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Liangliang Ren

Publications and source records attributed to Liangliang Ren.

15 recordsLinked to original sources
arXiv2026

A Systematic Gaia--ZTF Search for Short-Period Blue Compact-Binary Candidates

We present a catalog of 147 short-period (10.34--106.46~min) blue compact-binary candidates, identified by combining Gaia DR3 astrometry and photometry with ZTF DR23 light curves via a Gaia selection, period searches, and machine-learning morphology ranking. Of these, 111 lack prior compact-binary classifications. Multiwavelength data (DESI DR1, GALEX, AllWISE) reveal a heterogeneous sample: on the Gaia colour--magnitude diagram, 52 sources lie on the white-dwarf locus, 69 in the hot-subdwarf region, and 26 are intermediate. Among 26 sources with DESI spectra, only about one third follow the white-dwarf cooling sequence; the rest are more luminous blue stars with white-dwarf-like low-resolution spectra. We highlight a prioritized subset of new white-dwarf-locus candidates for follow-up, including ten with periods below 40~min and none with existing radial-velocity data. Under fiducial binary assumptions, 17 of these newly identified white-dwarf-locus candidates would exceed the adopted LISA signal-to-noise threshold (led by a 37~pc white dwarf), with the count depending on chirp mass (9 for $0.15\,M_\odot$, 17 for $0.3\,M_\odot$, 21 for $0.6\,M_\odot$), assuming orbital modulation. However, for most of the white-dwarf-locus sample, observed modulation amplitudes exceed any plausible ellipsoidal signal by three to five orders of magnitude, implying that rotating magnetic or chemically inhomogeneous single white dwarfs offer a viable alternative that ZTF photometry alone cannot rule out---the catalog includes at least one confirmed case. We release the full 147-source catalog, including periods, Gaia/spectroscopic classifications, harmonic/ellipsoidal diagnostics, and supplementary tables of fiducial GW estimates and UV--IR photometry.

astro-ph.SR↗
arXiv2026

Comparative Periodogram Analysis of 22 Years of Super-Kamiokande Solar $^{8}\mathrm{B}$ Neutrino Data: Classical, Phase-Based, and Information Theoretic Methods

Solar $^8\mathrm{B}$ neutrinos offer a unique probe of solar interior dynamics and neutrino electromagnetic properties. We present a systematic, multi-method periodogram analysis of the 22-year Super-Kamiokande solar neutrino dataset (1996--2018), comparing nine algorithms. Through hierarchical temporal segmentation, we disentangle astrophysical signals from detector systematics. The Generalized Lomb-Scargle (GLS) method provides the most statistically robust detections by correctly handling heteroscedastic uncertainties, whereas classical Lomb-Scargle systematically underestimates significance. The Lafler--Kinman method generally fails, whereas independent algorithms like MHAOV and PDM1 recover consistent periodicities, providing vital cross-validation. In pre-2001 and SK-I data, seven algorithms provide \textit{weak evidence} ($\ln B > 0$) for a $\sim 38.8$ d periodicity. However, this signal is entirely absent in the highest-statistics SK-IV modified flux data, where the Bayes factor decisively favors the null model ($\ln B \ll -5$), indicating it is a transient feature of the early low-statistics era. Conversely, a $\sim 24.3$ d signal in post-2001 raw flux is decisively rejected by the Bayesian framework and vanishes in modified flux, confirming its seasonal systematic origin. Furthermore, no evidence is found for an $\sim 11$-year solar cycle modulation, yielding a stringent amplitude upper limit of $<0.2\%$ of the mean flux. By highlighting the stark contrast between frequentist significance and Bayesian model selection ($\ln B$) in low signal-to-noise regimes, we establish a rigorous, multi-metric best-practice framework for periodicity searches. This work provides a direct methodological blueprint for next-generation observatories like Hyper-Kamiokande and JUNO.

astro-ph.HE↗
arXiv2026

Testing Gravitational-Wave Signal From Verification Binaries with Space-Based Gravitational-Wave Detectors

