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Feiyu Quan

Publications and source records attributed to Feiyu Quan.

3 recordsLinked to original sources

Projection Is All You Need: Interpreting Polarization Measurements in the Orion Clouds with Sub-Alfv\'enic MHD Simulations

Dust polarization observations are widely used to diagnose the relative importance of magnetic fields and turbulence in star forming molecular clouds, often through summary statistics such as the mean polarization direction $\mu$ and dispersion $\sigma$. Recent multi-scale polarization observations of the Orion Integral-Shaped Filament (ISF) reveal substantial diversity in polarization morphology among its dense cores, raising questions about the underlying Alfv\'enic nature of the cloud. In this work, we develop a statistical framework to compare polarization-based summary statistics from observations with those derived from projected three dimensional MHD simulations, explicitly accounting for projection effects. Using globally sub-Alfv\'enic simulations that naturally produce slightly super-Alfv\'enic dense cores, we show that modest deviations of core-scale magnetic fields from the parent cloud field, when combined with projection, can generate a wide range of plane-of-sky polarization dispersions. Applying hypothesis testing, we find that the observed $(\mu, \sigma)$ values in the Orion ISF are statistically consistent with sub-Alfv\'enic cloud models over a broad range of viewing angles. This broad degeneracy implies that $\mu$ and $\sigma$ alone cannot provide precise information about the three-dimensional magnetic-field distribution, and hence the Alfv\'en Mach number, of an individual cloud. While the observations can provide evidence against certain projection geometries, we demonstrate that polarization statistics based solely on $(\mu, \sigma)$ are insufficient to provide evidence against sub-Alfv\'enic cloud models. Our results highlight the necessity of explicitly incorporating projection effects when interpreting polarization observations of molecular clouds.

astro-ph.GA

The Green Bank Ammonia Survey: Data Release 2

We present an overview of the final data release (DR2) from the Green Bank Ammonia Survey (GAS). GAS is a Large Program at the Green Bank Telescope to map all Gould Belt star-forming regions with $A_\mathrm{V} \gtrsim 7$~mag visible from the northern hemisphere in emission from NH$_3$ and other key molecular tracers. This final release includes the data for all the regions observed: Heiles Cloud 2 and B18 in Taurus; Barnard 1, Barnard 1-E, IC348, NGC 1333, L1448, L1451, and Per7/34 in Perseus; L1688 and L1689 in Ophiuchus; Orion A (North and South) and Orion B in Orion; Cepheus, B59 in Pipe; Corona Australis (CrA) East and West; IC5146; and Serpens Aquila and MWC297 in Serpens. Similar to what was presented in GAS DR1, we find that the NH$_3$ emission and dust continuum emission from Herschel correspond closely. We find that the NH$_3$ emission is generally extended beyond the typical 0.1 pc length scales of dense cores, and we find that the transition between coherent core and turbulent cloud is a common result. This shows that the regions of coherence are common throughout different star forming regions, with a substantial fraction of the high column density regions displaying subsonic non-thermal velocity dispersions. We produce maps of the gas kinematics, temperature, and NH$_3$ column densities through forward modeling of the hyperfine structure of the NH$_3$ (1,1) and (2,2) lines. We show that the NH$_3$ velocity dispersion, $\sigma_v$, and gas kinetic temperature, $T_{\rm kin}$, vary systematically between the regions included in this release, with an increase in both the mean value and spread of $\sigma_v$ and $T_{\rm kin}$ with increasing star formation activity. The data presented in this paper are publicly available via \dataset[DOI: 10.11570/24.0091]{https://doi.org/10.11570/24.0091}.

astro-ph.GA

BEAM: Brainwave Empathy Assessment Model for Early Childhood

Empathy in young children is crucial for their social and emotional development, yet predicting it remains challenging. Traditional methods often only rely on self-reports or observer-based labeling, which are susceptible to bias and fail to objectively capture the process of empathy formation. EEG offers an objective alternative; however, current approaches primarily extract static patterns, neglecting temporal dynamics. To overcome these limitations, we propose a novel deep learning framework, the Brainwave Empathy Assessment Model (BEAM), to predict empathy levels in children aged 4-6 years. BEAM leverages multi-view EEG signals to capture both cognitive and emotional dimensions of empathy. The framework comprises three key components: 1) a LaBraM-based encoder for effective spatio-temporal feature extraction, 2) a feature fusion module to integrate complementary information from multi-view signals, and 3) a contrastive learning module to enhance class separation. Validated on the CBCP dataset, BEAM outperforms state-of-the-art methods across multiple metrics, demonstrating its potential for objective empathy assessment and providing a preliminary insight into early interventions in children's prosocial development.

cs.LG