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Yifan Zhou

Publications and source records attributed to Yifan Zhou.

At least 19 recordsLinked to original sources

Deep H$\alpha$ Imaging Survey of IC 348 with the Hubble Space Telescope: I. Accretion Properties of Stellar and Substellar Objects

Accretion governs the growth of young stars and the early evolution of their circumstellar disks, yet population-level measurements of accretion are often hampered by heterogeneous diagnostics and by samples preferentially selected toward disk-bearing or accreting objects. This can impact the mass accretion rate-stellar mass ($\dot{M}$-$M_\star$) relation, particularly at substellar masses. We present a uniform analysis of accretion in the $\sim$2 Myr-old star-forming cluster IC 348 based on deep Hubble Space Telescope F656N imaging. Using H$\alpha$ excess as a single, homogeneous accretion tracer, we derive accretion rates and robust upper limits for 200 cluster members spanning the stellar to substellar mass regime ($3\ M_\odot$ to $4\ M_{\rm Jup}$). Accretion is detected in $37\pm3\%$ of the sample, with fractions of $34\pm4\%$ among stellar members and $46\pm6\%$ among substellar objects. For accretors alone, the inferred $\dot{M}$-$M_\star$ relation is consistent with those measured in similarly aged regions such as Lupus. In contrast, including weak accretors and non-detections increases the scatter and flattens the slope while lowering the intercept, demonstrating the strong influence of the low-accretion tail on population-level accretion relations. For free-floating planetary-mass objects in IC 348, extrapolating the accretors-only fit overpredicts $\dot{M}$ by approximately an order of magnitude compared to the fit that includes upper limits, which instead implies mass accretion rates of $<10^{-12}$ $M_{\odot}\ \mathrm{yr}^{-1}$. These results show that sample selection, specifically whether the sample is restricted to disk-bearing or accreting targets, or instead is drawn from a complete membership census, is a dominant factor shaping population-level accretion relations across a wide dynamic range in stellar mass.

astro-ph.SR

PAWBench: How Far Are We from Probabilistically Aligned World Modeling?

Recent video generation models are increasingly framed as world models. Many physical processes can unfold in more than one valid way. Therefore, a world model should reproduce not only a plausible trajectory, but also the distribution of possible behaviors under the same initial observation and action. We call this distribution-level requirement probabilistic alignment. However, existing evaluations largely assess individual-video plausibility and do not test whether repeated generations recover the correct distribution. This raises a central question: how far are current video generators from probabilistically aligned world modeling? To answer it, we formalize probabilistic alignment as a distributional criterion for world models and introduce PAWBench, a benchmark for evaluating video generators as stochastic samplers of world dynamics. We further introduce PAWEval, an outcome-level protocol that converts repeated video rollouts into empirical distributions over possible physical behaviors. Across 50 scenarios and eleven current systems, no model consistently matches the reference probabilities while recovering the range of valid behaviors. Having established this gap, we test whether language prompts, initial noise sampling, or model training can reshape the model's predictive distribution. We believe our work can serve as a foundation for future efforts to move towards probabilistically aligned world modeling.

cs.CV

Implicit Q-learning-bootstrapped ant colony optimization for maritime moving-target observation scheduling with agile satellites

Maritime moving-target observation scheduling with agile Earth observation satellites is a dynamic, sequence-dependent combinatorial optimization problem. Sea-surface targets move continuously, causing feasible observation windows to vary with target motion and satellite orbital geometry. The scheduler must jointly determine task selection, satellite assignment, observation-window selection, and observation ordering under time-window, attitude-maneuvering, onboard-resource, and cloud-affected availability constraints. This paper proposes an implicit Q-learning-bootstrapped ant colony optimization method, termed IQACO, for multi-satellite maritime moving-target observation scheduling. Rather than directly learning a task-selection policy, IQACO embeds an offline implicit Q-learning module into constructive ant colony optimization to adaptively adjust the pheromone factor, heuristic factor, and evaporation rate. A compact search-state representation captures pheromone distribution, current and historical-best solution quality, and iteration progress. During online scheduling, ant colony optimization constructs feasible observation sequences, while the learned policy regulates exploration and exploitation according to the current search state. Experiments on 14 scenarios with different scales and satellite configurations show that IQACO obtains the highest mean observation benefit in every scenario, improves the result of conventional ant colony optimization by 3.40\%--9.40\%, accelerates convergence, and remains stable under different objective-weight settings. These results demonstrate that offline value learning provides an effective adaptive search-control mechanism for constrained maritime moving-target observation scheduling.

