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  1. Abstract

    To test theoretical models of massive star formation it is important to compare their predictions with observed systems. To this end, we conduct CO molecular line radiative transfer post-processing of 3D magnetohydrodynamic simulations of various stages in the evolutionary sequence of a massive protostellar core, including its infall envelope and disk wind outflow. Synthetic position–position–velocity cubes of various transitions of12CO,13CO, and C18O emission are generated. We also carry out simulated Atacama Large Millimeter/submillimeter Array (ALMA) observations of this emission. We compare the mass, momentum, and kinetic energy estimates obtained from molecular lines to the true values, finding that the mass and momentum estimates can have uncertainties of up to a factor of 4. However, the kinetic energy estimated from molecular lines is more significantly underestimated. Additionally, we compare the mass outflow rate and momentum outflow rate obtained from the synthetic spectra with the true values. Finally, we compare the synthetic spectra with real examples of ALMA-observed protostars and determine the best-fitting protostellar masses and outflow inclination angles. We then calculate the mass outflow rate and momentum outflow rate for these sources, finding that both rates agree with theoretical protostellar evolutionary tracks.

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  2. Abstract In this paper, we carry out a pilot parameter exploration for the collision-induced magnetic reconnection (CMR) mechanism that forms filamentary molecular clouds. Following Kong et al., we utilize Athena++ to model CMR in the context of resistive magnetohydrodynamics (MHD), considering the effect from seven physical conditions, including the ohmic resistivity ( η ), the magnetic field ( B ), the cloud density ( ρ ), the cloud radius R , the isothermal temperature T , the collision velocity v x , and the shear velocity v z . Compared to their fiducial model, we consider a higher and a lower value for each one of the seven parameters. We quantify the exploration results with five metrics, including the density probability distribution function ( ρ -PDF), the filament morphology (250 μ m dust emission), the B – ρ relation, the dominant fiber width, and the ringiness that describes the significance of the ringlike substructures. The exploration forms straight and curved CMR filaments with rich substructures that are highly variable in space and time. The variation translates to fluctuation in all five metrics, reflecting the chaotic nature of magnetic reconnection in CMR. A temporary B ∝ ρ relation is noticeable during the first 0.6 Myr. Overall, the exploration provides useful initial insights into the CMR mechanism. 
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  3. The actor-critic RL is widely used in various robotic control tasks. By viewing the actor-critic RL from the perspective of variational inference (VI), the policy network is trained to obtain the approximate posterior of actions given the optimality criteria. However, in practice, the actor-critic RL may yield suboptimal policy estimates due to the amortization gap and insufficient exploration. In this work, inspired by the previous use of Hamiltonian Monte Carlo (HMC) in VI, we propose to integrate the policy network of actor-critic RL with HMC, which is termed as Hamiltonian Policy. As such we propose to evolve actions from the base policy according to HMC, and our proposed method has many benefits. First, HMC can improve the policy distribution to better approximate the posterior and hence reduce the amortization gap. Second, HMC can also guide the exploration more to the regions of action spaces with higher Q values, enhancing the exploration efficiency. Further, instead of directly applying HMC into RL, we propose a new leapfrog operator to simulate the Hamiltonian dynamics. Finally, in safe RL problems, we find that the proposed method can not only improve the achieved return, but also reduce safety constraint violations by discarding potentially unsafe actions. With comprehensive empirical experiments on continuous control baselines, including MuJoCo and PyBullet Roboschool, we show that the proposed approach is a data-efficient and easy-to-implement improvement over previous actor-critic methods. 
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  4. Abstract We define a sample of 200 protostellar outflows showing blue- and redshifted CO emission in the nearby molecular clouds Ophiuchus, Taurus, Perseus, and Orion, to investigate the correlation between outflow orientations and local, but relatively large-scale, magnetic field directions traced by Planck 353 GHz dust polarization. At high significance ( p ∼ 10 −4 ), we exclude a random distribution of relative orientations and find that there is a preference for alignment of projected plane of sky outflow axes with magnetic field directions. The distribution of relative position angles peaks at ∼30° and exhibits a broad dispersion of ∼50°. These results indicate that magnetic fields have dynamical influence in regulating the launching and/or propagation directions of outflows. However, the significant dispersion around perfect alignment orientation implies that there are large measurement uncertainties and/or a high degree of intrinsic variation caused by other physical processes, such as turbulence or strong stellar dynamical interactions. Outflow to magnetic field alignment is expected to lead to a correlation in the directions of nearby outflow pairs, depending on the degree of order of the field. Analyzing this effect, we find limited correlation, except on relatively small scales ≲0.5 pc. Furthermore, we train a convolutional neural network to infer the inclination angle of outflows with respect to the line of sight and apply it to our outflow sample to estimate their full 3D orientations. We find that the angles between outflow pairs in 3D space also show evidence of small-scale alignment. 
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  5. Teaching a deep reinforcement learning (RL) agent to follow instructions in multi-task environments is a challenging problem. We consider that user defines every task by a linear temporal logic (LTL) formula. However, some causal dependencies in complex environments may be unknown to the user in advance. Hence, when human user is specifying instructions, the robot cannot solve the tasks by simply following the given instructions. In this work, we propose a hierarchical reinforcement learning (HRL) framework in which a symbolic transition model is learned to efficiently produce high-level plans that can guide the agent efficiently solve different tasks. Specifically, the symbolic transition model is learned by inductive logic programming (ILP) to capture logic rules of state transitions. By planning over the product of the symbolic transition model and the automaton derived from the LTL formula, the agent can resolve causal dependencies and break a causally complex problem down into a sequence of simpler low-level sub-tasks. We evaluate the proposed framework on three environments in both discrete and continuous domains, showing advantages over previous representative methods. 
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  6. Abstract We adopt the deep learning method casi-3d (Convolutional Approach to Structure Identification-3D) to systemically identify protostellar outflows in 12 CO and 13 CO observations of the nearby molecular clouds, Ophiuchus, Taurus, Perseus, and Orion. The total outflow masses are 267 M ⊙ , 795 M ⊙ , 1305 M ⊙ , and 6332 M ⊙ for Ophiuchus, Taurus, Perseus, and Orion, respectively. We show the outflow mass in each cloud is linearly proportional to the total number of young stellar objects. The estimated total 3D deprojected outflow energies are 9 × 10 45 erg, 6 × 10 46 erg, 1.2 × 10 47 erg, and 6 × 10 47 erg for Ophiuchus, Taurus, Perseus, and Orion, respectively. The energy associated with outflows is sufficient to offset turbulent dissipation at the current epoch for all four clouds. All clouds also exhibit a break point in the spatial power spectrum of the outflow prediction map, which likely corresponds to the typical outflow mass and energy injection scale. 
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  7. null (Ed.)