Space-based gravitational wave (GW) detectors will open the millihertz band to survey ultra-compact binaries (UCBs). \textit{Verification binaries} (VBs) is a key to verifying the performance of space-based GW detectors because its parameters are known from electromagnetic observations and it is expected to be a detectable source of GW. We evaluated 73 VBs, computing their detection prospects and parameter estimation precision for individual GW detectors and networks. Among single detectors, DECIGO shows the highest sensitivity, detecting 71 sources at signal-to-noise ratio $ρ$ $\geq$ 5, compared to 42 for LISA, 32 for Taiji, and 27 for TianQin, while the full TianQin + LISA + Taiji + DECIGO network improves this to 73 detectable sources. For parameter estimation, individual detectors achieve median precisions on the order of $\sim 10^{-2}-10^{-1} \, \text{M}_{\odot}$ for chirp mass, $\sim 1\,\text{kpc}$ for distance, $\sim 1-17\,\text{deg}$ for inclination and $\sim 10^{-4}-10^{-2}\,\text{deg}^2$ for sky localization. The complete TianQin + LISA + Taiji + DECIGO network enhances these constraints substantially, reducing the median uncertainties to approximately $\sim 10^{-2} \, \text{M}_{\odot}$ in chirp mass, $\sim 10^{-2}\,\text{kpc}$ in distance, $\sim 1\,\text{deg}$ in inclination and $\sim 10^{-4}\,\text{deg}^2$ in sky localization. The upcoming space-based GW detectors, especially their networks, have outstanding observational capabilities for UCB, which will advance our research on multi-messenger astronomy and deepen our understanding of UCB in the Milky Way.

gr-qc↗
arXiv2026

Sim-to-Real Dynamic Object Manipulation on Conveyor Systems via Optimization Path Shaping

Realizing generalizable dynamic object manipulation on conveyor systems is important for enhancing manufacturing efficiency, as it eliminates specialized engineering for different scenarios. To this end, imitation learning emerges as a promising paradigm, leveraging expert demonstrations to teach a policy manipulation skills. Although the generalization of an imitation learning policy can be improved by increasing demonstrations, demonstration collection is labor-intensive. Besides, public dynamic object manipulation data is scarce. In this work, we address this data scarcity problem via generating demonstrations in a simulator. A significant challenge of using simulated data lies in the appearance gap between simulated and real-world observations. To tackle this challenge, we propose Geometry-Enhanced Model (GEM), which employs our designed appearance noise annealing strategy to shape the policy optimization path, thereby prioritizing the geometry information in observations. Extensive experiments in simulated and real-world tasks demonstrate that GEM can generalize across environment backgrounds, robot embodiments, motion dynamics, and object geometries. Notably, GEM is deployed in a real canteen for tableware collection. Without test-scene data, GEM achieves a success rate of over 97% across more than 10,000 operations.

cs.RO↗
arXiv2025

SAM2Grasp: Resolve Multi-modal Grasping via Prompt-conditioned Temporal Action Prediction

Imitation learning for robotic grasping is often plagued by the multimodal problem: when a scene contains multiple valid targets, demonstrations of grasping different objects create conflicting training signals. Standard imitation learning policies fail by averaging these distinct actions into a single, invalid action. In this paper, we introduce SAM2Grasp, a novel framework that resolves this issue by reformulating the task as a uni-modal, prompt-conditioned prediction problem. Our method leverages the frozen SAM2 model to use its powerful visual temporal tracking capability and introduces a lightweight, trainable action head that operates in parallel with its native segmentation head. This design allows for training only the small action head on pre-computed temporal-visual features from SAM2. During inference, an initial prompt, such as a bounding box provided by an upstream object detection model, designates the specific object to be grasped. This prompt conditions the action head to predict a unique, unambiguous grasp trajectory for that object alone. In all subsequent video frames, SAM2's built-in temporal tracking capability automatically maintains stable tracking of the selected object, enabling our model to continuously predict the grasp trajectory from the video stream without further external guidance. This temporal-prompted approach effectively eliminates ambiguity from the visuomotor policy. We demonstrate through extensive experiments that SAM2Grasp achieves state-of-the-art performance in cluttered, multi-object grasping tasks.

