Leptonic dark matter annihilation in the evolving universe: constraints and implications

The cosmic electron and positron excesses have been explained as possible dark matter (DM) annihilation products. In this work we investigate the possible effects of such a DM annihilation scenario during the evolution history of the Universe. We first calculate the extragalactic $\gamma$-ray background (EGRB), which is produced through the final state radiation of DM annihilation to charged leptons and the inverse Compton scattering between electrons/positrons and the cosmic microwave background. The DM halo profile and the minimal halo mass, which are not yet well determined from the current N-body simulations, are constrained by the EGRB data from EGRET and Fermi telescopes. Then we discuss the impact of such leptonic DM models on cosmic evolution, such as the reionization and heating of intergalactic medium, neutral Hydrogen 21 cm signal and suppression of structure formation. We show that the impact on the Hydrogen 21 cm signal might show interesting signatures of DM annihilation, but the influence on star formation is not remarkable. Future observations of the 21 cm signals could be used to place new constraints on the properties of DM.Comment: 24 pages, 6 figures and 2 tables. Improved treatment of the energy deposition process, the suppression on structure formation is weaker. Accepted for publication by JCA

Committed emissions from existing energy infrastructure jeopardize 1.5 °C climate target.

Net anthropogenic emissions of carbon dioxide (CO2) must approach zero by mid-century (2050) in order to stabilize the global mean temperature at the level targeted by international efforts1-5. Yet continued expansion of fossil-fuel-burning energy infrastructure implies already 'committed' future CO2 emissions6-13. Here we use detailed datasets of existing fossil-fuel energy infrastructure in 2018 to estimate regional and sectoral patterns of committed CO2 emissions, the sensitivity of such emissions to assumed operating lifetimes and schedules, and the economic value of the associated infrastructure. We estimate that, if operated as historically, existing infrastructure will cumulatively emit about 658 gigatonnes of CO2 (with a range of 226 to 1,479 gigatonnes CO2, depending on the lifetimes and utilization rates assumed). More than half of these emissions are predicted to come from the electricity sector; infrastructure in China, the USA and the 28 member states of the European Union represents approximately 41 per cent, 9 per cent and 7 per cent of the total, respectively. If built, proposed power plants (planned, permitted or under construction) would emit roughly an extra 188 (range 37-427) gigatonnes CO2. Committed emissions from existing and proposed energy infrastructure (about 846 gigatonnes CO2) thus represent more than the entire carbon budget that remains if mean warming is to be limited to 1.5 degrees Celsius (°C) with a probability of 66 to 50 per cent (420-580 gigatonnes CO2)5, and perhaps two-thirds of the remaining carbon budget if mean warming is to be limited to less than 2 °C (1,170-1,500 gigatonnes CO2)5. The remaining carbon budget estimates are varied and nuanced14,15, and depend on the climate target and the availability of large-scale negative emissions16. Nevertheless, our estimates suggest that little or no new CO2-emitting infrastructure can be commissioned, and that existing infrastructure may need to be retired early (or be retrofitted with carbon capture and storage technology) in order to meet the Paris Agreement climate goals17. Given the asset value per tonne of committed emissions, we suggest that the most cost-effective premature infrastructure retirements will be in the electricity and industry sectors, if non-emitting alternatives are available and affordable4,18

Inelastic Scattering of Dark Matter with Heavy Cosmic Rays

We investigate the impact of inelastic collisions between dark matter (DM) and heavy cosmic ray (CR) nuclei on CR propagation. We approximate the fragmentation cross-sections for DM-CR collisions using collider-measured proton-nuclei scattering cross-sections, allowing us to assess how these collisions affect the spectra of CR Boron and Carbon. We derive new CR spectra from DM-CR collisions by incorporating these DM-CR cross-sections into the source terms and solving the diffusion equation for the complete network of reactions involved in generating secondary species. Utilizing the latest data from AMS-02 and DAMPE on the Boron-to-Carbon ratio, we estimate a 95\% upper limit for the effective inelastic cross-section of DM-proton as a function of DM mass. Our findings reveal that at $m_\chi \simeq 2 \,\rm{MeV}$, the effective inelastic cross-section between DM and protons must be less than $\mathcal{O}(10^{-32})~{\rm cm}^2$.Comment: 25 pages, 8 figure

Dynamic Schema Graph Fusion Network for Multi-Domain Dialogue State Tracking

Dialogue State Tracking (DST) aims to keep track of users’ intentions during the course of a conversation. In DST, modelling the relations among domains and slots is still an under-studied problem. Existing approaches that have considered such relations generally fall short in: (1) fusing prior slot-domain membership relations and dialogue-aware dynamic slot relations explicitly, and (2) generalizing to unseen domains. To address these issues, we propose a novel Dynamic Schema Graph Fusion Network (DSGFNet), which generates a dynamic schema graph to explicitly fuse the prior slot-domain membership relations and dialogue-aware dynamic slot relations. It also uses the schemata to facilitate knowledge transfer to new domains. DSGFNet consists of a dialogue utterance encoder, a schema graph encoder, a dialogue-aware schema graph evolving network, and a schema graph enhanced dialogue state decoder. Empirical results on benchmark datasets (i.e., SGD, MultiWOZ2.1, and MultiWOZ2.2), show that DSGFNet outperforms existing methods

The Effects of the Tractor and Semitrailer Routing Problem on Mitigation of Carbon Dioxide Emissions

The incorporation of CO2 emissions minimization in the vehicle routing problem (VRP) is of critical importance to enterprise practice. Focusing on the tractor and semitrailer routing problem with full truckloads between any two terminals of the network, this paper proposes a mathematical programming model with the objective of minimizing CO2 emissions per ton-kilometer. A simulated annealing (SA) algorithm is given to solve practical-scale problems. To evaluate the performance of the proposed algorithm, a lower bound is developed. Computational experiments on various problems generated randomly and a realistic instance are conducted. The results show that the proposed methods are effective and the algorithm can provide reasonable solutions within an acceptable computational time