Astroconformer: The Prospects of Analyzing Stellar Light Curves with Transformer-Based Deep Learning Models

Light curves of stars encapsulate a wealth of information about stellar oscillations and granulation, thereby offering key insights into the internal structure and evolutionary state of stars. Conventional asteroseismic techniques have been largely confined to power spectral analysis, neglecting the valuable phase information contained within light curves. While recent machine learning applications in asteroseismology utilizing Convolutional Neural Networks (CNNs) have successfully inferred stellar attributes from light curves, they are often limited by the local feature extraction inherent in convolutional operations. To circumvent these constraints, we present $\textit{Astroconformer}$, a Transformer-based deep learning framework designed to capture long-range dependencies in stellar light curves. Our empirical analysis, which focuses on estimating surface gravity ($\log g$), is grounded in a carefully curated dataset derived from $\textit{Kepler}$ light curves. These light curves feature asteroseismic $\log g$ values spanning from 0.2 to 4.4. Our results underscore that, in the regime where the training data is abundant, $\textit{Astroconformer}$ attains a root-mean-square-error (RMSE) of 0.017 dex around $\log g \approx 3$. Even in regions where training data are sparse, the RMSE can reach 0.1 dex. It outperforms not only the K-nearest neighbor-based model ($\textit{The SWAN}$) but also state-of-the-art CNNs. Ablation studies confirm that the efficacy of the models in this particular task is strongly influenced by the size of their receptive fields, with larger receptive fields correlating with enhanced performance. Moreover, we find that the attention mechanisms within $\textit{Astroconformer}$ are well-aligned with the inherent characteristics of stellar oscillations and granulation present in the light curves.Comment: 13 pages, 9 figures, Submitted to MNRA

Single-shot, full characterization of the spatial wavefunction of light fields via Stokes tomography

Since the diffraction behavior of a light field is fully determined by its spatial wavefunction, i.e., its spatial complex amplitude (SCA), full characterization of spatial wavefunction, plays a vital role in modern optics from both the fundamental and applied aspects. In this work, we present a novel complex-amplitude profiler based on spatial Stokes tomography with the capability to fully determine the SCA of a light field in a single shot with high precision and resolution. The SCA slice observed at any propagation plane provides complete information about the light field, thus allowing us to further retrieve the complete beam structure in 3 dimensions space, as well as the exact modal constitution in terms of spatial degrees of freedom. The principle demonstrated here provides an important advancement for the full characterization of light beams with a broad spectrum of potential applications in various areas of optics, especially for the growing field of structured light

Cluster-induced aggregation in polyurethane derivatives with multicolour emission and ultra-long organic room temperature phosphorescence

Non-conjugated luminescent polymers (NCLPs) have the advantages of simple synthesis, optical tunability, and excellent processability. However, the underlying luminous mechanism in NCLPs remains obscure and it is a considerable challenge to obtain NCLPs with ultra-long phosphorescence lifetime and multicolour emission simultaneously. In this article, linear polyurethane derivatives (PUs) with cluster-induced aggregation, multicolour luminescence and ultra-long phosphorescence have been prepared by simply adjusting the reaction temperature and the reaction time. DFT calculations and molecular dynamics simulations provide elaborate microstructural information on the PUs. With the synergistic effect of abundant hydrogen bonding interactions, through-space dative bonds, short interatomic contacts and oxygen clusters various luminous clusters are formed. The energy level splitting caused by clusters with different extents of spatial conjugation endows the NCLPs with multicolour clusteroluminescence, promotes intersystem crossing (ISC), and stabilises the triplet excited state, and finally an ultra-long room temperature phosphorescence (RTP) lifetime of 0.45 s is attained. Experimental encryption/decryption models validate the potential of the PUs in information security. The results have important implications for understanding the intrinsic mechanism of unconventional luminescence in the absence of any traditional conjugative units or heavy atom effects, and they provide a new horizon for the strategic design of multicolour luminescence and ultra-long phosphorescence in NCLPs for a range of practical applications

Chemokine receptor CXCR3 is important for lung tissue damage and airway remodeling induced by short-term exposure to cigarette smoking in mice

Aim: To investigate the role of chemokine receptor CXCR3 in cigarette smoking (CS)-induced pulmonary damage. Methods: CXCR3 knockout (CXCR3-/-) mice were used. Differences in airspace enlargement, mRNA expression of matrix metalloproteinases (MMPs), transforming growth factor (TGF) β1, CXCL10 in lung homogenates, and CXCL10 content in bronchoalveolar lavage (BAL) fluids and homogenates were compared between CXCR3-/- mice and wild-type (WT) mice three days after three-day CS exposures. Results: The linear intercept was significantly less in CXCR3-/- mice than in WT mice (30.1±0.9 μm vs 40.3±2.4 μm, P<0.01). Morphologically, collagen was deposited less around airways and vessels in CXCR3-/- mice. The lung hydroxyproline content was significantly lower in CXCR3-/- mice than in WT mice (6.0±1.0 μg/mL vs 12.0±1.6 μg/mL, P<0.05). Profoundly lower mRNA expression of MMP2, MMP12, TGFβ1, and CXCL10 was seen in lung homogenates from CXCR3-/- mice. CXCL10 concentrations in BAL fluid and lung homogenates were significantly lower in CXCR3-/- mice than in WT mice (BAL fluid: 19.3±1.4 pg/mL vs 24.8±1.6 pg/mL, P<0.05; lung homogenates: 76.6±7.0 pg/mL vs 119.5±15.9 pg/mL, P<0.05). Conclusion: CXCR3 is important in mediating lung tissue damage and airway remodeling following a short-term CS insult, possibly through up-regulation of CXCL10 and inducement of mRNA expression of MMPs. Targeting CXCR3 may be helpful for prevention of CS-induced pulmonary pathology