ALICE is the heavy-ion experiment at the CERN Large Hadron Collider. The experiment continuously took data during the first physics campaign of the machine from fall 2009 until early 2013, using proton and lead-ion beams. In this paper we describe the running environment and the data handling procedures, and discuss the performance of the ALICE detectors and analysis methods for various physics observables.

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Originally designed as a new nuclear reactor monitoring device, the Nucifer detector has successfully detected its first neutrinos. We provide the second shortest baseline measurement of the reactor neutrino flux. The detection of electron antineutrinos emitted in the decay chains of the fission products, combined with reactor core simulations, provides a new tool to assess both the thermal power and the fissile content of the whole nuclear core and could be used by the International Agency for Atomic Energy (IAEA) to enhance the Safeguards of civil nuclear reactors. Deployed at only 7.2 m away from the compact Osiris research reactor core (70 MW) operating at the Saclay research centre of the French Alternative Energies and Atomic Energy Commission (CEA), the experiment also exhibits a well-suited configuration to search for a new short baseline oscillation. We report the first results of the Nucifer experiment, describing the performances of the ∼ 0.85 m 3 detector remotely operating at a shallow depth equivalent to ∼ 12 m of water and under intense background radiation conditions. Based on 145 (106) days of data with reactor ON (OFF), leading to the detection of an estimated 40 760 νe, the mean number of detected antineutrinos is 281 ± 7(stat) ± 18(syst) νe/day, in agreement with the prediction 277 ± 23 νe/day. Due to the large background no conclusive results on the existence of light sterile neutrinos could be derived, however. As a first societal application we quantify how antineutrinos could be used for the Plutonium Management and Disposition Agreement. arXiv:1509.05610v4 [physics.ins-det]

Characterizing the spatial extent of epidemics at the outbreak stage is key to controlling the evolution of the disease. At the outbreak, the number of infected individuals is typically small, and therefore, fluctuations around their average are important: then, it is commonly assumed that the susceptible-infected-recovered mechanism can be described by a stochastic birth-death process of GaltonWatson type. The displacements of the infected individuals can be modeled by resorting to Brownian motion, which is applicable when long-range movements and complex network interactions can be safely neglected, like in the case of animal epidemics. In this context, the spatial extent of an epidemic can be assessed by computing the convex hull enclosing the infected individuals at a given time. We derive the exact evolution equations for the mean perimeter and the mean area of the convex hull, and we compare them with Monte Carlo simulations.branching Brownian motion | extreme value statistics M odels of epidemics traditionally consider three classes of populations-namely, the susceptible (S), the infected (I), and the recovered (R). This framework provides the basis of the so-called SIR model (1, 2), a fully connected mean-field model where the population sizes of the three species evolve with time t by the coupled nonlinear equations: dS/dt = −βIS, dI/dt = βIS − γI, and dR/dt = γI. Here, γ is the rate at which an infected individual recovers, and β denotes the rate at which it transmits the disease to a susceptible (3-5). In the simplest version of these models, the recovered cannot be infected again. These rate equations conserve the total population size I(t) + S(t) + R(t) = N; one assumes that, initially, there is only one infected individual, and the rest of the population is susceptible: I(0) = 1, S(0) = N − 1, and R(0) = 0. Of particular interest is the outbreak stage (i.e., the early times of the epidemic process), when the susceptible population is much larger than the number of infected or recovered. During this regime, for large N, the susceptible population hardly evolves and stays S(t) ∼ N; therefore, nonlinear effects can be safely neglected, and one can just monitor the evolution of the infected population alone: dI/dt ∼ (βN − γ)I(t). Thus, the ultimate fate of the epidemics depends on the key dimensionless parameter R 0 = βN/ γ, which is called the reproduction rate. If R 0 > 1, the epidemic explodes and invades a finite fraction of the population; if R 0 < 1, the epidemic goes to extinction, and in the critical case R 0 = 1 the infected population remains constant (6-8).This basic deterministic SIR has been generalized to a variety of both deterministic as well as stochastic models, and distinct advantages and shortcomings are discussed at length in refs. 9-11. Generally speaking, stochastic models are more suitable in the presence of a small number of infected individuals, when fluctuations around the average may be relevant (9, 10). During the outbreak of epidemics, the infected population is typically ...

In this paper we analyze some aspects of exponential flights, a stochastic process that governs the evolution of many random transport phenomena, such as neutron propagation, chemical or biological species migration, and electron motion. We introduce a general framework for d-dimensional setups and emphasize that exponential flights represent a deceivingly simple system, where in most cases closed-form formulas can hardly be obtained. We derive a number of exact (where possible) or asymptotic results, among which are the stationary probability density for two-dimensional systems, a long-standing issue in physics, and the mean residence time in a given volume. Bounded or unbounded domains as well as scattering or absorbing domains are examined, and Monte Carlo simulations are performed so as to support our findings.

Physical observables are often represented as walkers performing random displacements. When the number of collisions before leaving the explored domain is small, the diffusion approximation leads to incongruous results. In this Letter, we explicitly derive an explicit formula for the moments of the number of particle collisions in an arbitrary volume, for a broad class of transport processes. This approach is shown to generalize the celebrated Kac formula for the moments of residence times. Some applications are illustrated for bounded, unbounded and absorbing domains.

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We study the evolution of a collection of individuals subject to Brownian diffusion, reproduction, and disappearance. In particular, we focus on the case where the individuals are initially prepared at equilibrium within a confined geometry. Such systems are widespread in physics and biology and apply for instance to the study of neutron populations in nuclear reactors and the dynamics of bacterial colonies, only to name a few. The fluctuations affecting the number of individuals in space and time may lead to a strong patchiness, with particles clustered together. We show that the analysis of this peculiar behavior can be rather easily carried out by resorting to a backward formalism based on the Green's function, which allows the key physical observables, namely, the particle concentration and the pair correlation function, to be explicitly derived.