cs.RO↗
arXiv2025

A magnetic white dwarf formed through a binary merger within 35 million years

White dwarfs (WDs) represent the final evolutionary stage of most stars, typically originating from progenitor stars with masses below approximately 8 $M_{\odot}$ to 10 $M_{\odot}$. Formation through single-star evolution generally requires at least 25 Myr, with the youngest WDs often near the Chandrasekhar limit of 1.4 $M_{\odot}$. In contrast, WDs formed via binary channels, such as mergers or mass transfer, can develop smaller masses in a shorter timescale and may exhibit unique characteristics, including strong surface magnetic fields and rapid rotation. Accurately determining the ages of these WDs is essential for understanding their formation. A valuable method involves studying WDs in star clusters, where member stars share the same age and chemical composition, allowing for precise constraints on the formation times and metallicities of the WDs' progenitors. Here we report a WD found in the open cluster RSG 5, which is only 35 Myr old. The WD's mass is lower than 1.05 $M_{\odot}$, indicating it may not have formed through single-star evolution. The WD possesses an exceptionally strong surface magnetic field ($\ge 200$ MG), a short rotational period ($\sim 6.5$ min), and, most notably, a co-rotating half-ring of ionized circumstellar debris. This distinctive feature provides evidence for a binary merger origin, a scenario further substantiated by our stellar evolution models.

astro-ph.SR↗
arXiv2025

Bootstrapping Imitation Learning for Long-horizon Manipulation via Hierarchical Data Collection Space

Imitation learning (IL) with human demonstrations is a promising method for robotic manipulation tasks. While minimal demonstrations enable robotic action execution, achieving high success rates and generalization requires high cost, e.g., continuously adding data or incrementally conducting human-in-loop processes with complex hardware/software systems. In this paper, we rethink the state/action space of the data collection pipeline as well as the underlying factors responsible for the prediction of non-robust actions. To this end, we introduce a Hierarchical Data Collection Space (HD-Space) for robotic imitation learning, a simple data collection scheme, endowing the model to train with proactive and high-quality data. Specifically, We segment the fine manipulation task into multiple key atomic tasks from a high-level perspective and design atomic state/action spaces for human demonstrations, aiming to generate robust IL data. We conduct empirical evaluations across two simulated and five real-world long-horizon manipulation tasks and demonstrate that IL policy training with HD-Space-based data can achieve significantly enhanced policy performance. HD-Space allows the use of a small amount of demonstration data to train a more powerful policy, particularly for long-horizon manipulation tasks. We aim for HD-Space to offer insights into optimizing data quality and guiding data scaling. project page: https://hd-space-robotics.github.io.

cs.RO↗
arXiv2025

Discovery and characterization of ZTF J0112+5827: An 80.9-minute polar with strong cyclotron features

We report the discovery and characterization of ZTF J0112+5827, a new magnetic cataclysmic variable with an orbital period of 80.9 minutes. ROSAT observations revealed X-ray emission with an average flux of $(68.4 \pm 15.7) \times 10^{-14}$ erg s$^{-1}$ cm$^{-2}$ (0.1--2.4 keV). The ZTF light curves show ellipsoidal-like variability in the $g$ band and two prominent humps at phases $\sim$0.0 and $\sim$0.7 in $i$ and $r$ bands. Spectroscopic observations with the Palomar 200-inch telescope revealed cyclotron emission features and strong He II and Balmer emission lines. Doppler tomography shows clear accretion streams with line-of-sight velocities of $\sim$500 km s$^{-1}$, but no accretion disk. Analysis of cyclotron harmonics indicates a magnetic field strength of $38.7^{+1.3}_{-1.1}$ MG, confirming ZTF J0112+5827 as a polar system containing a strongly magnetic white dwarf.

astro-ph.SR↗
arXiv2025

VIP: Vision Instructed Pre-training for Robotic Manipulation

The effectiveness of scaling up training data in robotic manipulation is still limited. A primary challenge in manipulation is the tasks are diverse, and the trained policy would be confused if the task targets are not specified clearly. Existing works primarily rely on text instruction to describe targets. However, we reveal that current robotic data cannot train policies to understand text instruction effectively, and vision is much more comprehensible. Therefore, we introduce utilizing vision instruction to specify targets. A straightforward implementation is training a policy to predict the intermediate actions linking the current observation and a future image. Nevertheless, a single future image does not describe the task target in insufficient detail. To handle this problem, we propose to use sparse point flows to provide more detailed information. Extensive tasks are designed based on real and simulated environments to evaluate the effectiveness of our vision instructed pre-training (VIP) method. The results indicate VIP improves the performance on diverse tasks significantly, and the derived policy can complete competitive tasks like ``opening the lid of a tightly sealed bottle''.

