arXiv subjects

Huiling Chen

Publications and source records attributed to Huiling Chen.

At least 19 recordsLinked to original sources

arXiv2026

High Resolution Spectroscopic Follow-up Observation Results for 13 EMP Candidates Selected by Narrow-band Photometry

Extremely metal-poor (EMP; [Fe/H] < -3.0) stars preserve key information about the earliest stages of Galactic chemical evolution. Based on a training set of spectroscopic metallicities, we constructed catalogs of more than 120,000 EMP candidates from large-scale narrow-band photometric surveys. To validate this approach, we obtained high-resolution CFHT/ESPaDOnS spectra for 13 candidates selected from the SkyMapper-based catalog. The follow-up observation confirms that the narrow-band photometric selection is effective in identifying very metal-poor stars and retaining a substantial EMP fraction: four targets are confirmed as EMP stars, and all targets remain very metal-poor with [Fe/H] < -2.4. The photometric metallicities are systematically lower than the spectroscopic values by 0.44 dex, with a relatively small scatter of 0.19 dex, indicating a systematic offset in the photometric metallicity scale that could be reduced through improved calibration. Abundances for more than 20 chemical species are derived, leading to the identification of a new potassium-enhanced r-II star, J2001-1215. By combining chemical abundances with orbital properties, five stars are found to be dynamically consistent with known Galactic substructures, including Gaia--Sausage--Enceladus, Thamnos, and Sequoia. These stars provide high-resolution chemical measurements for the extremely metal-poor regime of these Galactic substructures. These results underscore the role of narrow-band photometry in efficiently selecting EMP candidates for targeted high-resolution spectroscopic follow-up, thereby enabling detailed chemical and dynamical studies of the early Milky Way.

astro-ph.GA↗

arXiv2026

Admission Without Answers: Label-Free Certification and Experience Learning for LLM-Based Optimization Modeling

Experience-learning agents for optimization modeling improve by storing verified skills, but existing learners admit knowledge by checking against known answers, which real ticket streams do not provide. The natural label-free alternatives are unreliable: on a 300-problem label-blind stream, admitting every executable model poisons roughly one admission in four, while single-instance agreement accepts models that match at one value but differ elsewhere. We propose AdmitOR, an admission gate built on calibrated external behavioral evidence. Candidates from three model families, prompting strategies, and solver stacks are run on instances resampled from an extracted parameter domain; agreement across the resulting value-function traces is summarized by a cross-family clique, and a calibrated threshold returns accept, abstain, or escalate. The preregistered false-discovery criterion holds on calibration data but not on the wild stream. We report this negative result in full and trace most failures to benchmark texts that do not faithfully encode their labeled instances. Comparing four admission judges on one collection of logs inside a state-of-the-art skill learner, AdmitOR raises admission precision to 0.927, against 0.871 for majority vote and 0.726 for execution success, yielding 3.1x and 8.0x fewer poisoned admissions. Its library is the smallest and attains the highest macro accuracy across five public benchmarks, 58.4 against 54.8 for majority vote and 53.9 for the ground-truth-labeled library. The 3.5-point gain over majority vote is supported by a paired bootstrap and survives correction for a host-side anomaly. To our knowledge, AdmitOR is the first label-free admission mechanism designed around an explicitly calibrated false-discovery target. The transfer failure identifies a necessary condition for extending it to wild streams.

cs.AI↗

arXiv2026

A Bayesian Search for Planet Engulfment Signatures in Solar Analogs

We present a systematic Bayesian search for chemical fingerprints of planet engulfment in 113 solar twins and analogs with high-precision abundance measurements, 45 of which host known or candidate planets or brown-dwarf companions. We constructed a Bayesian framework with three sets of abundance models: random scatter, Galactic chemical evolution, and planet engulfment with bulk Earth or CM chondrite compositions. Through model comparisons, we identified three candidates whose abundance patterns strongly favor planet engulfment over the alternatives, with inferred engulfed masses of about 7.5-33 Earth masses. Our findings correspond to a nominal detection rate of 1-3% for planet-engulfment signatures among solar analogs. This work extends abundance-based engulfment searches beyond the binary-star context and provides a framework for probing star-planet co-evolution with solar analogs, which goes beyond the commonly used abundance-condensation-temperature correlation (Tc slope).

astro-ph.EP↗

arXiv2026

Simulation-Driven Imitation Learning for Biosignals-Free Shared-Autonomy Prosthetic Grasping

