arXiv subjects

Jingxuan Yang

Publications and source records attributed to Jingxuan Yang.

At least 19 recordsLinked to original sources

arXiv2026

Phase-dependent chemistry of WASP-43 b revealed with a suite of one-, two-, and three-dimensional models

Our goal is to investigate the chemistry of the hot Jupiter WASP-43 b in detail using theoretical models, considering the constraints of the James Webb Space Telescope MIRI phase curve. With a suite of pseudo-two-dimensional and three-dimensional photochemical models, we simulate the composition of WASP-43 b in various configurations, and compare them with atmospheric retrieval models. We confirm that disequilibrium chemistry in our theoretical models reduces the methane concentration on the planet night side for wind jet speeds > 500 m/s. Varying the metallicity in the models induces large changes in the CO$_2$ and SO$_2$ concentrations, with SO$_2$ producing mid-infrared absorption features in synthetic emission spectra of the night side at atmospheric metallicities > 10x solar. Our models provide evidence for pole-to-equator circulation enhancing the CH$_4$, NH$_3$, and HCN abundances, which is nonetheless insufficient for detectable spectral features. Finally, we show that H$_2$O, CO, and CO$_2$ are robustly modeled, but species affected by photochemistry are more sensitive to model-specific assumptions and pathways. We conclude that horizontal quenching is the prime mechanism that explains the non-detection of methane in the MIRI phase-curve of WASP-43 b. This mechanism requires only moderate wind speeds and is operative at various thermal structures and atmospheric metallicities. Furthermore, coupled carbon-sulfur chemistry leads to an additional decrease in methane compared to previous models in the literature that did not contain sulfur chemistry. We do not favor a high metallicity as it would have led to observable SO$_2$ features in the MIRI spectra. Our study shows that phase-dependent photochemistry models are essential tools in the interpretation of hot-Jupiter phase curves, but benchmarking is needed to improve the accuracy of photochemical models in the future.

astro-ph.EP↗

arXiv2026

Pseudo-Text-Conditioned 3D Grounding DINO for Organ Localization in Abdominal CT

Reliable organ localization in abdominal CT can provide spatial priors for downstream trauma analysis. We propose CT-3GDINO, a lightweight 3D detector that adapts a Grounding-DINO-style query-based architecture to fixed organ localization using frozen pseudo-text class tokens instead of a real text encoder. The model combines a Swin3D visual backbone, bidirectional feature enhancement, pseudo-text-guided query selection, and a cross-modality decoder to predict normalized 3D boxes for liver, spleen, left kidney, right kidney, and bowel. We train and evaluate on 193 matched RSNA/RATIC CT volumes with segmentation-derived boxes. The best multi-scale model, trained from scratch, achieves 0.5830 overall top-1 class-wise mAP over 3D IoU thresholds from 0.1 to 0.7, outperforming fixed- and trainable-backbone classification-pretrained variants with 0.5570 and 0.4657 mAP. Performance is strong for coarse localization, with 0.9649 AP at IoU 0.1, but remains limited for strict box alignment, with 0.1552 AP at IoU 0.7. These results establish CT-3GDINO as an open-source baseline for pseudo-text-conditioned 3D organ localization and motivate future work on localization-aware pretraining, richer multimodal conditioning, and injury-focused detection.

cs.CV↗

arXiv2026

Deep Search with Hierarchical Meta-Cognitive Monitoring Inspired by Cognitive Neuroscience

