Constraints on primordial curvature power spectrum with pulsar timing arrays

The stochastic signal detected by NANOGrav, PPTA, EPTA, and CPTA can be explained by the scalar-induced gravitational waves. In order to determine the scalar-induced gravitational waves model that best fits the stochastic signal, we employ both single- and double-peak parameterizations for the power spectrum of the primordial curvature perturbations, where the single-peak scenarios include the $\delta$-function, box, lognormal, and broken power law model, and the double-peak scenario is described by the double lognormal form. Using Bayesian inference, we find that there is no significant evidence for or against the single-peak scenario over the double-peak model, with $\log$ (Bayes factors) among these models $\ln \mathcal{B} < 1$. Therefore, we are not able to distinguish the different shapes of the power spectrum of the primordial curvature perturbation with the current sensitivity of pulsar timing arrays.Comment: 19 pages, 1 table, 7 figure

Empirical studies on the network of social groups: the case of Tencent QQ

Participation in social groups are important but the collective behaviors of human as a group are difficult to analyze due to the difficulties to quantify ordinary social relation, group membership, and to collect a comprehensive dataset. Such difficulties can be circumvented by analyzing online social networks. In this paper, we analyze a comprehensive dataset obtained from Tencent QQ, an instant messenger with the highest market share in China. Specifically, we analyze three derivative networks involving groups and their members -- the hypergraph of groups, the network of groups and the user network -- to reveal social interactions at microscopic and mesoscopic level. Our results uncover interesting behaviors on the growth of user groups, the interactions between groups, and their relationship with member age and gender. These findings lead to insights which are difficult to obtain in ordinary social networks.Comment: 18 pages, 9 figure

Constraining the Merger History of Primordial-Black-Hole Binaries from GWTC-3

Primordial black holes (PBHs) can be not only cold dark matter candidates but also progenitors of binary black holes observed by LIGO-Virgo-KAGRA (LVK) Collaboration. The PBH mass can be shifted to the heavy distribution if multi-merger processes occur. In this work, we constrain the merger history of PBH binaries using the gravitational wave events from the third Gravitational-Wave Transient Catalog (GWTC-3). Considering four commonly used PBH mass functions, namely the log-normal, power-law, broken power-law, and critical collapse forms, we find that the multi-merger processes make a subdominant contribution to the total merger rate. Therefore, the effect of merger history can be safely ignored when estimating the merger rate of PBH binaries. We also find that GWTC-3 is best fitted by the log-normal form among the four PBH mass functions and confirm that the stellar-mass PBHs cannot dominate cold dark matter.Comment: 11 pages, 8 figures, 2 tables; accepted for publication in PR

Observational evidence for a spin-up line in the P-Pdot diagram of millisecond pulsars

It is believed that millisecond pulsars attain their fast spins by accreting matter and angular momentum from companion stars. Theoretical modelling of the accretion process suggests a spin-up line in the period-period derivative ($P$-$\dot{P}$) diagram of millisecond pulsars, which plays an important role in population studies of radio millisecond pulsars and accreting neutron stars in X-ray binaries. Here we present observational evidence for such a spin-up line using a sample of 143 radio pulsars with $P$ < 30 ms. We also find that PSRs~J1823$-$3021A and J1824$-$2452A, located near the classic spin-up line, are consistent with the broad population of millisecond pulsars. Finally, we show that our approach of Bayesian inference can probe accretion physics, allowing constraints to be placed on the accretion rate and the disk-magnetosphere interaction.Comment: 10 pages, 4 figures, 2 tables. Accepted for publication by ApJ

Nitrogen-doped carbon nanotubes with encapsulated Fe nanoparticles as efficient oxygen reduction catalyst for alkaline membrane direct ethanol fuel cells

Exploring low-cost and highly efficient non-precious metal electrocatalysts toward oxygen reduction reaction is crucial for the development of fuel cells. Herein, we report the synthesis of bamboo-like N-doped carbon nanotubes with encapsulated Fe-nanoparticles through high-temperature pyrolysis of multiple nitrogen complex consisting of benzoguanamine, cyanuric acid, and melamine. As-prepared catalyst exhibits high catalytic activity for oxygen reduction with onset potential of 1.10 V and half-wave potential of 0.93 V, as well as low H2O2 yield (<1%) in alkaline medium. The mass activity of the catalyst at 1.0 V (0.58 A g−1) can reach 43% of state-of-the-art commercial Pt/C. This catalyst also exhibits high durability and ethanol tolerance. When it was applied in alkaline membrane direct ethanol fuel cell, the peak power density could reach to 64 mW cm−2, indicating its attractive application prospect in fuel cells

Deep Learning the Effects of Photon Sensors on the Event Reconstruction Performance in an Antineutrino Detector

We provide a fast approach incorporating the usage of deep learning for evaluating the effects of photon sensors in an antineutrino detector on the event reconstruction performance therein. This work is an attempt to harness the power of deep learning for detector designing and upgrade planning. Using the Daya Bay detector as a benchmark case and the vertex reconstruction performance as the objective for the deep neural network, we find that the photomultiplier tubes (PMTs) have different relative importance to the vertex reconstruction. More importantly, the vertex position resolutions for the Daya Bay detector follow approximately a multi-exponential relationship with respect to the number of PMTs and hence, the coverage. This could also assist in deciding on the merits of installing additional PMTs for future detector plans. The approach could easily be used with other objectives in place of vertex reconstruction

Constraints on peculiar velocity distribution of binary black holes using gravitational waves with GWTC-3

The peculiar velocity encodes rich information about the formation, dynamics, evolution, and merging history of binary black holes. In this work, we employ a hierarchical Bayesian model to infer the peculiar velocity distribution of binary black holes for the first time using GWTC-3 by assuming a Maxwell-Boltzmann distribution for the peculiar velocities. The constraint on the peculiar velocity distribution parameter is rather weak and uninformative with the current GWTC-3 data release. However, the measurement of the peculiar velocity distribution can be significantly improved with the next-generation ground-based gravitational wave detectors. For instance, the uncertainty on the peculiar velocity distribution parameter will be measured within $\sim$ 10\% with $10^3$ golden binary black hole events for the Einstein Telescope. We, therefore, conclude that our statistical approach provides a robust inference for the peculiar velocity distribution.Comment: 15 pages, 2 figures