cs.RO↗
arXiv2024

A systematic search for redback and black widow candidates based on the 4FGL-DR3 unassociated sources and the Zwicky Transient Facility data

Spider pulsars constitute a distinct subset within the domain of radio millisecond pulsars, divided further into the categories of black widows and redbacks. Evident across multiple wavelengths, these pulsars manifest periodic variations and reside within binary systems. Investigating and discovering additional spider-type pulsars carries significant implications for comprehending the evolution of high-mass stars. Particularly crucial is the validation of the "Recycling" theory of millisecond pulsar genesis. In this investigation, we systematically explore spider pulsar binary systems utilizing time-domain variability data from the Zwicky Transient Facility, in conjunction with Fermi unassociated gamma-ray sources sourced from the 4FGL-DR3 catalog. We have implemented a time-domain data processing pipeline utilizing the Lomb-Scargle Periodogram algorithm, integrated with the wget data crawling technology. This approach has led to the identification of 194 ellipsoidal variables and irradiation-type binary stars. Subsequent refinement through the Gaia Hertzsprung-Russell diagram has culled a selection of 24 spider pulsar gold sample candidates. By incorporating the 4FGL 95\% confidence error ellipse, the pool was narrowed down to 19 gold sample candidates. Utilizing the Gaia color-reduced proper motion diagram further refined the selection to 9 gold sample candidates. These newly identified spider pulsar candidates will inform subsequent observational campaigns across radio, X-ray, and optical spectroscopy, thereby facilitating a deeper validation of their physical characteristics.

astro-ph.HE↗
arXiv2024

A new code for low-resolution spectral identification of white dwarf binary candidates

Close white dwarf binaries (CWDBs) are considered to be progenitors of several exotic astronomical phenomena (e.g., type Ia supernovae, cataclysmic variables). These violent events are broadly used in studies of general relativity and cosmology. However, obtaining precise stellar parameter measurements for both components of CWDBs is a challenging task given their low luminosities, swift time variation, and complex orbits. High-resolution spectra (R$> 20 000$) are preferred but expensive, resulting in a sample size that is insufficient for robust population study. To release the full potential of the less expensive low-resolution spectroscopic surveys, and thus greatly expand the CWDB sample size, it is necessary to develop a robust pipeline for spectra decomposition and analysis. We used an artificial neural network (ANN) to build spectrum generators for DA/DB white dwarfs and main-sequence stars. The best-fit stellar parameters were obtained by finding the least $χ^2$ solution to these feature lines and the continuum simultaneously. We demonstrate the reliability of our code with two well-studied CWDBs, WD 1534+503 and PG 1224+309. We also estimate the stellar parameters of 14 newly identified CWDB candidates, most of which are fitted with double component models for the first time. Our estimates agree with previous results for the common stars and follow the statistical distribution in the literature. The application of our code to a large volume of white dwarf binary candidates will offer important statistic samples to stellar evolution studies and future gravitational wave monitoring.

astro-ph.IM↗
arXiv2023

Detecting anisotropies of the stochastic gravitational wave background with TianQin

The investigation of the anisotropy of the stochastic gravitational wave background (SGWB) using the TianQin detector plays a crucial role in studying the early universe and astrophysics. In this work, we examine the response of the $AET$ channel of the TianQin Time Delay Interferometry (TDI) to the anisotropy of the SGWB. We calculate the corresponding angular sensitivity curves and find that TianQin is capable of detecting the anisotropy of the SGWB, with an angular sensitivity reaching $10^{-10}$ for quadrupoles. Due to the fixed $z$-axis of TianQin pointing towards J0806, its overlap reduction functions (ORFs) exhibit specific symmetries, enabling the resolution of different multipole moments $\ell m$. The detection sensitivity is optimal for the $(2, 0)$ mode, with a sensitivity reaching $10^{-10}$. Using the Fisher matrix approach, we estimate the parameters and find that in the power-law spectrum model, higher logarithmic amplitudes lead to more effective reconstruction of the spectral index for all multipole moments. Under the optimal scenario with a signal amplitude of $Ω_{\mathrm{GW}} (f = f_{\mathrm{c}}) h^2 = 10^{-9}$, the spectral indices can be reconstructed with uncertainties of $10^{-3}$, $10$, and $10^{-3}$ for $\ell = 0$, $1$, and $2$ multipole moments, respectively. For the cases of $(\ell, m) = (0, 0)$, $(1, 1)$, $(2, 0)$, and $(2, 2)$, the spectral indices can be reconstructed with uncertainties of $10^{-3}$, $10$, $10^{-3}$, and $10$, respectively.

