arXiv subjects

Xinyue Sheng

Publications and source records attributed to Xinyue Sheng.

10 recordsLinked to original sources

arXiv2026

ATLAS100 -- I. A volume-limited sample of supernovae and related transients within 100 Mpc

We present ATLAS100 -- a sample of 1729 supernovae and other explosive optical transients within $\sim 100$ Mpc observed by the ATLAS survey over a span of 5.75 years from 2017 September 21 to 2023 June 21. The volume-limited sample includes transients associated with galaxies with a spectroscopic redshift of $z \leq 0.025$, and spectroscopically classified transients within this redshift threshold where a host redshift was not available in existing catalogues. Our host galaxy list is constructed from aggregating all available galaxy redshift and distance catalogues. We carefully select all transients within a projected radius of 50\,kpc of these hosts. The ATLAS100 transient sample has a host galaxy redshift completeness fraction of $83$ per cent, consistent with expectations for the redshift completeness of local galaxy catalogues. Within this volume, the spectroscopic classifications are 87 per cent complete and we reclassify many ambiguous transients with joint light curve and spectroscopic considerations. Here, we release the catalogue together with compiled, binned and cleaned ATLAS photometry for all transients. We fit the light curve data to derive peak luminosity and characteristic timescales. We explore the sample characteristics, demographics and discuss completeness and purity of the sample.

astro-ph.HE↗

arXiv2026

AT2024lhc and AT2024kmq in the landscape of featureless tidal disruption events

We study AT2024kmq and AT2024lhc, two tidal disruption events (TDEs) with blue featureless spectra associated with high-mass black holes ($M_{\rm BH}\sim 10^8\,M_\odot$). Both events show optical precursors consistent with shock dissipation from stream self-intersection. Their X-ray emission is luminous ($L_{\rm X}\sim 10^{44}\,{\rm erg\,s^{-1}}$), highly variable (with minimum observed variability timescales of 1.3\,hr and 4.8\,hr for factor of $\sim3$ flux changes), long-lasting ($>1\,\rm yr$), emerging no later than the optical peak, and well characterized by power-laws with $1.7<\Gamma<3$ (where $f_\nu \propto \nu^{1-\Gamma}$). The X-ray properties and radio non-detections support a compact corona ($\lesssim 10 r_{\rm g}$) producing Comptonized X-ray emission. Using all published featureless TDEs, we find statistically significant bimodality in the distribution of their peak UV/optical blackbody luminosities and radii. We assemble a comparison TDE sample with early-time X-ray observations with eROSITA, in which we find different $M_{\rm BH}$ distributions in TDEs with different X-ray spectral evolution properties: low-mass black holes ($M_{\rm BH} \sim 10^6 M_\odot$) remain soft ($\Gamma>4$) within $t\lesssim 2$\,yr, intermediate masses ($\sim 10^7 M_\odot$) transition from soft to hard at $\sim$1 yr, while high masses ($\sim 10^8 M_\odot$) are hard ($1.5<\Gamma\lesssim 3$) from the outset. We interpret this result as evidence that the soft-to-hard state transition in TDEs occurs at the critical threshold of $\dot{M}_{\rm acc} \sim 0.03 \dot M_{\rm Edd}$ (similar to X-ray binaries), using the fact that the transition timescale predicted by simple disk theory scales with black hole mass as $t_{\rm tr}\propto M_{\rm BH}^{-3/4}$.

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arXiv2025

Attention-Based Preprocessing Framework for Improving Rare Transient Classification

With large numbers of transients discovered by current and future imaging surveys, machine learning is increasingly applied to light curve and host galaxy properties to select events for follow-up. However, finding rare types of transients remains difficult due to extreme class imbalances in training sets, and extracting features from host images is complicated by the presence of bright foreground sources, particularly if the true host is faint or distant. Here we present a data augmentation pipeline for images and light curves that mitigates these issues, and apply this to improve classification of Superluminous Supernovae Type I (SLSNe-I) and Tidal Disruption Events (TDEs) with our existing NEEDLE code. The method uses a Similarity Index to remove image artefacts, and a masking procedure that removes unrelated sources while preserving the transient and its host. This focuses classifier attention on the relevant pixels, and enables arbitrary rotations for class upsampling. We also fit observed multi-band light curves with a two-dimensional Gaussian Process and generate data-driven synthetic samples by resampling and redshifting these models, cross-matching with galaxy images in the same class to produce unique but realistic new examples for training. Models trained with the augmented dataset achieve substantially higher purity: for classifications with a confidence of 0.8 or higher, we achieve 75% (43%) purity and 75% (66%) completeness for SLSNe-I (TDEs).