Deep search agents powered by large language models have demonstrated strong capabilities in multi-step retrieval, reasoning, and long-horizon task execution. However, their practical failures often stem from the lack of mechanisms to monitor and regulate reasoning and retrieval states as tasks evolve under uncertainty. Insights from cognitive neuroscience suggest that human metacognition is hierarchically organized, integrating fast anomaly detection with selectively triggered, experience-driven reflection. In this work, we propose Deep Search with Meta-Cognitive Monitoring (DS-MCM), a deep search framework augmented with an explicit hierarchical metacognitive monitoring mechanism. DS-MCM integrates a Fast Consistency Monitor, which performs lightweight checks on the alignment between external evidence and internal reasoning confidence, and a Slow Experience-Driven Monitor, which is selectively activated to guide corrective intervention based on experience memory from historical agent trajectories. By embedding monitoring directly into the reasoning-retrieval loop, DS-MCM determines both when intervention is warranted and how corrective actions should be informed by prior experience. Experiments across multiple deep search benchmarks and backbone models demonstrate that DS-MCM consistently improves performance and robustness.

cs.CL↗

arXiv2025

Intelligent Resilience Testing for Decision-Making Agents with Dual-Mode Surrogate Adaptation

Testing and evaluating decision-making agents remains challenging due to unknown system architectures, limited access to internal states, and the vastness of high-dimensional scenario spaces. Existing testing approaches often rely on surrogate models of decision-making agents to generate large-scale scenario libraries; however, discrepancies between surrogate models and real decision-making agents significantly limit their generalizability and practical applicability. To address this challenge, this paper proposes intelligent resilience testing (IRTest), a unified online adaptive testing framework designed to rapidly adjust to diverse decision-making agents. IRTest initializes with an offline-trained surrogate prediction model and progressively reduces surrogate-to-real gap during testing through two complementary adaptation mechanisms: (i) online neural fine-tuning in data-rich regimes, and (ii) lightweight importance-sampling-based weighting correction in data-limited regimes. A Bayesian optimization strategy, equipped with bias-corrected acquisition functions, guides scenario generation to balance exploration and exploitation in complex testing spaces. Extensive experiments across varying levels of task complexity and system heterogeneity demonstrate that IRTest consistently improves failure-discovery efficiency, testing robustness, and cross-system generalizability. These results highlight the potential of IRTest as a practical solution for scalable, adaptive, and resilient testing of decision-making agents.

eess.SY↗

arXiv2025

Data availability and requirements relevant for the Ariel space mission and other exoplanet atmosphere applications

The goal of this white paper is to provide a snapshot of the data availability and data needs primarily for the Ariel space mission, but also for related atmospheric studies of exoplanets and brown dwarfs. It covers the following data-related topics: molecular and atomic line lists, line profiles, computed cross-sections and opacities, collision-induced absorption and other continuum data, optical properties of aerosols and surfaces, atmospheric chemistry, UV photodissociation and photoabsorption cross-sections, and standards in the description and format of such data. These data aspects are discussed by addressing the following questions for each topic, based on the experience of the "data-provider" and "data-user" communities: (1) what are the types and sources of currently available data, (2) what work is currently in progress, and (3) what are the current and anticipated data needs. We present a GitHub platform for Ariel-related data, with the goal to provide a go-to place for both data-users and data-providers, for the users to make requests for their data needs and for the data-providers to link to their available data. Our aim throughout the paper is to provide practical information on existing sources of data whether in databases, theoretical, or literature sources.

astro-ph.IM↗

arXiv2025

archNEMESIS: an open-source Python package for analysis of planetary atmospheric spectra

ArchNEMESIS is an open-source Python package developed for the analysis of remote sensing spectroscopic observations of planetary atmospheres. It is based on the widely used NEMESIS radiative transfer and retrieval tool, which has been extensively used for the investigation of a wide variety of planetary environments. The main goal of archNEMESIS is to provide the capabilities of its Fortran-based predecessor, keeping or exceeding the efficiency in the calculations, and benefitting from the advantages Python tools provide in terms of usability and portability. The code, stored in a public GitHub repository under a GPL-v3.0 license, is accompanied by detailed documentation available at https://archnemesis.readthedocs.io/.

astro-ph.IM↗

arXiv2024

Exploring Critical Testing Scenarios for Decision-Making Policies: An LLM Approach

