arXiv subjects

Tian-Xiang Mao

Publications and source records attributed to Tian-Xiang Mao.

5 recordsLinked to original sources

arXiv2026

NERV: Neural-network Enhanced Reconstruction of the UniVerse with Application to Baryon Acoustic Oscillations in the BOSS DR12 Galaxy Sample

We present the first application of neural-network-based baryon acoustic oscillation (BAO) reconstruction to real galaxy survey data, restoring the acoustic signature damped by nonlinear structure growth. {\texttt{NERV}} ({\bf N}eural-network {\bf E}nhanced {\bf R}econstruction of the Uni{\bf V}erse) augments standard reconstruction with a convolutional neural network trained on cubic $N$-body simulations, and explicitly accounts for realistic observational effects including the curved-sky geometry, the redshift-dependent selection function, and finite survey boundaries, by tessellating the survey volume into local patches. We validate the method on the \textsc{MultiDark-Patchy} mock catalogs, recovering unbiased BAO dilation parameters. Applied to the BOSS DR12 galaxy sample, NERV improves the precision of the BAO distance measurements significantly. These results establish neural reconstruction as a practical component of BAO analyses for ongoing surveys such as DESI, with the potential to substantially tighten constraints on the cosmic expansion history and the nature of dark energy.

astro-ph.CO↗

arXiv2025

Half a Million Binary Stars from the low resolution spectra of LAMOST

Binary stars are prevalent yet challenging to detect. We present a novel approach using convolutional neural networks (CNNs) to identify binary stars from low-resolution spectra obtained by the LAMOST survey. The CNN is trained on a dataset that distinguishes binaries from single main sequence stars based on their positions on the Hertzsprung-Russell diagram. Specifically, the training data labels stars with mass ratios between approximately 0.71 and 0.93 as intermediate mass ratio binaries, while excluding those beyond this range. The network achieves high accuracy with an area under the receiver operating characteristic curve of 0.949 on the test set. Its performance is further validated against known eclipsing binaries (97% detection rate) and binary stars identified by radial velocity variations (92% detection rate). Applying the trained CNN to a sample of one million main sequence stars from LAMOST DR10 and Gaia DR3 yields a catalog of 468,634 binary stars, which are mainly intermediate mass ratio binaries given the training data. This catalog includes 115 binary stars located beyond 10 kpc from the Sun and 128 cross-matched with known exoplanet hosts from the NASA Exoplanet Archive. This new catalog provides a valuable resource for future research on the properties, formation, and evolution of binary systems, particularly for statistically characterizing large populations.

astro-ph.SR↗

arXiv2021

Cosmic Tidal Reconstruction with Halo Fields

The gravitational coupling between large-scale perturbations and small-scale perturbations leads to anisotropic distortions of the small-scale matter distribution. The measured local small-scale power spectrum can thus be used to infer the large-scale matter distribution. In this paper, we present a new tidal reconstruction algorithm for reconstructing large-scale modes using the full three-dimensional tidal shear information. We apply it to simulated dark matter halo fields and the reconstructed large-scale density field correlates well with the original matter density field on large scales, improving upon the previous tidal reconstruction method which only uses two transverse shear fields. This has profound implications for recovering lost 21~cm radial modes due to foreground subtraction and constraining primordial non-Gaussianity using the multi-tracer method with future cosmological surveys.

astro-ph.CO↗

arXiv2020

Baryon acoustic oscillations reconstruction using convolutional neural networks

We propose a new scheme to reconstruct the baryon acoustic oscillations (BAO) signal, which contains key cosmological information, based on deep convolutional neural networks (CNN). Trained with almost no fine-tuning, the network can recover large-scale modes accurately in the test set: the correlation coefficient between the true and reconstructed initial conditions reaches $90\%$ at $k\leq 0.2 h\mathrm{Mpc}^{-1}$, which can lead to significant improvements of the BAO signal-to-noise ratio down to $k\simeq0.4h\mathrm{Mpc}^{-1}$. Since this new scheme is based on the configuration-space density field in sub-boxes, it is local and less affected by survey boundaries than the standard reconstruction method, as our tests confirm. We find that the network trained in one cosmology is able to reconstruct BAO peaks in the others, i.e. recovering information lost to non-linearity independent of cosmology. The accuracy of recovered BAO peak positions is far less than that caused by the difference in the cosmology models for training and testing, suggesting that different models can be distinguished efficiently in our scheme. It is very promising that Our scheme provides a different new way to extract the cosmological information from the ongoing and future large galaxy surveys.

astro-ph.CO↗

arXiv2018

Resolution of the apparent discrepancy between the number of massive subhaloes in Abell 2744 and ΛCDM

Schwinn et al. (2017) have recently compared the abundance and distribution of massive substructures identified in a gravitational lensing analysis of Abell 2744 by Jauzac et al. (2016) and N-body simulation and found no cluster in ΛCDM simulation that is similar to Abell 2744. Schwinn et al.(2017) identified the measured projected aperture masses with the actual masses associated with subhaloes in the MXXL N-body simulation. We have used the high resolution Phoenix cluster simulations to show that such an identification is incorrect: the aperture mass is dominated by mass in the body of the cluster that happens to be projected along the line-of-sight to the subhalo. This enhancement varies from factors of a few to factors of more than 100, particularly for subhaloes projected near the centre of the cluster. We calculate aperture masses for subhaloes in our simulation and compare them to the measurements for Abell 2744. We find that the data for Abell 2744 are in excellent agreement with the matched predictions from ΛCDM. We provide further predictions for aperture mass functions of subhaloes in idealized surveys with varying mass detection thresholds.

astro-ph.GA↗