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arXiv2025

MALLORN: Many Artificial LSST Lightcurves based on Observations of Real Nuclear transients

The Vera C. Rubin Observatory's 10-Year Legacy Survey of Space and Time (LSST) is expected to produce a hundredfold increase in the number of transients we observe. However, there are insufficient spectroscopic resources to follow up on all of the wealth of targets that LSST will provide. As such it is necessary to be able to prioritise objects for followup observations or inclusion in sample studies based purely on their LSST photometry. We are particularly keen to identify tidal disruption events (TDEs) with LSST. TDEs are immensely useful for determining black hole parameters and probing our understanding of accretion physics. To assist in these efforts, we present the Many Artificial LSST Lightcurves based on the Observations of Real Nuclear transients (MALLORN) data set and the corresponding classifier challenge for identifying TDEs. MALLORN comprises 10178 simulated LSST light curves, constructed from real Zwicky Transient Facility (ZTF) observations of 64 TDEs, 727 nuclear supernovae and 1407 AGN with spectroscopic labels using Gaussian process fitting, empirically-motivated spectral energy distributions from SNCosmo and the baseline from the Rubin Survey Simulator. Our novel approach can be easily adapted to simulate transients for any photometric survey using observations from another, requiring only the limiting magnitudes and an estimate of the cadence of observations. The MALLORN Astronomical Classification Challenge, launched on Kaggle on 15/10/2025, will allow competitors to test their photometric classifiers on simulated LSST data to find TDEs and improve upon their capabilities prior to the start of LSST.

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arXiv2025

TrustGLM: Evaluating the Robustness of GraphLLMs Against Prompt, Text, and Structure Attacks

Inspired by the success of large language models (LLMs), there is a significant research shift from traditional graph learning methods to LLM-based graph frameworks, formally known as GraphLLMs. GraphLLMs leverage the reasoning power of LLMs by integrating three key components: the textual attributes of input nodes, the structural information of node neighborhoods, and task-specific prompts that guide decision-making. Despite their promise, the robustness of GraphLLMs against adversarial perturbations remains largely unexplored-a critical concern for deploying these models in high-stakes scenarios. To bridge the gap, we introduce TrustGLM, a comprehensive study evaluating the vulnerability of GraphLLMs to adversarial attacks across three dimensions: text, graph structure, and prompt manipulations. We implement state-of-the-art attack algorithms from each perspective to rigorously assess model resilience. Through extensive experiments on six benchmark datasets from diverse domains, our findings reveal that GraphLLMs are highly susceptible to text attacks that merely replace a few semantically similar words in a node's textual attribute. We also find that standard graph structure attack methods can significantly degrade model performance, while random shuffling of the candidate label set in prompt templates leads to substantial performance drops. Beyond characterizing these vulnerabilities, we investigate defense techniques tailored to each attack vector through data-augmented training and adversarial training, which show promising potential to enhance the robustness of GraphLLMs. We hope that our open-sourced library will facilitate rapid, equitable evaluation and inspire further innovative research in this field.

cs.LG↗

arXiv2025

The La Silla Schmidt Southern Survey

We present the La Silla Schmidt Southern Survey (LS4), a new wide-field, time-domain survey to be conducted with the 1 m ESO Schmidt telescope. The 268 megapixel LS4 camera mosaics 32 2k$\times$4k fully depleted CCDs, providing a $\sim$20 deg$^2$ field of view with $1''$ pixel$^{-1}$ resolution. The LS4 camera will have excellent performance at longer wavelengths: in a standard 45 s exposure the expected 5$\sigma$ limiting magnitudes in $g$, $i$, $z$ are $\sim$21.5, $\sim$20.9, and $\sim$20.3 mag (AB), respectively. The telescope design requires a novel filter holder that fixes different bandpasses over each quadrant of the detector. Two quadrants will have $i$ band, while the other two will be $g$ and $z$ band and color information will be obtained by dithering targets across the different quadrants. The majority (90%) of the observing time will be used to conduct a public survey that monitors the extragalactic sky at both moderate (3 d) and high (1 d) cadence, as well as focused observations within the Galactic bulge and plane. Alerts from the public survey will be broadcast to the community via established alert brokers. LS4 will run concurrently with the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). The combination of LS4+LSST will enable detailed holistic monitoring of many nearby transients: high-cadence LS4 observations will resolve the initial rise and peak of the light curve while less-frequent but deeper observations by LSST will characterize the years before and after explosion. Here, we summarize the primary science objectives of LS4 including microlensing events in the Galaxy, extragalactic transients, the search for electromagnetic counterparts to multi-messenger events, and cosmology.

