Search

As searches for thermal and self-annihilating dark matter (DM) intensify, it becomes crucial to include as many relevant physical processes and ingredients as possible to refine signal predictions, in particular those which directly relate to the intimate properties of DM. We investigate the combined impact of DM subhalos and of the (velocity-dependent) Sommerfeld enhancement of the annihilation cross section. Both features are expected to play an important role in searches for thermal DM particle candidates with masses around or beyond TeV, or in scenarios with a light dark sector. We provide a detailed analytical description of the phenomena at play, and show how they scale with the subhalo masses and the main Sommerfeld parameters. We derive approximate analytical expressions that can be used to estimate the overall boost factors resulting from these combined effects, from which the intricate phenomenology can be better understood. DM subhalos lead to an increase of the Sommerfeld effect by several orders of magnitude (for both $s$- and $p$-wave annihilation processes), especially on resonances, which makes them critical to get sensible gamma-ray signal predictions for typical targets of different masses (from dwarf galaxies to galaxy clusters)., Comment: v2: 63 pages, 10 figures, improved version (additional plots), to appear in JCAP

Gamma-ray observations have long been used to constrain the properties of dark matter (DM), with a strong focus on weakly interacting massive particles annihilating through velocity-independent processes. However, in the absence of clear-cut observational evidence for the simplest candidates, the interest of the community in more complex DM scenarios involving a velocity-dependent cross-section has been growing steadily over the past few years. We present the first systematic study of velocity-dependent DM annihilation (in particular $p$-wave annihilation and Sommerfeld enhancement) in a variety of astrophysical objects, not only including the well-studied Milky Way dwarf satellite galaxies, but nearby dwarf irregular galaxies and local galaxy clusters as well. Particular attention is given to the interplay between velocity dependence and DM halo substructure. Uncertainties related to halo mass, phase-space and substructure modelling are also discussed in this velocity-dependent context. We show that, for $s$-wave annihilation, extremely large subhalo boost factors are to be expected, up to $10^{11}$ in clusters and up to $10^6-10^7$ in dwarf galaxies where subhalos are usually assumed not to play an important role. Boost factors for $p$-wave annihilation are smaller but can still reach $10^3$ in clusters. The angular extension of the DM signal is also significantly impacted, with e.g. the cluster typical emission radius increasing by a factor of order 10 in the $s$-wave case. We also compute the signal contrast of the objects in our sample with respect to annihilation happening in the Milky Way halo. Overall, we find that the hierarchy between the brightest considered targets depends on the specific details of the assumed particle-physics model., Comment: 52 pages, 10 figures. v3: minor changes, matches the published version

Investigating properties of phase change materials (PCMs) is an important issue due to their extensive use in heat storage systems and thermal regulation devices. Improvement of the efficiency of such systems should be based on a better knowledge of the microscopic mechanisms governing the thermal and rheological characteristics of PCMs. This may be accomplished by the use of molecular simulations of the aforementioned quantities and their linkage with macroscale investigations. In this work, we studied thermophysical properties of $n$-hexadecane for different temperatures regimes using molecular dynamics (MD) and carry out several experimental measurements. Particularly, we focused on the evaluation of various rheological and thermal properties such as thermal conductivity, $\kappa$, viscosity, $\eta$, diffusion coefficient, $D$, and heat capacities $C_p$ and $C_v$. Special attention was paid to the comparison of the results of simulations with experimental ones.

Autonomous systems are being developed and deployed in situations that may require some degree of ethical decision-making ability. As a result, research in machine ethics has proliferated in recent years. This work has included using moral dilemmas as validation mechanisms for implementing decision-making algorithms in ethically-loaded situations. Using trolley-style problems in the context of autonomous vehicles as a case study, I argue (1) that this is a misapplication of philosophical thought experiments because (2) it fails to appreciate the purpose of moral dilemmas, and (3) this has potentially catastrophic consequences; however, (4) there are uses of moral dilemmas in machine ethics that are appropriate and the novel situations that arise in a machine-learning context can shed some light on philosophical work in ethics., Comment: AI Ethics (2022)

