Cold gas and a Milky Way-type 2175 {\AA} bump in a metal-rich and highly depleted absorption system

We report the detection of a strong Milky Way-type 2175 \AA$$ extinction bump at $z$ = 2.1166 in the quasar spectrum towards SDSS J121143.42+083349.7 from the Sloan Digital Sky Survey (SDSS) Data Release 10. We conduct follow up observations with the Echelle Spectrograph and Imager (ESI) onboard the Keck-II telescope and the Ultraviolet and Visual Echelle Spectrograph (UVES) on the VLT. This 2175 \AA$$ absorber is remarkable in that we simultaneously detect neutral carbon (C I), neutral chlorine (Cl I), and carbon monoxide (CO). It also qualifies as a damped Lyman alpha system. The J1211+0833 absorber is found to be metal-rich and has a dust depletion pattern resembling that of the Milky Way disk clouds. We use the column densities of the C I fine structure states and the C II/C I ratio (under the assumption of ionization equilibrium) to derive the temperature and volume density in the absorbing gas. A Cloudy photoionization model is constructed, which utilizes additional atoms/ions to constrain the physical conditions. The inferred physical conditions are consistent with a canonical cold (T $\sim$ 100 K) neutral medium with a high density ($n$(H I) $\sim$ 100 cm$^{-3}$) and a slightly higher pressure than the local interstellar medium. Given the simultaneous presence of C I, CO, and the 2175 \AA$$ bump, combined with the high metallicity, high dust depletion level and overall low ionization state of the gas, the absorber towards J1211+0833 supports the scenario that the presence of the bump requires an evolved stellar population.Comment: 18 pages, 17 figures, to be published in MNRA

Spatially resolved BPT mapping of nearby Seyfert 2 galaxies

We present spatially resolved BPT mapping of the extended narrow line regions (ENLRs) of seven nearby Seyfert 2 galaxies, using HST narrow band filter imaging. We construct the BPT diagrams using $\leq$ 0.1" resolution emission line images of [O III]$\lambda$5007, H$\alpha$, [S II]$\lambda$$\lambda$6717,6731, and H$\beta$. By mapping these diagnostic lines according to the BPT classification, we dissect the ENLR into Seyfert, LINER, and star-forming regions. The nucleus and ionization cones are dominated by Seyfert-type emission, which can be interpreted as predominantly photoionization by the active galactic nucleus (AGN). The Seyfert nucleus and ionization cones transition to and are surrounded by a LINER cocoon, extending up to $\sim$ 250 pc in thickness. The ubiquity of the LINER cocoon in Seyfert 2 galaxies suggests that the circumnuclear regions are not necessarily Seyfert-type, and LINER activity plays an important role in Seyfert 2 galaxies. We demonstrate that spatially resolved diagnostics are crucial to understanding the excitation mechanisms in different regions and the AGN-host galaxy interactions.Comment: 19 pages, 14 figures; accepted for publication in Ap

Large Trajectory Models are Scalable Motion Predictors and Planners

Motion prediction and planning are vital tasks in autonomous driving, and recent efforts have shifted to machine learning-based approaches. The challenges include understanding diverse road topologies, reasoning traffic dynamics over a long time horizon, interpreting heterogeneous behaviors, and generating policies in a large continuous state space. Inspired by the success of large language models in addressing similar complexities through model scaling, we introduce a scalable trajectory model called State Transformer (STR). STR reformulates the motion prediction and motion planning problems by arranging observations, states, and actions into one unified sequence modeling task. With a simple model design, STR consistently outperforms baseline approaches in both problems. Remarkably, experimental results reveal that large trajectory models (LTMs), such as STR, adhere to the scaling laws by presenting outstanding adaptability and learning efficiency. Qualitative results further demonstrate that LTMs are capable of making plausible predictions in scenarios that diverge significantly from the training data distribution. LTMs also learn to make complex reasonings for long-term planning, without explicit loss designs or costly high-level annotations

Decomposition and Decoupling Analysis of Carbon Emissions in Xinjiang Energy Base, China

China faces a difficult choice of maintaining socioeconomic development and carbon emissions mitigation. Analyzing the decoupling relationship between economic development and carbon emissions and its driving factors from a regional perspective is the key for the Chinese government to achieve the 2030 emission reduction target. This study adopted the logarithmic mean Divisia index (LMDI) method and Tapio index, decomposed the driving forces of the decoupling, and measured the sector’s decoupling states from carbon emissions in Xinjiang province, China. The results found that: (1) Xinjiang’s carbon emissions increased from 93.34 Mt in 2000 to 468.12 Mt in 2017. Energy-intensive industries were the key body of carbon emissions in Xinjiang. (2) The economic activity effect played the decisive factor to carbon emissions increase, which account for 93.58%, 81.51%, and 58.62% in Xinjiang during 2000–2005, 2005–2010, and 2010–2017, respectively. The energy intensity effect proved the dominant influence for carbon emissions mitigation, which accounted for −22.39% of carbon emissions increase during 2000–2010. (3) Weak decoupling (WD), expansive coupling (EC), expansive negative decoupling (END) and strong negative decoupling (SND) were identified in Xinjiang during 2001 to 2017. Gross domestic product (GDP) per capita elasticity has a major inhibitory effect on the carbon emissions decoupling. Energy intensity elasticity played a major driver to the decoupling in Xinjiang. Most industries have not reached the decoupling state in Xinjiang. Fuel processing, power generation, chemicals, non-ferrous, iron and steel industries mainly shown states of END and EC. On this basis, it is suggested that local governments should adjust the industrial structure, optimize energy consumption structure, and promote energy conservation and emission reduction to tap the potential of carbon emissions mitigation in key sectors