Progenitor delay-time distribution of short gamma-ray bursts: Constraints from observations

Context. The progenitors of short gamma-ray bursts (SGRBs) have not yet been well identified. The most popular model is the merger of compact object binaries (NS-NS/NS-BH). However, other progenitor models cannot be ruled out. The delay-time distribution of SGRB progenitors, which is an important property to constrain progenitor models, is still poorly understood. Aims. We aim to better constrain the luminosity function of SGRBs and the delay-time distribution of their progenitors with newly discovered SGRBs. Methods. We present a low-contamination sample of 16 Swift SGRBs that is better defined by a duration shorter than 0.8 s. By using this robust sample and by combining a self-consistent star formation model with various models for the distribution of time delays, the redshift distribution of SGRBs is calculated and then compared to the observational data. Results. We find that the power-law delay distribution model is disfavored and that only the lognormal delay distribution model with the typical delay tau >= 3 Gyr is consistent with the data. Comparing Swift SGRBs with T90 > 0.8 s to our robust sample (T90 < 0.8 s), we find a significant difference in the time delays between these two samples. Conclusions. Our results show that the progenitors of SGRBs are dominated by relatively long-lived systems (tau >= 3 Gyr), which contrasts the results found for Type Ia supernovae. We therefore conclude that primordial NS-NS systems are not favored as the dominant SGRB progenitors. Alternatively, dynamically formed NS-NS/BH and primordial NS-BH systems with average delays longer than 5 Gyr may contribute a significant fraction to the overall SGRB progenitors.Comment: 8 pages, 6 figures, Astron. Astrophys. in pres

Hadronic decays of the highly excited 2D2D DsD_s resonances

Hadronic decays of the highly excited $2D$ $D_s$ resonances have been studied in the $^3P_0$ model. Widths of all possible hadronic decay channels of the $2D$ $D_s$ have been computed. $D^*_{s1}(2700)$, $D^*_{s1}(2860)$, $D^*_{s3}(2860)$, $D(2600)$ and $D(2750)$ can be produced from hadronic decays of the $2D$ $D_s$, and relevant hadronic decay widths have been particularly paid attention to. The hadronic decay widths of $2D$ $D_s$ to $D(2600)$ or $D(2750)$ may be large, and the numerical results are different in different assignments of $D(2600)$ and $D(2750)$. The hadronic decay widths of $2D$ $D_s$ to $D^*_{s1}(2860)$, $D^*_{s3}(2860)$ or $D^*_{s1}(2700)$ are very small, and different in different assignments of $D^*_{s1}(2700)$.Comment: 7 pages, 1 figure. High Energy Physics - Theor

g-Factors and the Interplay of Collective and Single-Particle Degrees of Freedom in Superdeformed Mass-190 Nuclei

Interplay of collective and single-particle degrees of freedom is a common phenomenon in strongly correlated many-body systems. Despite many successful efforts in the study of superdeformed nuclei, there is still unexplored physics that can be best understood only through the nuclear magnetic properties. We point out that study of the gyromagnetic factor (g-factor) may open a unique opportunity for understanding superdeformed structure. Our calculations suggest that investigation of the g-factor dependence on spin and particle number can provide important information on single-particle structure and its interplay with collective motion in the superdeformed mass-190 nuclei. Modern experimental techniques combined with the new generation of sensitive detectors should be capable of testing our predictions.Comment: 4 pages, 2 eps figures, accepted by Phys. Rev.

A Double-Jet System in the G31.41+0.31 Hot Molecular Core

This work presents a detailed study of the gas kinematics towards the "Hot Molecular Core" (HMC) G31.41+0.31 via multi-epoch VLBI observations of the H2O 22 GHz and CH3OH 6.7 GHz masers, and single-epoch VLBI of the OH 1.6 GHz masers. Water masers present a symmetric spatial distribution with respect to the HMC center, where two nearby (0.2" apart), compact, VLA sources (labeled "A" and "B") are previously detected. The spatial distribution of a first group of water masers, named "J1", is well fit with an elliptical profile, and the maser proper motions mainly diverge from the ellipse center, with average speed of 36 km s-1. These findings strongly suggest that the "J1" water maser group traces the heads of a young (dynamical time of 1.3 10^3 yr), powerful (momentum rate of ~0.2 M_sun yr-1 km s-1), collimated (semi-opening angle ~10 deg) jet emerging from a MYSO located close (within 0.15") to the VLA source "B". Most of the water features not belonging to "J1" present an elongated (about 2" in size), NE--SW oriented (PA = 70 deg), S-shape distribution, which we denote with the label "J2". The elongated distribution of the "J2" group and the direction of motion, approximately parallel to the direction of elongation, of most "J2" water masers suggests the presence of another collimated outflow, emitted from a MYSO near the VLA source "A". The orientation of the "J2" jet agrees well with that (PA = 68 deg) of the well-defined V_LSR gradient across the HMC revealed by previous interferometric, thermal line observations. Furthermore, the "J2" jet is powerful enough to sustain the large momentum rate, 0.3 M_sun yr-1 km s-1, estimated assuming that the V_LSR gradient represents a collimated outflow. These two facts lead us to favour the interpretation of the V_LSR gradient across the G31.41+0.31 HMC in terms of a compact and collimated outflow.Comment: 23 pages, 7 figures, accepted for publication in Astronomy & Astrophysic

Efficient, concurrent Bayesian analysis of full waveform LaDAR data

Bayesian analysis of full waveform laser detection and ranging (LaDAR) signals using reversible jump Markov chain Monte Carlo (RJMCMC) algorithms have shown higher estimation accuracy, resolution and sensitivity to detect weak signatures for 3D surface profiling, and construct multiple layer images with varying number of surface returns. However, it is computational expensive. Although parallel computing has the potential to reduce both the processing time and the requirement for persistent memory storage, parallelizing the serial sampling procedure in RJMCMC is a significant challenge in both statistical and computing domains. While several strategies have been developed for Markov chain Monte Carlo (MCMC) parallelization, these are usually restricted to fixed dimensional parameter estimates, and not obviously applicable to RJMCMC for varying dimensional signal analysis. In the statistical domain, we propose an effective, concurrent RJMCMC algorithm, state space decomposition RJMCMC (SSD-RJMCMC), which divides the entire state space into groups and assign to each an independent RJMCMC chain with restricted variation of model dimensions. It intrinsically has a parallel structure, a form of model-level parallelization. Applying the convergence diagnostic, we can adaptively assess the convergence of the Markov chain on-the-fly and so dynamically terminate the chain generation. Evaluations on both synthetic and real data demonstrate that the concurrent chains have shorter convergence length and hence improved sampling efficiency. Parallel exploration of the candidate models, in conjunction with an error detection and correction scheme, improves the reliability of surface detection. By adaptively generating a complimentary MCMC sequence for the determined model, it enhances the accuracy for surface profiling. In the computing domain, we develop a data parallel SSD-RJMCMC (DP SSD-RJMCMCU) to achieve efficient parallel implementation on a distributed computer cluster. Adding data-level parallelization on top of the model-level parallelization, it formalizes a task queue and introduces an automatic scheduler for dynamic task allocation. These two strategies successfully diminish the load imbalance that occurred in SSD-RJMCMC. Thanks to the coarse granularity, the processors communicate at a very low frequency. The MPIbased implementation on a Beowulf cluster demonstrates that compared with RJMCMC, DP SSD-RJMCMCU has further reduced problem size and computation complexity. Therefore, it can achieve a super linear speedup if the number of data segments and processors are chosen wisely