Biosignals-free shared-autonomy control of upper-limb prosthetic hands aims to enable natural and low-effort manipulation without relying on EMG or other physiological signals. Recent imitation-learning-based approaches have shown promising results, but their scalability is limited by the cost and variability of collecting large amounts of real-world human demonstration data. In this work, we present a scalable simulation framework that automatically generates diverse reach-to-grasp demonstrations from a wrist-mounted virtual camera. The framework combines physically feasible grasp synthesis, natural reaching trajectories retargeting, and reach--grasp--lift execution in procedurally generated indoor environments. It records wrist-view observations, proprioception, and actions to build a large-scale demonstration dataset for imitation learning. Through extensive simulation benchmarks, we evaluate object and scene generalization and compare several representative state-of-the-art imitation learning methods. Results show that the simulated demonstrations are sufficiently rich and consistent for effective policy learning. In three realistic settings, the learned sim-to-real policy achieves over 90\% grasp success, surpasses baseline methods, and exhibits stronger generalization, highlighting the promise of simulation-driven training for biosignals-free shared-autonomy prosthetic grasping. The demonstrations are available at \href{https://sites.google.com/view/sim-prosthetic-grasp/home}{https://sites.google.com/view/sim-prosthetic-grasp/home}.

cs.RO↗

arXiv2026

A Systematic NLTE Study of Very Metal-Poor Stars with Metallicity Down to $-4.3$ dex. II. Lithium Abundance and New Insight to the Lithium Plateau

Metal-poor stars are crucially important for understanding the early Galaxy, first stars, and the Universe. In this series of papers, we present a homogeneous non-local thermodynamic equilibrium (NLTE) abundances analysis of 12 elements for 103 very/extremely metal-poor (VMP/EMP) stars with metallicity down to $-4.3$ dex. The sample was selected from the LAMOST survey and observed by the high-resolution spectroscopy of Subaru. In this paper, we present the NLTE abundances and evolution of lithium in these stars. We report different lithium behaviors corresponding to different evolutionary stages and their signatures: 1) The Spite Plateau shows a slightly positive slope, indicating increasing lithium abundance with increasing metallicity. Most significantly, it appears to extend to lower metallicities as previously suggested, calling into question the reality of the so-called 'meltdown' at low metallicity; 2) We confirm a lithium plateau for lower red giant branch (LRGB) stars with A(Li) $= 1.13$ dex in our sample, while lithium abundance drops rapidly to A(Li)$<0.5$ as stars continue to evolve to higher stage. 3) We identify four Li-rich stars in our sample across different evolutionary stages, showing complex and multiple lithium production mechanisms in VMP/EMP stars. These findings suggest that early Galactic lithium enrichment results from a complex interplay between depletion and production processes.

astro-ph.SR↗

arXiv2026

BiomedAP: A Vision-Informed Dual-Anchor Framework with Gated Cross-Modal Fusion for Robust Medical Vision-Language Adaptation

Biomedical Vision--Language Models (VLMs) have shown remarkable promise in few-shot medical diagnosis but face a critical bottleneck: \textit{fragility to prompt variations}.Existing adaptation frameworks typically optimize visual and textual prompts as independent streams, relying on ideal ``Golden Prompts''. In clinical reality, where descriptions are often noisy and heterogeneous, this modality isolation leads to unstable cross-modal alignment. To address this, we propose BiomedAP, a vision-informed dual-anchor framework with gated cross-modal fusion.BiomedAP enforces synergistic alignment through two mechanisms: (1) Gated Cross-Modal Fusion, which enables layer-wise interaction between modalities, acting as a dynamic noise regulator to suppress irrelevant textual cues; and (2) a Dual-Anchor Constraint that regularizes learnable prompts toward stable semantic centroids derived from both expert templates (High Anchors) and few-shot visual prototypes (Low Anchors). Extensive experiments across 11 benchmarks demonstrate that BiomedAP consistently surpasses baselines, achieving competitive few-shot accuracy and markedly enhanced robustness under prompt perturbations. Our code is available at: https://github.com/tongdiedie/BiomedAP. Keywords: Vision-Language Models; Prompt Learning; Parameter-Efficient Fine-Tuning; Few-shot Learning

cs.CV↗

arXiv2026

Filter Design for Estimating the Stellar Metallicity of Metal-poor Stars from Gaia XP Spectra