We simulate the excited states of the Lipkin model using the recently proposed Quantum Equation of Motion (qEOM) method. The qEOM generalizes the EOM on classical computers and gives access to collective excitations based on quasi-boson operators $\hat{O}^\dagger_n(\alpha)$ of increasing configuration complexity $\alpha$. We show, in particular, that the accuracy strongly depends on the fermion to qubit encoding. Standard encoding leads to large errors, but the use of symmetries and the Gray code reduces the quantum resources and improves significantly the results on current noisy quantum devices. With this encoding scheme, we use IBM quantum machines to compute the energy spectrum for a system of $N=2, 3$ and $4$ particles and compare the accuracy against the exact solution. We found that the results of the approach with $\alpha = 2$, an analog of the second random phase approximation (SRPA), are, in principle, more accurate than with $\alpha = 1$, which corresponds to the random phase approximation (RPA), but the SRPA is more amenable to noise for large coupling strengths. Thus, the proposed scheme shows potential for achieving higher spectroscopic accuracy by implementations with higher configuration complexity, if a proper error mitigation method is applied., Comment: Article: 20 pages, 10 figures

Covariance estimation for the Iterative Closest Point (ICP) point cloud registration algorithm is essential for state estimation and sensor fusion purposes. We argue that a major source of error for ICP is in the input data itself, from the sensor noise to the scene geometry. Benefiting from recent developments in deep learning for point clouds, we propose a data-driven approach to learn an error model for ICP. We estimate covariances modeling data-dependent heteroscedastic aleatoric uncertainty, and epistemic uncertainty using a variational Bayesian approach. The system evaluation is performed on LiDAR odometry on different datasets, highlighting good results in comparison to the state of the art.

Motivated by current interest in understanding statistical properties of random landscapes in high-dimensional spaces, we consider a model of the landscape in $\mathbb{R}^N$ obtained by superimposing $M>N$ plane waves of random wavevectors and amplitudes. For this landscape we show how to compute the "annealed complexity" controlling the asymptotic growth rate of the mean number of stationary points as $N\to \infty$ at fixed ratio $\alpha=M/N>1$. The framework of this computation requires us to study spectral properties of $N\times N$ matrices $W=KTK^T$, where $T$ is diagonal with $M$ mean zero i.i.d. real normally distributed entries, and all $MN$ entries of $K$ are also i.i.d. real normal random variables. We suggest to call the latter Gaussian Marchenko-Pastur Ensemble, as such matrices appeared in the seminal 1967 paper by those authors. We compute the associated mean spectral density and evaluate some moments and correlation functions involving products of characteristic polynomials for such and related matrices., Comment: 40 pages, 6 figures

Physical activity (PA) is significantly associated with many health outcomes. The wide usage of wearable accelerometer-based activity trackers in recent years has provided a unique opportunity for in-depth research on PA and its relations with health outcomes and interventions. Past analysis of activity tracker data relies heavily on aggregating minute-level PA records into day-level summary statistics, in which important information of PA temporal/diurnal patterns is lost. In this paper we propose a novel functional data analysis approach based on Riemann manifolds for modeling PA and its longitudinal changes. We model smoothed minute-level PA of a day as one-dimensional Riemann manifolds and longitudinal changes in PA in different visits as deformations between manifolds. The variability in changes of PA among a cohort of subjects is characterized via variability in the deformation. Functional principal component analysis is further adopted to model the deformations and PC scores are used as a proxy in modeling the relation between changes in PA and health outcomes and/or interventions. We conduct comprehensive analyses on data from two clinical trials: Reach for Health (RfH) and Metabolism, Exercise and Nutrition at UCSD (MENU), focusing on the effect of interventions on longitudinal changes in PA patterns and how different modes of changes in PA influence weight loss, respectively. The proposed approach reveals unique modes of changes including overall enhanced PA, boosted morning PA, and shifts of active hours specific to each study cohort. The results bring new insights into the study of longitudinal changes in PA and health and have the potential to facilitate designing of effective health interventions and guidelines.