Recent advances in decision-making policies have led to significant progress in fields such as autonomous driving and robotics. However, testing these policies remains crucial with the existence of critical scenarios that may threaten their reliability. Despite ongoing research, challenges such as low testing efficiency and limited diversity persist due to the complexity of the decision-making policies and their environments. To address these challenges, this paper proposes an adaptable Large Language Model (LLM)-driven online testing framework to explore critical and diverse testing scenarios for decision-making policies. Specifically, we design a "generate-test-feedback" pipeline with templated prompt engineering to harness the world knowledge and reasoning abilities of LLMs. Additionally, a multi-scale scenario generation strategy is proposed to address the limitations of LLMs in making fine-grained adjustments, further enhancing testing efficiency. Finally, the proposed LLM-driven method is evaluated on five widely recognized benchmarks, and the experimental results demonstrate that our method significantly outperforms baseline methods in uncovering both critical and diverse scenarios. These findings suggest that LLM-driven methods hold significant promise for advancing the testing of decision-making policies.

cs.LG↗

arXiv2024

JWST/NIRISS and HST: Exploring the improved ability to characterise exoplanet atmospheres in the JWST era

The Hubble Space Telescope has been a pioneering instrument for studying the atmospheres of exoplanets, specifically its WFC3 and STIS instruments. With the launch of JWST, we are able to observe larger spectral ranges at higher precision. NIRISS/SOSS covers the range 0.6--2.8 microns, and thus can serve as a direct comparison to WFC3 (0.8--1.7 microns). We perform atmospheric retrievals of WFC3 and NIRISS transmission spectra of WASP-39 b in order to compare their constraining power. We find that NIRISS is able to retrieve precise H2O abundances that do not suffer a degeneracy with the continuum level, due to the coverage of multiple spectral features. We also combine these datasets with spectra from STIS, and find that challenges associated with fitting the steep optical slope can bias the retrieval results. In an effort to diagnose the differences between the WFC3 and NIRISS retrievals, we perform the analysis again on the NIRISS data cut to the same wavelength range as WFC3. We find that the water abundance is in strong disagreement with both the WFC3 and full NIRISS retrievals, highlighting the importance of wide wavelength coverage. Finally, we carry out mock retrievals on the different instruments, which shows further evidence of the challenges in constraining water abundance from the WFC3 data alone. Our study demonstrates the vast information gain of JWST's NIRISS instrument over WFC3, highlighting the insights to be obtained from our new era of space-based instruments.

astro-ph.EP↗

arXiv2024

Few-Shot Testing of Autonomous Vehicles with Scenario Similarity Learning

Testing and evaluation are critical to the development and deployment of autonomous vehicles (AVs). Given the rarity of safety-critical events such as crashes, millions of tests are typically needed to accurately assess AV safety performance. Although techniques like importance sampling can accelerate this process, it usually still requires too many numbers of tests for field testing. This severely hinders the testing and evaluation process, especially for third-party testers and governmental bodies with very limited testing budgets. The rapid development cycles of AV technology further exacerbate this challenge. To fill this research gap, this paper introduces the few-shot testing (FST) problem and proposes a methodological framework to tackle it. As the testing budget is very limited, usually smaller than 100, the FST method transforms the testing scenario generation problem from probabilistic sampling to deterministic optimization, reducing the uncertainty of testing results. To optimize the selection of testing scenarios, a cross-attention similarity mechanism is proposed to learn to extract the information of AV's testing scenario space. This allows iterative searches for scenarios with the smallest evaluation error, ensuring precise testing within budget constraints. Experimental results in cut-in scenarios demonstrate the effectiveness of the FST method, significantly enhancing accuracy and enabling efficient, precise AV testing.

eess.SY↗

arXiv2024

Atmospheric retrievals suggest the presence of a secondary atmosphere and possible sulfur species on L 98-59 d from JWST NIRSpec G395H transmission spectroscopy

