Dark Matter directional detection: comparison of the track direction determination

Several directional techniques have been proposed for a directional detection of Dark matter, among others anisotropic crystal detectors, nuclear emulsion plates, and low-pressure gaseous TPCs. The key point is to get access to the initial direction of the nucleus recoiling due to the elastic scattering by a WIMP. In this article, we aim at estimating, for each method, how the information of the recoil track initial direction is preserved in different detector materials. We use the SRIM simulation code to emulate the motion of the first recoiling nucleus in each material. We propose the use of a new observable, D, to quantify the preservation of the initial direction of the recoiling nucleus in the detector. We show that in an emulsion mix and an anisotropic crystal, the initial direction is lost very early, while in a typical TPC gas mix, the direction is well preserved.Comment: 9 pages, 5 figure

A detection algorithm for the first jump time in sample trajectories of jump-diffusions driven by α-stable white noise

The purpose of this paper is to develop a detection algorithm for the first jump point in sampling trajectories of jump-diffusions which are described as solutions of stochastic differential equations driven by $\alpha$-stable white noise. This is done by a multivariate Lagrange interpolation approach. To this end, we utilise computer simulation algorithm in MATLAB to visualise the sampling trajectories of the jump-diffusions for various combinations of parameters arising in the modelling structure of stochastic differential equations

Decision process in large-scale crisis management

International audienceThis paper deals with the decision-aiding process in large-scale crisis such as natural disasters. It consists in four phases: decision context characterization, system modelling, aggregation and integration. The elements of the context, such as crisis level, risk situation, decision-maker problem issue are defined through the characterization phase. At the feared event occurrence, these elements will interact on a target system. Through the model on this system, the consequences to stakes could be assessed or estimated. The presented aggregation approaches will allow taking the right decisions. The architecture of a Decision Support System is presented in the integration phase

Nuclear Recoil Identification in a Scientific Charge-Coupled Device

Charge-coupled devices (CCDs) are a leading technology in direct dark matter searches because of their eV-scale energy threshold and high spatial resolution. The sensitivity of future CCD experiments could be enhanced by distinguishing nuclear recoil signals from electronic recoil backgrounds in the CCD silicon target. We present a technique for event-by-event identification of nuclear recoils based on the spatial correlation between the primary ionization event and the lattice defect left behind by the recoiling atom, later identified as a localized excess of leakage current under thermal stimulation. By irradiating a CCD with an $^{241}$Am$^{9}$Be neutron source, we demonstrate $>93\%$ identification efficiency for nuclear recoils with energies $>150$ keV, where the ionization events were confirmed to be nuclear recoils from topology. The technique remains fully efficient down to 90 keV, decreasing to 50$\%$ at 8 keV, and reaching ($6\pm2$)$\%$ at 1.5--3.5 keV. Irradiation with a $^{24}$Na $\gamma$-ray source shows no evidence of defect generation by electronic recoils, with the fraction of electronic recoils with energies $<85$ keV that are spatially correlated with defects $<0.1$$\%$.Comment: 9 pages, 7 figure

Combining machine learning and metaheuristics algorithms for classification method PROAFTN

© Crown 2019. The supervised learning classification algorithms are one of the most well known successful techniques for ambient assisted living environments. However the usual supervised learning classification approaches face issues that limit their application especially in dealing with the knowledge interpretation and with very large unbalanced labeled data set. To address these issues fuzzy classification method PROAFTN was proposed. PROAFTN is part of learning algorithms and enables to determine the fuzzy resemblance measures by generalizing the concordance and discordance indexes used in outranking methods. The main goal of this chapter is to show how the combined meta-heuristics with inductive learning techniques can improve performances of the PROAFTN classifier. The improved PROAFTN classifier is described and compared to well known classifiers, in terms of their learning methodology and classification accuracy. Through this chapter we have shown the ability of the metaheuristics when embedded to PROAFTN method to solve efficiency the classification problems

Search for Daily Modulation of MeV Dark Matter Signals with DAMIC-M

Dark Matter (DM) particles with sufficiently large cross sections may scatter as they travel through Earth's bulk. The corresponding changes in the DM flux give rise to a characteristic daily modulation signal in detectors sensitive to DM-electron interactions. Here, we report results obtained from the first underground operation of the DAMIC-M prototype detector searching for such a signal from DM with MeV-scale mass. A model-independent analysis finds no modulation in the rate of 1$e^-$ events with periods in the range 1-48 h. We then use these data to place exclusion limits on DM in the mass range [0.53, 2.7] MeV/c$^2$ interacting with electrons via a dark photon mediator. Taking advantage of the time-dependent signal we improve by $\sim$2 orders of magnitude on our previous limit obtained from the total rate of 1$e^-$ events, using the same data set. This daily modulation search represents the current strongest limit on DM-electron scattering via ultralight mediators for DM masses around 1 MeV/c$^2$

Application of Decision Theory methods for a Community of Madrid Soil classification case

A land classification method was designed for the Community of Madrid (CM), which has lands suitable for either agriculture use or natural spaces. The process started from an extensive previous CM study that contains sets of land attributes with data for 122 types and a minimum-requirements method providing a land quality classification (SQ) for each land. Borrowing some tools from Operations Research (OR) and from Decision Science, that SQ has been complemented by an additive valuation method that involves a more restricted set of 13 representative attributes analysed using Attribute Valuation Functions to obtain a quality index, QI, and by an original composite method that uses a fuzzy set procedure to obtain a combined quality index, CQI, that contains relevant information from both the SQ and the QI methods

Research and development project assessment and social impact

Nowadays, organisations increasingly need to adapt to the fast evolution of markets and societies in our globalised world in order to be competitive. Therefore, it is essential to take the right decisions when it comes to invest in research and development (R & D) projects. However, an issue that has not been given much attention is how to measure the social impact (or return) of R & D projects. In this exploratory study, the findings of an analysis of how R & D projects are assessed and selected, including this social perspective, are presented. The methodology which has been used in this research includes both interviews and analysis of the data obtained through them. The major finding is that in the current situation the social impact is not taken into account, but is growing the awareness of this perspective among different types of organizations dealing with R & D activities.(undefined)info:eu-repo/semantics/publishedVersio

Emission of single and few electrons in XENON1T and limits on light dark matter

Delayed single- and few-electron emissions plague dual-phase time projection chambers, limiting their potential to search for light-mass dark matter. This paper examines the origins of these events in the XENON1T experiment. Characterization of the intensity of delayed electron backgrounds shows that the resulting emissions are correlated, in time and position, with high-energy events and can effectively be vetoed. In this work we extend previous S2-only analyses down to a single electron. From this analysis, after removing the correlated backgrounds, we observe rates <30 events/(electron×kg×day) in the region of interest spanning 1 to 5 electrons. We derive 90% confidence upper limits for dark matter-electron scattering, first direct limits on the electric dipole, magnetic dipole, and anapole interactions, and bosonic dark matter models, where we exclude new parameter space for dark photons and solar dark photons