The estimation of stellar atmospheric parameters for large-scale samples, particularly metal-poor stars, is a cornerstone of Galactic archaeology. In this work, we optimized a photometric filter design tailored to measuring stellar metallicities for very metal-poor stars with [Fe/H]$< -1$.The optimal configurations consist of a central wavelength $\lambda_{\rm c}$ = 3960 Angstrom with a bandwidth $\Delta\lambda$ = 80 Angstrom for giant stars, and $\lambda_{\rm c} $= 3920 Angstrom with $\Delta\lambda$ = 80 Angstrom for dwarf stars. By applying these optimized filters to synthetic photometry derived from Gaia XP spectra, we inferred metallicities for both populations. Both internal and external validations demonstrate high precision across a wide metallicity range: 0.18-0.19 dex for $-2 \le \rm [Fe/H] \le -1$, 0.23-0.33 dex for $-3 \le \rm [Fe/H] \le -2$, and approximately 0.39 dex for the most metal-poor regime, successfully extending down to $\rm [Fe/H] \approx -4$ for giant stars, $\rm [Fe/H] \approx -3.3$ for dwarf stars. Finally, we present a catalog of approximately 14.5 million metal-poor stars with robust $\rm [Fe/H]$ measurements, along with more than ten thousand red giant ultra metal-poor candidates with $\rm [Fe/H] < -4.0$, providing a valuable resource for exploring the early formation and chemical evolution of the Milky Way.

astro-ph.SR↗

arXiv2026

ReLoop: Structured Modeling and Behavioral Verification for Reliable LLM-Based Optimization

Large language models (LLMs) can translate natural language into optimization code, but silent failures pose a critical risk: code that executes and returns solver-feasible solutions may encode semantically incorrect formulations---a feasibility--correctness gap reaching 90 percentage points on compositional problems. We introduce ReLoop, which addresses this gap through two complementary mechanisms. Structured generation decomposes code production into a four-stage reasoning chain (understand, formalize, synthesize, verify), preventing formulation errors at their source. Behavioral verification detects errors that survive generation by testing whether the formulation responds correctly to solver-based parameter perturbation---an external semantic signal that bypasses LLM self-review and requires no ground truth. The two mechanisms are complementary by error structure: structured generation drives the largest gains on compositional problems (+8.5pp accuracy on RetailOpt-190 with Claude Opus 4.6), while behavioral verification dominates on localized defects +4.4pp on MAMO-ComplexLP, its largest contribution across benchmarks). Combined with diagnostic execution recovery, ReLoop reaches 100% executable code on Claude Opus 4.6 and consistently improves accuracy on chat-tuned foundation models across three benchmarks; we further identify a known limitation of narrowly-tuned SFT models, whose learned output formats are brittle to chain-of-thought prompts---an interaction we document and analyze. We release RetailOpt-190, 190 compositional retail optimization scenarios targeting the multi-constraint interactions where LLMs most frequently fail.

cs.SE↗

arXiv2026

S-PLUS: Beyond Spectroscopy IV. Stellar Parameters and Elemental-abundance Ratios for Six Million Stars from DR4 and First Results for the Magellanic Clouds

We combine narrow/medium-band filter photometry from the Southern Photometric Local Universe Survey (S-PLUS) DR4 with ultra broad-band filter photometry from Gaia EDR3 to derive fundamental stellar parameters ($T_{\rm eff}$, $\log g$, [Fe/H], ages) and elemental-abundance ratios ([C/Fe] and [$\alpha$/Fe]) for 5.4 million stars in the Galaxy (4.9 million dwarfs and 0.5 million giants), as well as for over 0.7 million red giant stars in the Large and Small Magellanic Clouds (LMC and SMC). The precisions of the abundance estimates range from 0.05-0.10 dex for metallicity in the relatively metal-rich range ([Fe/H] $> -1.0$) to 0.10-0.30 dex in the metal-poor regime ([Fe/H] $<-1.0$), 0.10-0.20\,dex for [C/Fe], and 0.05 dex for [$\alpha$/Fe]. The stellar parameters for LMC and SMC member stars are somewhat less precise than those from the S-PLUS main survey, primarily because of the effect of high reddening. The use of both metallicity- and carbon-sensitive filters provides unbiased measurements of both [Fe/H] and [C/Fe], of particular importance for very low-metallicity ([Fe/H] $< -2.0$) stars, where carbon enhancement can lead to systematically high estimates of [Fe/H] when only a single metallicity-sensitive filter is employed. Furthermore, multiple narrow-band filters enable metallicity estimates down to [Fe/H] $\sim -4.0$ with an accuracy of around 0.3 dex, exceeding the precision typically achieved by low/medium-resolution spectroscopy. This extensive photometric dataset, combined with the other three datasets in this series, will serve as a valuable legacy resource for Milky Way and Magellanic Clouds studies.

