arXiv subjects

Wang Zhenyu

Publications and source records attributed to Wang Zhenyu.

4 recordsLinked to original sources

arXiv2025

SN 2022acko: a low-luminosity SNe IIP with signs of early circumstellar interaction

We present optical-ultraviolet photometry and optical spectra for the type II supernova (SN) 2022acko. The spectroscopic observations span phases from $\sim$ 1.5 to $\sim$ 60 days after the explosion, while the light curve was observed up to $\sim$ 300 days. The V-band peak is $-15.5 \pm 0.3$ mag, suggesting that SN 2022acko is a low-luminosity SN II (LLSN). The overall observed properties of SN 2022acko are consistent with those produced by a lower mass progenitor ($\rm M_{ZAMS} \sim $9-10M$_{\odot}$). The spectra at $t=1.5$d and $t=2.5$d exhibit a broad emission feature peaking near 4600 Å(the ``ledge'' feature), which we interpret as blueshifted He II 4686 Ålines arising from the ionized ejecta. Moreover, a possible flash-ionized (FI) emission line of H$α$ (FWHM $\sim 1100\ \rm km \ s^{-1}$) was superposed on the broad emission component of H$α$ P-Cgyni profile in the $t=1.5$d spectrum. Assuming an ejecta velocity of $\rm 12000\ km\ s^{-1}$, the rapid disappearance of this narrow H$α$ emission line within two days suggests highly confined CSM within $\sim \rm 2\times10^{14}\, cm$. Assuming a spherically symmetric CSM, the mass loss rate within this radius is estimated to be $\rm \sim 5 \times 10^{-4} M_{\odot} \ year^{-1}$ based on our hybrid light curve model. The early ``ledge'' feature observed in SN 2022acko have also been observed in other SNe II, suggesting that early-phase circumstellar interaction (CSI) is more common than previously thought.

astro-ph.HE↗

arXiv2025

SA-EMO: Structure-Aligned Encoder Mixture of Operators for Generalizable Full-waveform Inversion

Full-waveform inversion (FWI) can produce high-resolution subsurface models, yet it remains inherently ill-posed, highly nonlinear, and computationally intensive. Although recent deep learning and numerical acceleration methods have improved speed and scalability, they often rely on single CNN architectures or single neural operators, which struggle to generalize in unknown or complex geological settings and are ineffective at distinguishing diverse geological types. To address these issues, we propose a Structure-Aligned Encoder-Mixture-of-Operators (SA-EMO) architecture for velocity-field inversion under unknown subsurface structures. First, a structure-aligned encoder maps high-dimensional seismic wavefields into a physically consistent latent space, thereby eliminating spatio-temporal mismatch between the waveform and velocity domains, recovering high-frequency components, and enhancing feature generalization. Then, an adaptive routing mechanism selects and fuses multiple neural-operator experts, including spectral, wavelet, multiscale, and local operators, to predict the velocity model. We systematically evaluate our approach on the OpenFWI benchmark and the Marmousi2 dataset. Results show that SA-EMO significantly outperforms traditional CNN or single-operator methods, achieving an average MAE reduction of approximately 58.443% and an improvement in boundary resolution of about 10.308%. Ablation studies further reveal that the structure-aligned encoder, the expert-fusion mechanism, and the routing module each contribute markedly to the performance gains. This work introduces a new paradigm for efficient, scalable, and physically interpretable full-waveform inversion.

cs.LG↗

arXiv2024

$Mesiri$:Mephisto Early Supernovae Ia Rapid Identifier

The early time observations of Type Ia supernovae (SNe Ia) play a crucial role in investigating and resolving longstanding questions about progenitor stars and the explosion mechanisms of these events. Colors of supernovae (SNe) in the initial days after the explosion can help differentiate between different types of SNe. However, the use of true color information to identify SNe Ia at the early-time explosion is still in its infancy. The Multi-channel Photometric Survey Telescope (Mephisto) is a photometric survey telescope equipped with three CCD cameras, capable of simultaneously imaging the same patch of sky in three bands (\emph{u, g, i} or \emph{v, r, z}), yielding real-time colors of astronomical objects. In this paper, we introduce a new time-series classification tool named Mephisto Early Supernovae Ia Rapid Identifier (\emph{\texttt{Mesiri}}), which for the first time, utilizes real-time color information to distinguish early-time SNe Ia from core-collapse supernovae (CCSNe). \emph{\texttt{Mesiri}} is based on the deep learning approach and can achieve an accuracy of $96.75\pm0.79$\%, and AUC of $98.87\pm0.53$\% in case of single epoch random observation before the peak brightness. These values reach towards perfectness if additional data points on several night observations are considered. The classification with real-time color significantly outperforms that with pseudo-color, especially at the early time, i.e., with only a few points of observations. The architecture of BiLSTM shows the best performance than the others that have been tested in this work.

astro-ph.IM↗

arXiv2019

Network2Vec Learning Node Representation Based on Space Mapping in Networks

Complex networks represented as node adjacency matrices constrains the application of machine learning and parallel algorithms. To address this limitation, network embedding (i.e., graph representation) has been intensively studied to learn a fixed-length vector for each node in an embedding space, where the node properties in the original graph are preserved. Existing methods mainly focus on learning embedding vectors to preserve nodes proximity, i.e., nodes next to each other in the graph space should also be closed in the embedding space, but do not enforce algebraic statistical properties to be shared between the embedding space and graph space. In this work, we propose a lightweight model, entitled Network2Vec, to learn network embedding on the base of semantic distance mapping between the graph space and embedding space. The model builds a bridge between the two spaces leveraging the property of group homomorphism. Experiments on different learning tasks, including node classification, link prediction, and community visualization, demonstrate the effectiveness and efficiency of the new embedding method, which improves the state-of-the-art model by 19% in node classification and 7% in link prediction tasks at most. In addition, our method is significantly faster, consuming only a fraction of the time used by some famous methods.

cs.SI↗