L 98-59 d is a Super-Earth planet orbiting an M-type star. We performed retrievals on the transmission spectrum of L 98-59 d obtained using NIRSpec G395H during a single transit, from JWST Cycle 1 GTO 1224. The wavelength range of this spectrum allows us to detect the presence of several atmospheric species. We found that the spectrum is consistent with a high mean molecular weight atmosphere. The atmospheric spectrum indicates the possible presence of the sulfur-bearing species H$_2$S and SO$_2$, which could hint at active volcanism on this planet if verified by future observations. We also tested for signs of stellar contamination in the spectrum, and found signs of unocculted faculae on the star. The tentative signs of an atmosphere on L 98-59 d presented in this work from just one transit bodes well for possible molecular detections in the future, particularly as it is one of the best targets among small exoplanets for atmospheric characterization using JWST.

astro-ph.EP↗

arXiv2024

NEMESISPY: A Python package for simulating and retrieving exoplanetary spectra

NEMESISPY is a Python package developed to perform parametric atmospheric modelling and radiative transfer calculation for the retrievals of exoplanetary spectra. It is a recent development of the well-established Fortran NEMESIS library (P. G. J. Irwin et al., 2008), which has been applied to the atmospheric retrievals of both solar system planets and exoplanets employing numerous different observing geometries. NEMESISPY can be easily interfaced with Bayesian inference algorithms to retrieve atmospheric properties from spectroscopic observations. Recently, NEMESISPY has been applied to the retrievals of Hubble and Spitzer data of a hot Jupiter (Yang et al., 2023), as well as to JWST/Mid-Infrared Instrument (JWST/MIRI) data of a hot Jupiter (Yang et al., 2024).

astro-ph.EP↗

arXiv2024

Latitudinal Asymmetry in the Dayside Atmosphere of WASP-43b

We present two-dimensional near-infrared temperature maps of the canonical hot Jupiter WASP-43b using a phase-curve observation with JWST NIRSpec/G395H. From the white-light planetary transit, we improve constraints on the planet's orbital parameters and measure a planet-to-star radius ratio of $0.15883^{+0.00056}_{-0.00053}$. Using the white-light phase curve, we measure a longitude of maximum brightness of $6.9^{+0^\circ.5}_{-0^\circ.5}$ east of the substellar point and a phase-curve offset of $10.0^{+0^\circ.8}_{-0^\circ.8}$. We also find an $\approx4σ$ detection of a latitudinal hotspot offset of $-13.4^{+3^\circ.2}_{-1^\circ.7}$, the first significant detection of a non-equatorial hotspot in an exoplanet atmosphere. We show that this detection is robust to variations within planetary parameter uncertainties, but only if the transit is used to improve constraints, showing the importance of transit observations to eclipse mapping. Maps retrieved from the NRS1 and NRS2 detectors are similar, with hotspot locations consistent between the two detectors at the $1σ$ level. Our JWST data show brighter (hotter) nightsides and a dimmer (colder) dayside at the shorter wavelengths relative to fits to \textit{Spitzer} 3.6 and 4.5 \microns\ phase curves. Through comparison between our phase curves and a set of general circulation models, we find evidence for clouds on the nightside and atmospheric drag or high metallicity reducing the eastward hotspot offset.

astro-ph.EP↗

arXiv2024

Simultaneous retrieval of orbital phase resolved JWST/MIRI emission spectra of the hot Jupiter WASP-43b: evidence of water, ammonia and carbon monoxide

