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Context.Quasar outflows play a significant role in the active galactic nucleus (AGN) feedback, impacting the interstellar medium and potentially influencing galaxy evolution. Characterizing these outflows is essential for understanding AGN-driven processes. Aims.We aim to analyze the physical properties of the mini-broad absorption line outflow in quasar J1402+2330 using data from the Dark Energy Spectroscopic Instrument (DESI) survey. We seek to measure the outflow’s location, energetics, and potential impact on AGN feedback processes. Methods.In the spectrum of J1402+2330, we identify multiple ionic absorption lines, including ground and excited states. We measure the ionic column densities and then use photoionization models to determine the total hydrogen column density and ionization parameter of the outflow. We utilized the population ratio of the excited state to the ground state of N IIIand S IVto determine the electron number density. Results.The derived electron number density, combined with the ionization parameter, indicates an outflow distance of approximately 2.2 kpc from the central source. Having a mass outflow rate of more than one thousand solar masses per year and a kinetic energy output exceeding 5% of the Eddington luminosity, this outflow can significantly contribute to AGN feedback. Conclusions.Our findings suggest the absorption outflow in J1402+2330 plays a potentially significant role in AGN feedback processes. This study highlights the value of DESI data in exploring AGN feedback mechanisms. 

Abstract Recently, layered transition metal thiophosphate MPX₃(M= transition metals,X= S or Se) have gained significant attention because of their rich magnetic, optical, and electronic properties. Specifically, the diverse magnetic structures and the robustness of magnetism in the two-dimensional (2D) limit have made them prominent candidates to study 2D magnetism. Numerous efforts such as substitutions and interlayer intercalations have been adopted to tune the magnetic properties of these materials, which has greatly deepened the understanding of the underlying mechanisms that govern the properties. In this work, we focus on modifying the magnetism of Ising-type antiferromagnet FePS₃using electrochemical lithium intercalation. Our work demonstrate the effectiveness of electrochemical intercalation as a controllable tool to modulating magnetism, including tuning magnetic ordering temperature and inducing low temperature spin-glass state, offering an approach for implementing this material into applications. 

Abstract Observations of temporary Forbush decreases (FDs) in the Galactic cosmic-ray (GCR) flux due to the passage of solar storms are useful for space-weather studies and alerts. Here, we introduce techniques that use global networks of ground-based neutron monitors and muon detectors to measure variations of GCR rigidity spectra in space during FDs by (1) fitting count rate decreases for power-law rigidity spectra in space with anisotropy up to second order and (2) using the “leader fraction” derived from a single neutron monitor. We demonstrate that both provide consistent results for hourly spectral index variations for five major FDs, and they agree with daily space-based data when available from the Alpha Magnetic Spectrometer. We have also made the neutron monitor leader fraction publicly available in real time. This work verifies that ground-based observations can be used to precisely monitor GCR spectral variation over a wide range of rigidities during space-weather events, with results in real time or from short-term postanalysis. 

ABSTRACT In the Hubble Space Telescope/Cosmic Origins Spectrograph spectrum of the Seyfert 1 galaxy 2MASX J14292507+4518318, we have identified a narrow absorption line outflow system with a velocity of −151 km s−1. This outflow exhibits absorption troughs from the resonance states of ions like C iv, N v, S iv, and Si ii, as well as excited states from C ii* and Si ii*. Our investigation of the outflow involved measuring ionic column densities and conducting photoionization analysis. These allow the total column density of the outflow to be estimated as log NH = 19.84 cm−2, its ionization parameter to be log UH = −2.0, and its electron number density to be log ne = 2.75 cm−3. These measurements enabled us to determine the mass-loss rate and the kinetic luminosity of the outflow system to be $$\dot{M}$$ = 0.22 $$\mathrm{ M}_{\odot } \, \mathrm{ yr}^{-1}$$ and $$\log \dot{E_{\mathrm{ K}}}$$ = 39.3 erg s−1, respectively. We have also measured the location of the outflow system to be at ∼275 pc from the central source. This outflow does not contribute to the active galactic nucleus (AGN) feedback processes due to the low ratio of the outflow’s kinetic luminosity to the AGN’s Eddington luminosity ($$\dot{E_{\mathrm{ K}}}/{L_{\mathrm{ Edd}}}\approx 0.00025 {{\, \rm per\, cent}}$$). This outflow is remarkably similar to the two bipolar lobe outflows observed in the Milky Way by XMM–Newton and Chandra. 

To perform manipulation tasks in the real world, robots need to operate on objects with various shapes, sizes and without access to geometric models. To achieve this it is often infeasible to train monolithic neural network policies across such large variations in object properties. Towards this generalization challenge, we propose to learn modular task policies which compose object-centric task-axes controllers. These task-axes controllers are parameterized by properties associated with underlying objects in the scene. We infer these controller parameters directly from visual input using multi- view dense correspondence learning. Our overall approach provides a simple and yet powerful framework for learning manipulation tasks. We empirically evaluate our approach on 3 different manipulation tasks and show its ability to generalize to large variance in object size, shape and geometry. 

Manipulation tasks can often be decomposed into multiple subtasks performed in parallel, e.g., sliding an object to a goal pose while maintaining con- tact with a table. Individual subtasks can be achieved by task-axis controllers defined relative to the objects being manipulated, and a set of object-centric controllers can be combined in an hierarchy. In prior works, such combinations are defined manually or learned from demonstrations. By contrast, we propose using reinforcement learning to dynamically compose hierarchical object-centric controllers for manipulation tasks. Experiments in both simulation and real world show how the proposed approach leads to improved sample efficiency, zero-shot generalization to novel test environments, and simulation-to-reality transfer with- out fine-tuning.