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Xinjie Luo

Publications and source records attributed to Xinjie Luo.

2 recordsLinked to original sources

A prolonged plateau-to-tail transition in the Type II supernova SN2025abyc

We present optical photometric and spectroscopic observations of the Type II supernova SN2025abyc. During the optically thick phase between approximately 10 and 70 d after explosion, its light curves show strongly wavelength-dependent decline rates of approximately 2.7, 2.1, 0.9, and 0.8 mag/100d in the g, c, r, and o bands, respectively. At approximately 70 d, the light curves begin to depart from their nearly linear plateau evolution and gradually transition toward the radioactive tail. A Fermi-Dirac fit to the well-sampled ATLAS o-band light curve yields a transition midpoint of t_PT ~ 100.5d. The interval between the end of the linear plateau and this transition midpoint is approximately 30 d, indicating a prolonged plateau-to-tail transition. This timescale is comparable to those measured for SN2013by, SN2013ej, and SN2014G. Spectroscopically, at +13 d post-explosion, the Halpha profile appears weak and broad, whereas Hbeta and Hgamma display clear P-Cygni profiles. This morphology can be explained by the normal early spectroscopic evolution of SNe II, although partial filling of the Halpha absorption trough by emission associated with circumstellar interaction cannot be excluded. SN2025abyc otherwise follows the general photospheric velocity evolution of SNe II, while remaining toward the high-velocity side of the comparison distribution in Halpha, Hbeta, and FeII. Exploratory light-curve modelling suggests a synthesized Ni mass of approximately 0.03-0.04 solar mass. We suggest that the extended circumstellar environment, Ni distribution, and hydrogen-envelope structure could all play a role in shaping the observed light-curve evolution, particularly the prolonged plateau-to-tail transition.

astro-ph.SR

Osprey: Pixel Understanding with Visual Instruction Tuning

Multimodal large language models (MLLMs) have recently achieved impressive general-purpose vision-language capabilities through visual instruction tuning. However, current MLLMs primarily focus on image-level or box-level understanding, falling short in achieving fine-grained vision-language alignment at pixel level. Besides, the lack of mask-based instruction data limits their advancements. In this paper, we propose Osprey, a mask-text instruction tuning approach, to extend MLLMs by incorporating fine-grained mask regions into language instruction, aiming at achieving pixel-wise visual understanding. To achieve this goal, we first meticulously curate a mask-based region-text dataset with 724K samples, and then design a vision-language model by injecting pixel-level representation into LLM. Specifically, Osprey adopts a convolutional CLIP backbone as the vision encoder and employs a mask-aware visual extractor to extract precise visual mask features from high resolution input. Experimental results demonstrate Osprey's superiority in various region understanding tasks, showcasing its new capability for pixel-level instruction tuning. In particular, Osprey can be integrated with Segment Anything Model (SAM) seamlessly to obtain multi-granularity semantics. The source code, dataset and demo can be found at https://github.com/CircleRadon/Osprey.

cs.CV