arXiv subjects

Yuxi Meng

Publications and source records attributed to Yuxi Meng.

5 recordsLinked to original sources

arXiv2025

The impact of cosmic filaments on the abundance of satellite galaxies

The impact of cosmic web environments on galaxy properties plays a critical role in understanding galaxy formation. Using the state-of-the-art cosmological simulation IllustrisTNG, we investigate how satellite galaxy abundance differs between filaments and the field, with filaments identified using the DisPerSE algorithm. When filaments are identified using galaxies as tracers, we find that, across all magnitude bins, central galaxies in filaments tend to host more satellite galaxies than their counterparts in the field, in qualitative agreement with observational results from the Sloan Digital Sky Survey. The average ratios between satellite luminosity functions in filaments and the field are $3.49$, $2.61$, and $1.90$ in the central galaxy $r$-band magnitude bins of $M_{r, {\rm cen}} \sim -22$, $-21$, and $-20$, respectively. We show that much of this excess can be attributed to the higher host halo masses of galaxies in filaments. After resampling central galaxies in both environments to match the halo mass distributions within each magnitude bin, the satellite abundance enhancement in filaments is reduced by up to $79 \%$. Additionally, the choice of tracers used to identify filaments introduces a significant bias: when filaments are identified using the dark matter density field, the environmental difference in satellite abundance is reduced by more than $70 \%$; after further resampling in both magnitude and halo mass, the difference is further suppressed by another $\sim 60$--$95 \%$. Our results highlight the importance of halo mass differences and tracer choice biases when interpreting and understanding the impact of environment on satellite galaxy properties.

astro-ph.GA↗

arXiv2025

Detection of HI filament: Pair Stacking vs. Filament Stacking

The faint 21 cm signal emitted by neutral hydrogen in cosmic filaments is expected to be detectable. However, due to its weakness, stacking techniques are required. We assessed two stacking methods--pair stacking and filament stacking--using the EAGLE and IllustrisTNG simulations. Pair stacking leverages the fact that cosmic filaments connect massive structures (i.e., knots) in the cosmic web, while filament stacking directly aggregates filaments identified from galaxy distributions. Our analysis indicates that, although pair stacking is convenient, it faces contamination from massive structures; after removing this contamination, the filament signal is significantly reduced. In contrast, HI detection via filament stacking appears more promising. The column density in filament stacking reaches $\sim 10^{16}$--$10^{17}~\mathrm{cm}^{-2}$ even when all haloes are masked, whereas pair stacking does not reach this level even without masking, and is further suppressed by several orders of magnitude once masking is applied. The effectiveness of filament stacking can be further improved with higher galaxy number density and better spatial resolution in radio intensity mapping observations. With the advent of upcoming optical and radio data, the detection of HI in cosmic filaments remains promising.

astro-ph.CO↗

arXiv2025

A widely applicable Galaxy Group finder Using Machine Learning

Galaxy groups are essential for studying the distribution of matter on a large scale in redshift surveys and for deciphering the link between galaxy traits and their associated halos. In this work, we propose a widely applicable method for identifying groups through machine learning techniques in real space taking into account the impact of redshift distortion. Our methodology involves two neural networks: one is a classification model for identifying central galaxy groups, and the other is a regression model for predicting the mass of these groups. Both models input observable galaxy traits, allowing future applicability to real survey data. Testing on simulated datasets indicates our method accurately identifies over $92\%$ of groups with $\mathrm{M}_{vir} \geq 10^{11}h^{-1}\mathrm{M}_\odot$, with $80\%$ achieving a membership completeness of at least $80\%$. The predicted group masses vary by less than 0.3 dex across different mass scales, even in the absence of a priori data. Our network adapts seamlessly to expand to sparse samples with a flux limit of $m_{r} < 14$, to high redshift samples at $z=1.08$, and to galaxy samples from the TNG300 hydrodynamical simulation without further training. Furthermore, the framework can easily adjust to real surveys by training on redshift distorted samples without needing parameter changes. Careful consideration of different observational effects in redshift space makes it promising that this method will be applicable to real galaxy surveys.

astro-ph.CO↗

arXiv2024

Estimation of line-of-sight velocities of individual galaxies using neural networks I. Modelling redshift-space distortions at large scales

We present a scheme based on artificial neural networks (ANN) to estimate the line-of-sight velocities of individual galaxies from an observed redshift-space galaxy distribution. We find an estimate of the peculiar velocity at a galaxy based on galaxy counts and barycenters in shells around it. By training the network with environmental characteristics, such as the total mass and mass center within each shell surrounding every galaxy in redshift space, our ANN model can accurately predict the line-of-sight velocity of each individual galaxy. When this velocity is used to eliminate the RSD effect, the two-point correlation function (TPCF) in real space can be recovered with an accuracy better than 1% at $s$ > 8 $h^{-1}\mathrm{Mpc}$, and 4% on all scales compared to ground truth. The real-space power spectrum can be recovered within 3% on $k$< 0.5 $\mathrm{Mpc}^{-1}h$, and less than 5% for all $k$ modes. The quadrupole moment of the TPCF or power spectrum is almost zero down to $s$ = 10 $h^{-1}\mathrm{Mpc}$ or all $k$ modes, indicating an effective correction of the spatial anisotropy caused by the RSD effect. We demonstrate that on large scales, without additional training with new data, our network is adaptable to different galaxy formation models, different cosmological models, and mock galaxy samples at high redshifts and high biases, achieving less than 10% error for scales greater than 15 $h^{-1}\mathrm{Mpc}$. As it is sensitive to large-scale densities, it does not manage to remove Fingers of God in large clusters, but works remarkably well at recovering real-space galaxy positions elsewhere. Our scheme provides a novel way to predict the peculiar velocity of individual galaxies, to eliminate the RSD effect directly in future large galaxy surveys, and to reconstruct the 3-D cosmic velocity field accurately.

astro-ph.CO↗

arXiv2024

Normalized solutions for a fractional Schrödinger-Poisson system with critical growth

In this paper, we study the fractional critical Schrödinger-Poisson system \[\begin{cases} (-Δ)^su +λϕu= αu+μ|u|^{q-2}u+|u|^{2^*_s-2}u,&~~ \mbox{in}~{\mathbb R}^3,\\ (-Δ)^tϕ=u^2,&~~ \mbox{in}~{\mathbb R}^3,\end{cases} \] having prescribed mass \[\int_{\mathbb R^3} |u|^2dx=a^2,\] where $ s, t \in (0, 1)$ satisfies $2s+2t > 3, q\in(2,2^*_s), a>0$ and $λ,μ>0$ parameters and $α\in{\mathbb R}$ is an undetermined parameter. Under the $L^2$-subcritical perturbation $q\in (2, 2+\frac{4s}{3})$, we derive the existence of multiple normalized solutions by means of the truncation technique, concentration-compactness principle and the genus theory. For the $L^2$-supercritical perturbation $q\in (2+\frac{4s}{3}, 2^*_s)$, by applying the constrain variational methods and the mountain pass theorem, we show the existence of positive normalized ground state solutions.

math.AP↗