astro-ph.SR↗

arXiv2026

TwistNet-2D: Learning Second-Order Channel Interactions via Spiral Twisting for Texture Recognition

Second-order feature statistics are central to texture recognition, yet existing mechanisms exhibit a structural tension: bilinear pooling and Gram matrices capture global channel correlations but discard spatial structure, whereas self-attention models capture cross-position relations through weighted sums rather than explicit pairwise products. We propose TwistNet-2D, a lightweight module that computes local pairwise channel products under directional spatial displacement, jointly encoding where features co-occur and how they interact. The core component, Spiral-Twisted Channel Interaction (STCI), shifts one feature map along a prescribed direction before L2-normalized channel multiplication, capturing cross-position co-occurrence patterns that characterize structured and periodic textures. Four directional heads are aggregated through content-adaptive channel reweighting, and the result is injected via a sigmoid-gated residual path with near-zero initialization. TwistNet-2D adds only approximately 3.5% parameters and approximately 2% FLOPs over ResNet-18. To isolate the contribution of architectural inductive bias from that of transfer learning, all models in this study are trained from scratch without ImageNet pretraining. Under this protocol, TwistNet-2D consistently surpasses parameter-matched baselines and substantially larger ConvNeXt and Swin Transformer backbones across four texture and fine-grained recognition benchmarks, while the multi-head structure produces interpretable, orientation-selective representations that align with classical texture analysis.

cs.CV↗

arXiv2025

OPAL: Operator-Programmed Algorithms for Landscape-Aware Black-Box Optimization

Black-box optimization often relies on evolutionary and swarm algorithms whose performance is highly problem dependent. We view an optimizer as a short program over a small vocabulary of search operators and learn this operator program separately for each problem instance. We instantiate this idea in Operator-Programmed Algorithms (OPAL), a landscape-aware framework for continuous black-box optimization that uses a small design budget with a standard differential evolution baseline to probe the landscape, builds a $k$-nearest neighbor graph over sampled points, and encodes this trajectory with a graph neural network. A meta-learner then maps the resulting representation to a phase-wise schedule of exploration, restart, and local search operators. On the CEC~2017 test suite, a single meta-trained OPAL policy is statistically competitive with state-of-the-art adaptive differential evolution variants and achieves significant improvements over simpler baselines under nonparametric tests. Ablation studies on CEC~2017 justify the choices for the design phase, the trajectory graph, and the operator-program representation, while the meta-components add only modest wall-clock overhead. Overall, the results indicate that operator-programmed, landscape-aware per-instance design is a practical way forward beyond ad hoc metaphor-based algorithms in black-box optimization.

cs.NE↗

arXiv2025

A Metallicity Catalog of Very Metal-poor Main-sequence Turn-off and Red Giant Stars from LAMOST DR10

We present a catalog of 8,440 candidate very metal-poor (VMP; [Fe/H] < -2.0) main-sequence turn-off (MSTO) and red giant stars in the Milky Way, identified from low-resolution spectra in LAMOST DR10. More than 7,000 of these candidates are brighter than G ~ 16, making them excellent targets for high-resolution spectroscopic follow-up with 4-10 meter-class telescopes. Unlike most previous studies, we employed an empirical calibration to estimate metallicities from the equivalent widths (EWs) of the Calcium Triplet (CaT) lines, taking advantage of the high signal-to-noise ratio (SNR) in the red arm of LAMOST spectra. We further refined this calibration to improve its reliability for more distant stars. This method enables robust identification of VMP candidates with metallicities as low as [Fe/H] = -4.0 among both MSTO and red giant stars. Comparisons with metal-poor samples from other spectroscopic surveys and high-resolution follow-up observations confirm the accuracy of our estimates, showing a typical median offset of ~0.1 dex and a standard deviation of ~0.2 dex.

astro-ph.GA↗

arXiv2025

Physics-Informed Evolution: An Evolutionary Framework for Solving Quantum Control Problems Involving the Schr\"odinger Equation

