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The lightcurves of asteroids are essential for determining their physical characteristics, including shape, spin, size, and surface composition. However, most asteroids are missing some of these basic physical parameters due to lack of photometric data. Although a few telescopes or surveys are specially designed for photometric lightcurve observations of asteroids, many ground-based and space-based sky surveys for hunting new exoplanets, transient events, etc., should capture numerous small Solar System objects. This will benefit the physical studies of these objects. In order to extract data of these moving objects from time-domain photometric surveys, we have developed a new method using the model tree algorithm in the field of machine learning. A dedicated module is built to automatically identify moving objects in dataset, and extract their photometric and astrometric data. As the first application of this novel method, we have analyzed data in five fields of the Yunnan-Hong Kong wide field photometric (YNHK) survey, from which 538 lightcurves of 211 asteroids are successfully extracted. Meanwhile, we also tested the method based on the data from NASA's Transiting Exoplanet Survey Satellite, and the result proves the reliability of our method. With derived lightcurves of 13 asteroids from the YNHK survey, we have determined their synodic spin periods, among which the periods of 4 asteroids are estimated for the first time. In future, we are going to apply this method to search for small objects in the outer part of the Solar System from the Chinese Space Station Telescope survey.

Measuring coverage of dark spots on cool stars is important in understanding how stellar magnetic activity scales with the rotation rate and convection zone depth. In this respect, it is crucial to infer surface magnetic patterns on G and K stars, to reveal solar-like stellar dynamos in action. Molecular bands serve as invaluable indicators of cool spots on the surfaces of stars, as they play a crucial role in enabling accurate assessments of the extent of spot coverage across the stellar surface. Therefore, more reliable surface images can be obtained considering the inversion of atomic lines with molecular bands. In this context, we simultaneously carry out Doppler imaging (DI) using atomic lines as well as Titanium Oxide (TiO) band profiles of PW And (K2 V) and also investigate chromospheric activity indicators for the first time in the literature, using the high-resolution spectra. The surface spot distribution obtained from the inversion process represents both atomic line and TiO-band profiles quite accurately. The chromospheric emission is also correlated with photospheric spot coverage, except during a possible flare event during the observations. We detect frequent flare activity, using TESS photometry. We also introduce a new open-source, Python-based DI code SpotDIPy that allows performing surface reconstructions of single stars using the maximum entropy method. We test the code by comparing surface reconstruction simulations with the extensively used DoTS code. We show that the surface brightness distribution maps reconstructed via both codes using the same simulated data are consistent with each other., Comment: Accepted for publication by the Astrophysical Journal

The degradation process of lithium-ion batteries is intricately linked to their entire lifecycle as power sources and energy storage devices, encompassing aspects such as performance delivery and cycling utilization. Consequently, the accurate and expedient estimation or prediction of the aging state of lithium-ion batteries has garnered extensive attention. Nonetheless, prevailing research predominantly concentrates on either aging estimation or prediction, neglecting the dynamic fusion of both facets. This paper proposes a hybrid model for capacity aging estimation and prediction based on deep learning, wherein salient features highly pertinent to aging are extracted from charge and discharge relaxation processes. By amalgamating historical capacity decay data, the model dynamically furnishes estimations of the present capacity and forecasts of future capacity for lithium-ion batteries. Our approach is validated against a novel dataset involving charge and discharge cycles at varying rates. Specifically, under a charging condition of 0.25C, a mean absolute percentage error (MAPE) of 0.29% is achieved. This outcome underscores the model's adeptness in harnessing relaxation processes commonly encountered in the real world and synergizing with historical capacity records within battery management systems (BMS), thereby affording estimations and prognostications of capacity decline with heightened precision., Comment: 3 figures, 1 table, 9 pages

The digitalization of energy sectors has expanded the coding responsibilities for power engineers and researchers. This research article explores the potential of leveraging Large Language Models (LLMs) to alleviate this burden. Here, we propose LLM-based frameworks for different programming tasks in power systems. For well-defined and routine tasks like the classic unit commitment (UC) problem, we deploy an end-to-end framework to systematically assesses four leading LLMs-ChatGPT 3.5, ChatGPT 4.0, Claude and Google Bard in terms of success rate, consistency, and robustness. For complex tasks with limited prior knowledge, we propose a human-in-the-loop framework to enable engineers and LLMs to collaboratively solve the problem through interactive-learning of method recommendation, problem de-composition, subtask programming and synthesis. Through a comparative study between two frameworks, we find that human-in-the-loop features like web access, problem decomposition with field knowledge and human-assisted code synthesis are essential as LLMs currently still fall short in acquiring cutting-edge and domain-specific knowledge to complete a holistic problem-solving project.

