arXiv subjects

Jiarui Sun

Publications and source records attributed to Jiarui Sun.

25 records · Page 2Linked to original sources

arXiv2024

Tele-Correlation: Calibrating Shear-Shear Correlation with Real Data

Tele-correlation refers to the correlation of galaxy shapes with large angular separations (e.g., $>100$ degrees). Since there are no astrophysical reasons causing such a correlation on cosmological scales, any detected tele-correlation could disclose systematic effects in shear-shear correlation measurement. If the shear estimators are measured on single exposures, we show that the field distortion (FD) signal associated with the galaxy position on the CCD can be retained and used in tele-correlation to help us directly calibrate the multiplicative and additive biases in shear-shear correlations. We use the DECaLS shear catalog produced by the Fourier\_Quad pipeline to demonstrate this idea. To our surprise, we find that significant multiplicative biases can arise (up to more than 10\%) due to redshift binning of the galaxies. Correction for this bias leads to about 1$σ$ increase of the best-fit value of $S_8$ from $0.760^{+0.015}_{-0.017}$ to $0.777^{+0.016}_{-0.019}$ in our tomography study.

astro-ph.CO↗

arXiv2024

The rotation curve and mass distribution of M31

To gain a better understanding of the Andromeda galaxy M31 and its role in the Local Group, measuring its mass precisely is essential. In this work, we have constructed the rotation curve of M31 out to $\sim$125 kpc using 13,679 M31 objects obtained from various sources, including the LAMOST data release 9 (LAMOST DR9), the DESI survey, and relevant literature. We divide all objects in our sample into bulge, disk and halo components. For the sources in the M31 disk, we have measured their circular velocities by a kinematic model with asymmetric drift corrections. For the bulge and halo objects, we calculate their velocity dispersions and use the spherical and projected Jeans equation to obtain the circular velocities. Our findings indicate a nearly isotropic nature for the M31 bulge, while the halo exhibits tangential anisotropy. The results show that the rotation curve remains constant at $\sim$220 km s$^{-1}$ up to radius $\sim$25 kpc and gradually decreases to $\sim$170 km s$^{-1}$ further out. Based on the newly determined rotation curve, we have constructed a mass distribution model for M31. Our measurement of the M31 virial mass is $M_{\rm vir} = 1.14^{+0.51}_{-0.35} \times 10^{12} M_\odot$ within $r_{\rm vir} = 220 \pm 25$ kpc.

astro-ph.GA↗

arXiv2023

Towards Accurate Human Motion Prediction via Iterative Refinement

Human motion prediction aims to forecast an upcoming pose sequence given a past human motion trajectory. To address the problem, in this work we propose FreqMRN, a human motion prediction framework that takes into account both the kinematic structure of the human body and the temporal smoothness nature of motion. Specifically, FreqMRN first generates a fixed-size motion history summary using a motion attention module, which helps avoid inaccurate motion predictions due to excessively long motion inputs. Then, supervised by the proposed spatial-temporal-aware, velocity-aware and global-smoothness-aware losses, FreqMRN iteratively refines the predicted motion though the proposed motion refinement module, which converts motion representations back and forth between pose space and frequency space. We evaluate FreqMRN on several standard benchmark datasets, including Human3.6M, AMASS and 3DPW. Experimental results demonstrate that FreqMRN outperforms previous methods by large margins for both short-term and long-term predictions, while demonstrating superior robustness.

cs.CV↗

arXiv2023

Binary microlensing with plasma environment -- Star and planet

Galactic microlensing has been widely used to study the star and planet. The stellar wind plays an important role in the formation, environment and habitability of the planet. In this work we study a binary microlensing system including the stellar wind, i.e. a star with plasma environment plus a planet. Plasma surrounding the main lens causes chromatic deflection of the light rays, in addition to the gravitational one. As a result, such a lensing system can generate complicated caustics which depends on the different lensing parameters. In this work we study the magnification curves for different traces of the background source and compare the transitions of the formation of ``hill and hole'' in the magnification curves. We find that the plasma will cause extra caustic, shrink the central caustics generated by the star and push the caustic by the planet outwards. Observations and modelling of binary microlensing curves with taking plasma effect into account can provide a potential method to study plasma environment of the stars. In case of a high plasma density of the stellar wind, the plasma lensing effects will be observable in the sub-mm band.

astro-ph.SR↗

arXiv2022

Dynamic Graph Node Classification via Time Augmentation

Node classification for graph-structured data aims to classify nodes whose labels are unknown. While studies on static graphs are prevalent, few studies have focused on dynamic graph node classification. Node classification on dynamic graphs is challenging for two reasons. First, the model needs to capture both structural and temporal information, particularly on dynamic graphs with a long history and require large receptive fields. Second, model scalability becomes a significant concern as the size of the dynamic graph increases. To address these problems, we propose the Time Augmented Dynamic Graph Neural Network (TADGNN) framework. TADGNN consists of two modules: 1) a time augmentation module that captures the temporal evolution of nodes across time structurally, creating a time-augmented spatio-temporal graph, and 2) an information propagation module that learns the dynamic representations for each node across time using the constructed time-augmented graph. We perform node classification experiments on four dynamic graph benchmarks. Experimental results demonstrate that TADGNN framework outperforms several static and dynamic state-of-the-art (SOTA) GNN models while demonstrating superior scalability. We also conduct theoretical and empirical analyses to validate the efficiency of the proposed method. Our code is available at https://sites.google.com/view/tadgnn.

cs.LG↗

arXiv2020

Elastica: A compliant mechanics environment for soft robotic control

Soft robots are notoriously hard to control. This is partly due to the scarcity of models able to capture their complex continuum mechanics, resulting in a lack of control methodologies that take full advantage of body compliance. Currently available simulation methods are either too computational demanding or overly simplistic in their physical assumptions, leading to a paucity of available simulation resources for developing such control schemes. To address this, we introduce Elastica, a free, open-source simulation environment for soft, slender rods that can bend, twist, shear and stretch. We demonstrate how Elastica can be coupled with five state-of-the-art reinforcement learning algorithms to successfully control a soft, compliant robotic arm and complete increasingly challenging tasks.

cs.RO↗

arXiv2018

Globally Variance-Constrained Sparse Representation and Its Application in Image Set Coding

Sparse representation leads to an efficient way to approximately recover a signal by the linear composition of a few bases from a learnt dictionary, based on which various successful applications have been achieved. However, in the scenario of data compression, its efficiency and popularity are hindered. It is because of the fact that encoding sparsely distributed coefficients may consume more bits for representing the index of nonzero coefficients. Therefore, introducing an accurate rate-constraint in sparse coding and dictionary learning becomes meaningful, which has not been fully exploited in the context of sparse representation. According to the Shannon entropy inequality, the variance of a Gaussian distributed data bounds its entropy, indicating the actual bitrate can be well estimated by its variance. Hence, a Globally Variance-Constrained Sparse Representation (GVCSR) model is proposed in this work, where a variance-constrained rate term is introduced to the optimization process. Specifically, we employ the Alternating Direction Method of Multipliers (ADMM) to solve the non-convex optimization problem for sparse coding and dictionary learning, both of them have shown the state-of-the-art rate-distortion performance for image representation. Furthermore, we investigate the potential of applying the GVCSR algorithm in the practical image set compression, where the optimized dictionary is trained to efficiently represent the images captured in similar scenarios by implicitly utilizing inter-image correlations. Experimental results have demonstrated superior rate-distortion performance against the state-of-the-art methods.

cs.CV↗