Spectroscopic phase curves of hot Jupiters measure their emission spectra at multiple orbital phases, thus enabling detailed characterisation of their atmospheres. Precise constraints on the atmospheric composition of these exoplanets offer insights into their formation and evolution. We analyse four phase-resolved emission spectra of the hot Jupiter WASP-43b, generated from a phase curve observed with the MIRI/LRS onboard the JWST, to retrieve its atmospheric properties. Using a parametric 2D temperature model and assuming a chemically homogeneous atmosphere within the observed pressure region, we simultaneously fit the four spectra to constrain the abundances of atmospheric constituents, thereby yielding more precise constraints than previous work that analysed each spectrum independently. Our analysis reveals statistically significant evidence of NH3 (4$σ$) in a hot Jupiter's emission spectra for the first time, along with evidence of H2O (6.5$σ$), CO (3.1$σ$), and a non-detection of CH4. With our abundance constraints, we tentatively estimate the metallicity of WASP-43b at 0.6-6.5$\times$solar and its C/O ratio at 0.6-0.9. Our findings offer vital insights into the atmospheric conditions and formation history of WASP-43b by simultaneously constraining the abundances of carbon, oxygen, and nitrogen-bearing species.

astro-ph.EP↗

arXiv2024

Two-Dimensional Eclipse Mapping of the Hot Jupiter WASP-43b with JWST MIRI/LRS

We present eclipse maps of the two-dimensional thermal emission from the dayside of the hot Jupiter WASP-43b, derived from an observation of a phase curve with the JWST MIRI/LRS instrument. The observed eclipse shapes deviate significantly from those expected for a planet emitting uniformly over its surface. We fit a map to this deviation, constructed from spherical harmonics up to order $\ell_{\rm max}=2$, alongside the planetary, orbital, stellar, and systematic parameters. This yields a map with a meridionally-averaged eastward hot-spot shift of $(7.75 \pm 0.36)^{\circ}$, with no significant degeneracy between the map and the additional parameters. We show the latitudinal and longitudinal contributions of the day-side emission structure to the eclipse shape, finding a latitudinal signal of $\sim$200 ppm and a longitudinal signal of $\sim$250 ppm. To investigate the sensitivity of the map to the method, we fix the non-mapping parameters and derive an "eigenmap" fitted with an optimised number of orthogonal phase curves, which yields a similar map to the $\ell_{\rm max}=2$ map. We also fit a map up to $\ell_{\rm max}=3$, which shows a smaller hot-spot shift, with a larger uncertainty. These maps are similar to those produced by atmospheric simulations. We conclude that there is a significant mapping signal which constrains the spherical harmonic components of our model up to $\ell_{\rm max}=2$. Alternative mapping models may derive different structures with smaller-scale features; we suggest that further observations of WASP-43b and other planets will drive the development of more robust methods and more accurate maps.

astro-ph.EP↗

arXiv2024

Few-Shot Scenario Testing for Autonomous Vehicles Based on Neighborhood Coverage and Similarity

Testing and evaluating the safety performance of autonomous vehicles (AVs) is essential before the large-scale deployment. Practically, the number of testing scenarios permissible for a specific AV is severely limited by tight constraints on testing budgets and time. With the restrictions imposed by strictly restricted numbers of tests, existing testing methods often lead to significant uncertainty or difficulty to quantifying evaluation results. In this paper, we formulate this problem for the first time the "few-shot testing" (FST) problem and propose a systematic framework to address this challenge. To alleviate the considerable uncertainty inherent in a small testing scenario set, we frame the FST problem as an optimization problem and search for the testing scenario set based on neighborhood coverage and similarity. Specifically, under the guidance of better generalization ability of the testing scenario set on AVs, we dynamically adjust this set and the contribution of each testing scenario to the evaluation result based on coverage, leveraging the prior information of surrogate models (SMs). With certain hypotheses on SMs, a theoretical upper bound of evaluation error is established to verify the sufficiency of evaluation accuracy within the given limited number of tests. The experiment results on cut-in scenarios demonstrate a notable reduction in evaluation error and variance of our method compared to conventional testing methods, especially for situations with a strict limit on the number of scenarios.

eess.SY↗

arXiv2024

Accurately Predicting Probabilities of Safety-Critical Rare Events for Intelligent Systems

