Daily Papers

Cool (approx 10^4K), dense material permeates the hot (approx 10^6K), tenuous solar corona in form of coronal condensations, for example prominences and coronal rain. As the solar atmosphere evolves, turbulence can drive mixing between the condensations and the surrounding corona, with the mixing layer exhibiting an enhancement in emission from intermediate temperature (approx10^5K) spectral lines, which is often attributed to turbulent heating within the mixing layer. However, radiative cooling is highly efficient at intermediate temperatures and numerical simulations have shown that radiative cooling can far exceed turbulent heating in prominence-corona mixing scenarios. As such the mixing layer can have a net loss of thermal energy, i.e., the mixing layer is cooling rather than heating. Here, we investigate the observational signatures of cooling processes in Kelvin-Helmholtz mixing between a prominence thread and the surrounding solar corona through 2D numerical simulations. Optically thin emission is synthesised for Si IV, along with optically thick emission for Halpha, Ca II K and Mg II h using Lightweaver The Mg II h probes the turbulent mixing layer, whereas Halpha and Ca II K form within the thread and along its boundary respectively. As the mixing evolves, intermediate temperatures form leading to an increase in Si IV emission, which coincides with increased radiative losses. The simulation is dominated by cooling in the mixing layer, rather than turbulent heating, and yet enhanced emission in warm lines is produced. As such, an observational signature of decreased emission in cooler lines and increased emission in hotter lines may be a signature of mixing, rather than an implication of heating.

The field of exocomets has been built around the unmatched number of detections made in the circumstellar disc of the archetypal star Beta Pictoris. An exocomet detection in spectroscopy is identified by variable atomic absorption features in a stellar spectrum, associated with transiting gas in and trailing an exocomet coma. This paper presents the largest spectroscopic search for exocomet transits to date, which overcomes the limitations of biased samples of stars with debris discs, and instead looks through the approx7500 stars in the HARPS archive for signs of exocomets in the CaII doublet (H:396.847nm and K:393.366nm). The search resulted in 155 candidate stars, which after filtering for false positives (e.g. binaries, stellar activity, etc.), were cut down to 22 stars. These 22 stars are classified into Tier1, 2, and 3 exocomet candidates, reflecting the confidence level of their exocomet detection. Our two best candidates (Tier1: Beta Pictoris, HD172555) and four lower confidence candidates (Tier2: Gl1, HIP5158, HD94771, HR1996) are discussed, yielding a detection rate of 0.03% (Tier1 only) and 0.1% (Tier1 & 2) in the HARPS sample. Both Tier1 stars are known exocomet host stars. These two young A-type stars correspond to 0.4% of all A-types in the sample, suggesting that detecting signs of exocomet transits using CaII is more likely around young A-type stars. Reanalysing a past HARPS study, we found no evidence to support the previously claimed four exocomet detections, indicating either that those detections are not robust or that we are only sensitive to the strongest signals.

Accelerating research on Large Multimodal Models (LMMs) in non-English languages is crucial for enhancing user experiences across broader populations. In this paper, we introduce JMMMU (Japanese MMMU), the first large-scale Japanese benchmark designed to evaluate LMMs on expert-level tasks based on the Japanese cultural context. To facilitate comprehensive culture-aware evaluation, JMMMU features two complementary subsets: (i) culture-agnostic (CA) subset, where the culture-independent subjects (e.g., Math) are selected and translated into Japanese, enabling one-to-one comparison with its English counterpart MMMU; and (ii) culture-specific (CS) subset, comprising newly crafted subjects that reflect Japanese cultural context. Using the CA subset, we observe performance drop in many LMMs when evaluated in Japanese, which is purely attributable to language variation. Using the CS subset, we reveal their inadequate Japanese cultural understanding. Further, by combining both subsets, we identify that some LMMs perform well on the CA subset but not on the CS subset, exposing a shallow understanding of the Japanese language that lacks depth in cultural understanding. We hope this work will not only help advance LMM performance in Japanese but also serve as a guideline to create high-standard, culturally diverse benchmarks for multilingual LMM development. The project page is https://mmmu-japanese-benchmark.github.io/JMMMU/.