-term memory, non-linear autoregressive neural networks with exogenous inputs, and a deep-learning neural networks model. Each machine learning model is trained over past decades datasets of recorded data, and forecast performances are validated and evaluated using separate test sets with reference to the historical average of discharge values and simpler multiparametric regressions. Final results are presented to the users through a user-friendly web interface developed from a tied connection with end-users in an effective co-design process. Methods are tested for forecasting the accumulated seasonal river discharges up to six months in advance for two catchments in Colombia, South America. Results indicate that the machine learning algorithms that make use of a complex and/or recurrent architecture can better simulate the temporal dynamic behaviour of the accumulated river discharge inflow to both case study reservoirs, thus rendering SCHT a useful tool in providing information for water resource managers in better planning the allocation of water resources for different users and for hydropower plant managers when negotiating power purchase contracts in competitive energy markets