Intelligent systems are increasingly integral to our daily lives, yet rare safety-critical events present significant latent threats to their practical deployment. Addressing this challenge hinges on accurately predicting the probability of safety-critical events occurring within a given time step from the current state, a metric we define as 'criticality'. The complexity of predicting criticality arises from the extreme data imbalance caused by rare events in high dimensional variables associated with the rare events, a challenge we refer to as the curse of rarity. Existing methods tend to be either overly conservative or prone to overlooking safety-critical events, thus struggling to achieve both high precision and recall rates, which severely limits their applicability. This study endeavors to develop a criticality prediction model that excels in both precision and recall rates for evaluating the criticality of safety-critical autonomous systems. We propose a multi-stage learning framework designed to progressively densify the dataset, mitigating the curse of rarity across stages. To validate our approach, we evaluate it in two cases: lunar lander and bipedal walker scenarios. The results demonstrate that our method surpasses traditional approaches, providing a more accurate and dependable assessment of criticality in intelligent systems.

cs.LG↗

arXiv2024

Adaptive Testing Environment Generation for Connected and Automated Vehicles with Dense Reinforcement Learning

The assessment of safety performance plays a pivotal role in the development and deployment of connected and automated vehicles (CAVs). A common approach involves designing testing scenarios based on prior knowledge of CAVs (e.g., surrogate models), conducting tests in these scenarios, and subsequently evaluating CAVs' safety performances. However, substantial differences between CAVs and the prior knowledge can significantly diminish the evaluation efficiency. In response to this issue, existing studies predominantly concentrate on the adaptive design of testing scenarios during the CAV testing process. Yet, these methods have limitations in their applicability to high-dimensional scenarios. To overcome this challenge, we develop an adaptive testing environment that bolsters evaluation robustness by incorporating multiple surrogate models and optimizing the combination coefficients of these surrogate models to enhance evaluation efficiency. We formulate the optimization problem as a regression task utilizing quadratic programming. To efficiently obtain the regression target via reinforcement learning, we propose the dense reinforcement learning method and devise a new adaptive policy with high sample efficiency. Essentially, our approach centers on learning the values of critical scenes displaying substantial surrogate-to-real gaps. The effectiveness of our method is validated in high-dimensional overtaking scenarios, demonstrating that our approach achieves notable evaluation efficiency.

eess.SY↗

arXiv2023

Testing 2D temperature models in Bayesian retrievals of atmospheric properties from hot Jupiter phase curves

Spectroscopic phase curves of transiting hot Jupiters are spectral measurements at multiple orbital phases, giving a set of disc-averaged spectra that probe multiple hemispheres. By fitting model phase curves to observations, we can constrain the atmospheric properties of hot Jupiters such as molecular abundance, aerosol distribution and thermal structure, which offer insights into their dynamics, chemistry, and formation. In this work, we propose a novel 2D temperature scheme consisting of a dayside and a nightside to retrieve information from near-infrared phase curves, and apply the scheme to phase curves of WASP-43b observed by HST/WFC3 and Spitzer/IRAC. In our scheme, temperature is constant on isobars on the nightside and varies with cos$^n$(longitude/$ε$) on isobars on the dayside, where $n$ and $ε$ are free parameters. We fit all orbital phases simultaneously using the radiative transfer package NEMESISPY coupled to a Bayesian inference code. We first validate the performance of our retrieval scheme with synthetic phase curves generated from a GCM, and find our 2D scheme can accurately retrieve the latitudinally-averaged thermal structure and constrain the abundance of H$_2$O and CH$_4$. We then apply our 2D scheme to the observed phase curves of WASP-43b and find: (1) the dayside temperature-pressure profiles do not vary strongly with longitude and are non-inverted; (2) the retrieved nightside temperatures are extremely low, suggesting significant nightside cloud coverage; (3) the H$_2$O volume mixing ratio is constrained to $5.6\times10^{-5}$--$4.0\times10^{-4}$, and we retrieve an upper bound for CH$_4$ at $\sim$10$^{-6}$.

astro-ph.EP↗