gr-qc↗
arXiv2023

Implicit and Efficient Point Cloud Completion for 3D Single Object Tracking

The point cloud based 3D single object tracking has drawn increasing attention. Although many breakthroughs have been achieved, we also reveal two severe issues. By extensive analysis, we find the prediction manner of current approaches is non-robust, i.e., exposing a misalignment gap between prediction score and actually localization accuracy. Another issue is the sparse point returns will damage the feature matching procedure of the SOT task. Based on these insights, we introduce two novel modules, i.e., Adaptive Refine Prediction (ARP) and Target Knowledge Transfer (TKT), to tackle them, respectively. To this end, we first design a strong pipeline to extract discriminative features and conduct the matching with the attention mechanism. Then, ARP module is proposed to tackle the misalignment issue by aggregating all predicted candidates with valuable clues. Finally, TKT module is designed to effectively overcome incomplete point cloud due to sparse and occlusion issues. We call our overall framework PCET. By conducting extensive experiments on the KITTI and Waymo Open Dataset, our model achieves state-of-the-art performance while maintaining a lower computational cost.

cs.CV↗
arXiv2023

A Systematic Search for Short-period Close White Dwarf Binary Candidates Based on Gaia EDR3 Catalog and Zwicky Transient Facility Data

Galactic short-period close white dwarf binaries (CWDBs) are important objects for space-borne gravitational-wave (GW) detectors in the millihertz frequency bands. Due to the intrinsically low luminosity, only about 25 identified CWDBs are detectable by the Laser Interferometer Space Antenna (LISA), which are also known as verification binaries (VBs). The Gaia Early Data Release 3 (EDR3) provids a catalog containing a large number of CWDB candidates, which also includes parallax and photometry measurements. We crossmatch the Gaia EDR3 and Zwicky Transient Facility public data release 8, and apply period-finding algorithms to obtain a sample of periodic variables. The phase-folded light curves are inspected, and finally we obtain a binary sample containing 429 CWDB candidates. We further classify the samples into eclipsing binaries (including 58 HW Vir-type binaries, 65 EA-type binaries, 56 EB-type binaries, and 41 EW-type binaries) and ellipsoidal variations (209 ELL-type binaries). We discovered four ultrashort period binary candidates with unique light-curve shapes. We estimate the GW amplitude of all of our binary candidates, and calculate the corresponding signal-to-noise ratio (S/N) for TianQin and LISA. We find two (six) potential GW candidates with S/Ns greater than 5 in the nominal mission time of TianQin (LISA), which increases the total number of candidate VBs for TianQin (LISA) to 18 (31).

astro-ph.SR↗
arXiv2022

Personalized Trajectory Prediction via Distribution Discrimination

Trajectory prediction is confronted with the dilemma to capture the multi-modal nature of future dynamics with both diversity and accuracy. In this paper, we present a distribution discrimination (DisDis) method to predict personalized motion patterns by distinguishing the potential distributions. Motivated by that the motion pattern of each person is personalized due to his/her habit, our DisDis learns the latent distribution to represent different motion patterns and optimize it by the contrastive discrimination. This distribution discrimination encourages latent distributions to be more discriminative. Our method can be integrated with existing multi-modal stochastic predictive models as a plug-and-play module to learn the more discriminative latent distribution. To evaluate the latent distribution, we further propose a new metric, probability cumulative minimum distance (PCMD) curve, which cumulatively calculates the minimum distance on the sorted probabilities. Experimental results on the ETH and UCY datasets show the effectiveness of our method.

cs.CV↗