Physics-informed Neural Networks (PINNs) show that embedding physical laws directly into the learning objective can significantly enhance the efficiency and physical consistency of neural network solutions. Similar to optimizing loss functions in machine learning, evolutionary algorithms iteratively optimize objective functions by simulating natural selection processes. Inspired by this principle, we ask a natural question: can physical information be similarly embedded into the fitness function of evolutionary algorithms? In this work, we propose Physics-informed Evolution (PIE), a novel framework that incorporates physical information derived from governing physical laws into the evolutionary fitness landscape, thereby extending Physics-informed artificial intelligence methods from machine learning to the broader domain of evolutionary computation. As a concrete instantiation, we apply PIE to quantum control problems governed by the Schr\"{o}dinger equation, where the goal is to find optimal control fields that drive quantum systems from initial states to desired target states. We validate PIE on three representative quantum control benchmarks: state preparation in V-type three-level systems, entangled state generation in superconducting quantum circuits, and two-atom cavity QED systems. Within the PIE framework, we systematically compare the performance of ten single-objective and five multi-objective evolutionary algorithms. Experimental results demonstrate that by embedding physical information into the fitness function, PIE effectively guides evolutionary search, yielding control fields with high fidelity, low state deviation, and robust performance across different scenarios. Our findings further suggest that the Physics-informed principle extends naturally beyond neural network training to the broader domain of evolutionary computation.

quant-ph↗

arXiv2024

Twisted Convolutional Networks (TCNs): Enhancing Feature Interactions for Non-Spatial Data Classification

Twisted Convolutional Networks (TCNs) are proposed as a novel deep learning architecture for classifying one-dimensional data with arbitrary feature order and minimal spatial relationships. Unlike conventional Convolutional Neural Networks (CNNs) that rely on structured feature sequences, TCNs explicitly combine subsets of input features through theoretically grounded multiplicative and pairwise interaction mechanisms to create enriched representations. This feature combination strategy, formalized through polynomial feature expansions, captures high-order feature interactions that traditional convolutional approaches miss. We provide a comprehensive mathematical framework for TCNs, demonstrating how the twisted convolution operation generalizes standard convolutions while maintaining computational tractability. Through extensive experiments on five benchmark datasets from diverse domains (medical diagnostics, political science, synthetic data, chemometrics, and healthcare), we show that TCNs achieve statistically significant improvements over CNNs, Residual Networks (ResNet), Graph Neural Networks (GNNs), DeepSets, and Support Vector Machine (SVM). The performance gains are validated through statistical testing. TCNs also exhibit superior training stability and generalization capabilities, highlighting their robustness for non-spatial data classification tasks.

cs.CV↗

arXiv2024

A Derivative-Hilbert operator acting on BMOA space

Let $\mu$ be a positive Borel measure on the interval $[0,1)$. The Hankel matrix $\mathcal{H}_{\mu}=(\mu_{n,k})_{n,k\geq 0}$ with entries $\mu_{n,k}=\mu_{n+k}$, where $\mu_{n}=\int_{[0,1)}t^nd\mu(t)$, induces, formally, the Derivative-Hilbert operator $$\mathcal{DH}_\mu(f)(z)=\sum_{n=0}^\infty\left(\sum_{k=0}^\infty \mu_{n,k}a_k\right)(n+1)z^n , ~z\in \mathbb{D},$$ where $f(z)=\sum_{n=0}^\infty a_nz^n$ is an analytic function in $\mathbb{D}$. We characterize the measures $\mu$ for which $\mathcal{DH}_\mu$ is a bounded operator on $BMOA$ space. We also study the analogous problem from the $\alpha$-Bloch space $\mathcal{B}_\alpha(\alpha>0)$ into the $BMOA$ space.