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arXiv2023

NEural Engine for Discovering Luminous Events (NEEDLE): identifying rare transient candidates in real time from host galaxy images

Known for their efficiency in analyzing large data sets, machine learning classifiers are widely used in wide-field sky surveys. The upcoming Vera C. Rubin Observatory Legacy of Time and Space Survey (LSST) will generate millions of alerts every night, enabling the discovery of large samples of rare events. Identifying such objects soon after explosion will be essential to study their evolution. This requires a machine learning framework that makes use of all available transient and contextual information. Using $\sim5400$ transients from the ZTF Bright Transient Survey as input data, we develop NEEDLE, a novel hybrid classifier to select for two rare classes with strong environmental preferences: superluminous supernovae (SLSNe) preferring dwarf galaxies, and tidal disruption events (TDEs) occurring in the centres of nucleated galaxies. The input data includes detection and reference images, photometric information from the alert packets, and host galaxy magnitudes from Pan-STARRS. Despite having only a few tens of examples of the rare classes, our average (best) completeness on an unseen test set reaches 77% (93%) for SLSNe and 72% (87%) for TDEs. This may still result in a large fraction of false positives for the rare transients, given the large class imbalance in real surveys. However, the goal of NEEDLE is to find good candidates for spectroscopic classification, rather than to select pure photometric samples. Our network is designed with LSST in mind and we expect performance to improve further with the higher resolution images and more accurate transient and host photometry that will be available from Rubin. Our system will be deployed as an annotator on the UK alert broker, Lasair, to provide predictions to the community in real time.

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arXiv2023

Stochastic Recurrent Neural Networks for Modelling Astronomical Time Series: Advantages and Limitations

This paper reviews the Stochastic Recurrent Neural Network (SRNN) as applied to the light curves of Active Galactic Nuclei by Sheng et al. (2022). Astronomical data have inherent limitations arising from telescope capabilities, cadence strategies, inevitable observing weather conditions, and current understanding of celestial objects. When applying machine learning methods, it is vital to understand the effects of data limitations on our analysis and ability to make inferences. We take Sheng et al. (2022) as a case study, and illustrate the problems and limitations encountered in implementing the SRNN for simulating AGN variability as seen by the Rubin Observatory.

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arXiv2022

The Birth of a Relativistic Jet Following the Disruption of a Star by a Cosmological Black Hole

A black hole can launch a powerful relativistic jet after it tidally disrupts a star. If this jet fortuitously aligns with our line of sight, the overall brightness is Doppler boosted by several orders of magnitude. Consequently, such on-axis relativistic tidal disruption events (TDEs) have the potential to unveil cosmological (redshift $z>$1) quiescent black holes and are ideal test beds to understand the radiative mechanisms operating in super-Eddington jets. Here, we present multi-wavelength (X-ray, UV, optical, and radio) observations of the optically discovered transient \target at $z=1.193$. Its unusual X-ray properties, including a peak observed luminosity of $\gtrsim$10$^{48}$ erg s$^{-1}$, systematic variability on timescales as short as 1000 seconds, and overall duration lasting more than 30 days in the rest-frame are traits associated with relativistic TDEs. The X-ray to radio spectral energy distributions spanning 5-50 days after discovery can be explained as synchrotron emission from a relativistic jet (radio), synchrotron self-Compton (X-rays), and thermal emission similar to that seen in low-redshift TDEs (UV/optical). Our modeling implies a beamed, highly relativistic jet akin to blazars but requires extreme matter-domination, i.e, high ratio of electron-to-magnetic field energy densities in the jet, and challenges our theoretical understanding of jets.

astro-ph.HE↗

arXiv2022

LSST Cadence Strategy Evaluations for AGN Time-series Data in Wide-Fast-Deep Field

Machine learning is a promising tool to reconstruct time-series phenomena, such as variability of active galactic nuclei (AGN), from sparsely-sampled data. Here we use three Continuous Auto-Regressive Moving Average (CARMA) representations of AGN variability -- the Damped Random Walk (DRW) and (over/under-)Damped Harmonic Oscillator (DHO) -- to simulate 10-year AGN light curves as they would appear in the upcoming Vera Rubin Observatory Legacy Survey of Space and Time (LSST), and provide a public tool to generate these for any survey cadence. We investigate the impact on AGN science of five proposed cadence strategies for LSST's primary Wide-Fast-Deep (WFD) survey. We apply for the first time in astronomy a novel Stochastic Recurrent Neural Network (SRNN) algorithm to reconstruct input light curves from the simulated LSST data, and provide a metric to evaluate how well SRNN can help recover the underlying CARMA parameters. We find that the light curve reconstruction is most sensitive to the duration of gaps between observing season, and that of the proposed cadences, those that change the balance between filters, or avoid having long gaps in the {g}-band perform better. Overall, SRNN is a promising means to reconstruct densely sampled AGN light curves and recover the long-term Structure Function of the DRW process (SF$_\infty$) reasonably well. However, we find that for all cadences, CARMA/SRNN models struggle to recover the decorrelation timescale ($τ$) due to the long gaps in survey observations. This may indicate a major limitation in using LSST WFD data for AGN variability science.

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