Gradients have been exploited in proposal distributions to accelerate the convergence of Markov chain Monte Carlo algorithms on discrete distributions. However, these methods require a natural differentiable extension of the target discrete distribution, which often does not exist or does not provide effective gradient guidance. In this paper, we develop a gradient-like proposal for any discrete distribution without this strong requirement. Built upon a locally-balanced proposal, our method efficiently approximates the discrete likelihood ratio via Newton's series expansion to enable a large and efficient exploration in discrete spaces. We show that our method can also be viewed as a multilinear extension, thus inheriting its desired properties. We prove that our method has a guaranteed convergence rate with or without the Metropolis-Hastings step. Furthermore, our method outperforms a number of popular alternatives in several different experiments, including the facility location problem, extractive text summarization, and image retrieval., Comment: Published at AISTATS 2023

We present a Zeeman-Doppler imaging study of two weak-line T Tauri stars TAP 4 and TAP 40, based on the high-resolution spectropolarimetric observations with ESPaDOnS at the Canada-France-Hawaii Telescope in November 2013, in the framework of the MaTYSSE large programme. We apply two Zeeman-Doppler imaging codes to the Stokes I and V profiles to reconstruct their brightness and large-scale magnetic field images. The results given by the two imaging codes are in good agreement with each other. TAP 4 shows a large polar cool spot and several intermediate-latitude warm spots on its surface, whereas TAP 40 exhibits very weak variations in its Stokes I profiles suggesting a mostly unspotted photosphere. We detect Zeeman signatures in the Stokes V profiles of both stars. The reconstructed magnetic maps reveal dominantly toroidal fields, which enclose about 60 per cent of the total magnetic energy for both of TAP 4 and TAP 40. Both stars show prominent circular ring features of the azimuthal magnetic field. We derive a solar-like surface differential rotation on TAP 4 from the tomographic modelling. The brightness image of TAP 4 is used to predict the radial velocity jitters induced by its activity. After filtering out the activity jitter, the RMS of its RVs is reduced from 1.7 km s$^{-1}$ to 0.2 km s$^{-1}$, but we do not detect any periodic signals in the filtered RVs of TAP 4, implying that it is unlikely to host a close-in exoplanet more massive than $\sim$3.5 M$_{\rm Jup}$ at 0.1 au., Comment: 10 pages, 11 figures, accepted for publication in MNRAS

We apply the variational autoencoder (VAE) to the LAMOST-K2 low-resolution spectra to detect the magnetic activity of the stars in the K2 field. After the training on the spectra of the selected inactive stars, the VAE model can efficiently generate the synthetic reference templates needed by the spectral subtraction procedure, without knowing any stellar parameters. Then we detect the peculiar spectral features, such as chromospheric emissions, strong nebular emissions and lithium absorptions, in our sample. We measure the emissions of the chromospheric activity indicators, H$\alpha$ and Ca II infrared triplet (IRT) lines, to quantify the stellar magnetic activity. The excess emissions of H$\alpha$ and Ca II IRT lines of the active stars are correlated well to the rotational periods and the amplitudes of light curves derived from the K2 photometry. We degrade the LAMOST spectra to simulate the slitless spectra of the China Space Station Telescope (CSST) and apply the VAE to the simulated data. For cool active stars, we reveal a good agreement between the equivalent widths (EWs) of H$\alpha$ line derived from the spectra with two resolutions. The result indicates the ability of identifying the magnetically active stars in the future CSST survey, which will deliver an unprecedented large database of low-resolution spectra as well as simultaneous multi-band photometry of stars., Comment: 13 pages, 19 figures, accepted for publication in MNRAS. Table 1 is available on Zenodo at https://doi.org/10.5281/zenodo.6802956 and the code can be found on GitHub at https://github.com/xylib/vae-for-spectroscopic-survey

We report the spatial beam self-cleaning in bi-tapered conventional multimode fibers (MMFs) with different tapered lengths. Through the introduction of the bi-tapered structure in MMFs, the input beam with poor beam quality from a high-power fiber laser can be converted to a centered, bell-shaped beam in a short length, due to the strengthened nonlinear modes coupling. It is found that the bi-tapered MMF with longer tapered length at the same waist diameter shows better beam self-cleaning effect and larger spectral broadening. The obtained results offer a new method to improve the beam quality of high-power laser at low cost. Besides, it may be interesting for manufacturing bi-tapered MMF-based devices to obtain the quasi-fundamental mode beam in spatiotemporal mode-locked fiber lasers.