math.FA↗

arXiv2024

Generalized Hilbert Operator Acting on Hardy Spaces

Let $\alpha>0$ and $\mu$ be a positive Borel measure on the interval $[0,1)$. The Hankel matrix $\mathcal{H}_{\mu,\alpha}=(\mu_{n,k,\alpha})_{n,k\ge0}$ with entries $\mu_{n,k,\alpha}=\int_{[0,1)}^{}\frac{\Gamma(n+\alpha)}{\Gamma(n+1)\Gamma(\alpha)}t^{n+k}d\mu(t)$, induces, formally, the generalized-Hilbert operator as $$ \mathcal{H}_{\mu,\alpha}\left ( f \right ) \left ( z \right ) =\sum_{n=0}^{\infty} \left (\sum_{k=0}^{\infty} \mu_{n,k,\alpha}a_k \right )z^n,z\in\mathbb{D} $$ where $f(z)={\textstyle \sum_{k=0}^{\infty }} a_kz^k$ is an analytic function in $\mathbb{D}$. This article is devoted study the measures $\mu$ for which $\mathcal{H}_{\mu,\alpha }$ is a bounded(resp., compact) operator from $H^p(0<p\le1)$ into $H^p(1\le q<\infty)$. Then, we also study the analogous problem in the Hardy spaces $H^p(1\le p\le2)$. Finally, we obtain the essential norm of $\mathcal{H}_{\mu,\alpha}$ from $H^p(0<p\le1)$ into $H^p(1\le q<\infty)$.

math.FA↗

arXiv2024

Magnetic field of the roAp star KIC~10685175: observations versus theory

KIC 10685175 is a roAp star whose polar magnetic field is predicted to be 6 kG through a non-adiabatic axisymmetric pulsation theoretical model. In this work, we aim to measure the magnetic field strength of KIC 10685175 using high-resolution spectropolarimetric observations, and compare it with the one predicted by the theoretical model. From the study of two high-resolution unpolarized spectra, we obtained [$T_{\rm eff}$, $\log g$, [Fe/H], [$\alpha$/Fe], $V_{mic}$]=[8250 $\pm$ 200\,K, 4.4 $\pm$ 0.1, -0.4 $\pm$ 0.2, 0.16 $\pm$ 0.1, 1.73 $\pm$ 0.2\,km~s$^{-1}$]. Although the Fe absorption lines appear relatively weak in comparison to typical Ap stars with similar $T_{\rm eff}$, the lines belonging to rare earth elements (Eu and Nd) are stronger than that in chemically normal stars, indicating the peculiar nature of KIC~10685175. The mean longitudinal magnetic field $\langle B_\ell \rangle=-226\pm39$\,G has been measured in the polarized spectrum, but magnetically split lines were not detected. No significant line profile variability was evident in our spectra. Also the longitudinal magnetic field strengths measured using line masks constructed for different elements have been rather similar. Due to a poor rotation phase coverage of our data, additional spectroscopic and polarimetric observations are needed to allow us to conclude on the inhomogeneous element distribution over the stellar surface. The estimated polar magnetic field is $4.8 \pm 0.8$\,kG, which is consistent with the predicted polar magnetic field strength of about 6\,kG within 3$\sigma$. This work therefore provides support for the pulsation theoretical model.

astro-ph.SR↗

arXiv2024

The Kinematic and Chemical Properties of the Close-in Planet Host Star 8 UMi

A recent study by Hon et al. reported that a close-in planet around the red clump star, 8 UMi, should have been engulfed during the expansion phase of its parent star's evolution. They explained the survival of this exoplanet through a binary-merger channel for 8 UMi. The key to testing this formation scenario is to derive the true age of this star: is it an old "imposter" resulting from a binary merger, or a genuinely young red clump giant? To accomplish this, we derive kinematic and chemical properties for 8 UMi using astrometric data from {\it Gaia} DR3 and the element-abundance pattern measured from a high-resolution ($R \sim 75,000$) spectrum taken by SOPHIE. Our analysis shows that 8 UMi is a normal thin-disk star with orbital rotation speed of $\it{V}_\mathrm{\phi}=\mathrm{244.96 km s^{-1}}$, and possesses a Solar metallicity ([Fe/H] $= -0.05 \pm 0.07$) and $\alpha$-element abundance ratio ([$\alpha$/Fe] $= +0.01 \pm 0.03$). By adopting well-established relationships between age and space velocities/elemental abundances, we estimate a kinematic age of $3.50^{+3.00}_{-2.00}$ Gyr, and a chemical age of $3.25^{+2.50}_{-1.50}$ Gyr from [C/N] and $3.47 \pm 1.96$ Gyr from [Y/Mg] for 8 UMi, respectively. These estimates are consistent with the isochrone-fitting age ($1.90^{+1.15}_{-0.30}$ Gyr) of 8 UMi, but are all much younger than the timescale required in a binary-merger scenario. This result challenges the binary-merger model; the existence of such a closely orbiting exoplanet around a giant star remains a mystery yet to be resolved.

astro-ph.EP↗