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Jifei Wang

Publications and source records attributed to Jifei Wang.

2 recordsLinked to original sources

The multiple corrugations in the Galactic disk derived from the LAMOST and Gaia survey data

Large spectroscopic and astrometric surveys have revealed complex wave-like features in the Milky Way disk, suggesting that its kinematic and chemical structures are shaped by time-dependent perturbations. Recent studies have reported oscillatory patterns in the Rg-Vphi-VR space, hinting at a possible structural transition in the outer disk. We aim to characterise the transition between the inner and outer Galactic thin disk and to investigate whether radial corrugations can provide a plausible physical interpretation of the observed features. We analysed two large stellar samples from LAMOST DR8 and Gaia DR3, combining spatial, kinematic, and chemical diagnostics. A simplified corrugation model consisting of two radial waves propagating in opposite directions was constructed and fitted to the observed VR pattern. We further validated the model using N-body simulations. Both LAMOST and Gaia samples reproduce the previously reported wave-like pattern in the Rg-Vphi-VR plane. We identify a clear transition between the inner and outer disks via the variations in rotational velocity and metallicities. The corrugation model naturally reproduces the periodic variation of VR with galactocentric radius, and the superposition of the inward and outward propagating modes gives rise to a comparable oscillatory pattern in both observations and simulations. Our modelling suggests that radial corrugations can provide a plausible interpretation of the observed kinematic signatures. The results highlight the complex, multi-perturber nature of the Galactic disk and motivate further investigation with upcoming surveys.

astro-ph.GA

Residual Switching Network for Portfolio Optimization

This paper studies deep learning methodologies for portfolio optimization in the US equities market. We present a novel residual switching network that can automatically sense changes in market regimes and switch between momentum and reversal predictors accordingly. The residual switching network architecture combines two separate residual networks (ResNets), namely a switching module that learns stock market conditions, and the main module that learns momentum and reversal predictors. We demonstrate that over-fitting noisy financial data can be controlled with stacked residual blocks and further incorporating the attention mechanism can enhance powerful predictive properties. Over the period 2008 to H12017, the residual switching network (Switching-ResNet) strategy verified superior out-of-sample performance with an average annual Sharpe ratio of 2.22, compared with an average annual Sharpe ratio of 0.81 for the ANN-based strategy and 0.69 for the linear model.

q-fin.ST