cs.AI

SemaPLC: A Project-Grounded, Verification-Gated Agent Harness for PLC Code Generation

Programmable logic controllers (PLCs) run industrial plants, and large language models can already generate independent program organization units (POUs) for them. Whether such logic integrates into an existing PLC project and then runs correctly has been checked only in limited tests. We present \textsc{SemaPLC}, a project-grounded and verification-gated agent harness assembled from conventional tools but governed by a strict completion rule. Rather than stopping when the model judges its own output adequate, \textsc{SemaPLC} declares a task complete only when logged external checks confirm it. Those checks cover the specification, the compilation, and the behavior on a live runtime. On 117 independent-POU tasks matching existing benchmarks, it attains the highest strict verified pass rate on all seven models (72.6\% mean). On a project-context track of 65 tasks whose generated logic must compile and run inside a real project, it attains the highest mean on integrated compilation, static behavior, and dynamic behavior. Of the three layers, dynamic behavior is the most revealing. We measure it by deploying the generated and the reference logic to a live PLC runtime and comparing their executed traces. All methods fall within 10 static points of one another, whereas dynamic scores separate them sharply, from 22.4 to 31.4 for the baselines against 52.2 for \textsc{SemaPLC}. Overall, our verification-gated harness raises the mean at every layer and most sharply at runtime. Execution, not static scoring, is the faithful test of whether generated control logic actually works. \textsc{SemaPLC} is open-sourced at https://github.com/midea-ai/SemaPLC.

cs.SE

StagedWorkspace: A Versioned Workspace for Knowledge-Work Agents

AI agents increasingly perform knowledge work (i.e., produce and modify persistent digital artifacts such as code repositories, documents, spreadsheets, slides, reports), yet the parsed views they search, the native files they edit, the changes they review, and the artifacts they submit can refer to different versions of the same work product. We formulate this as a workspace-state contract: every view should be explicitly tied to a version of the evolving workspace state. Coding agents partly address this need through repository contracts for search, diffs, and tests, whereas an analogous contract is less explicit for PDFs, spreadsheets, slides, notebooks, and mixed-format project folders. We propose StagedWorkspace, a versioned workspace for knowledge-work agents. The workspace binds parsed records and review diffs to content hashes of the native files as they change. In fixed-harness ablations on OfficeQA Pro and APEX-Agents, dual parsed/native access has the highest point estimate for every tested model; relative to the more limiting single view, it improves OfficeQA Pass@1 by 8.3-12.1 points and APEX mean rubric score by 4.7-9.2 points. SW-AGENT scores 63.9% with Gemini 3.1 Pro on OfficeQA and 42.1 with GPT-5.4 Nano on APEX, compared with published same-model scores of 29.3% and 25.5, respectively. A paired review-axis ablation on 57 file-editing tasks further finds higher observed scores when diffs are visible. These results identify workspace state as an experimental variable in knowledge-work agents and motivate benchmarks that score evidence, staged edits, and submitted artifacts as explicit state transitions.

cs.AI

One-Step Epitaxial Access to Rhombohedral Graphene Flat-Band States on Step-Bunched SiC

Rhombohedral graphene multilayers provide a moiré-free platform for correlated and topological flat-band physics, but direct, transfer-free epitaxial access to thickness-tunable multilayers remains limited. Here we report a one-step graphitization route on 4$^\circ$ off-axis 4H-SiC, in which high-temperature flash annealing simultaneously drives self-organized step bunching and multilayer graphene formation. Atomic-resolution cross-sectional scanning transmission electron microscopy identify local ABC registry and distinguish rhombohedral from Bernal stacking. The thickness is tuned from bilayer to more than twenty layers by varying single parameter, the annealing temperature. Angle-resolved photoemission spectroscopy directly tracks the thickness-dependent evolution from interface-dominated low-energy states toward pronounced near-Fermi-level flat-band spectral weight in thick multilayers. Low-temperature scanning tunneling microscopy and spectroscopy on a 17-layer film further reveal a 13.4 meV low-energy spectral reconstruction and a $\sqrt{3} \times \sqrt{3}$ Kekulé-like modulation, providing microscopic signatures consistent with an intervalley-mixed electronic texture. This one-step, transfer-free approach establishes step-bunched SiC as an epitaxial platform that links stacking engineering with moiré-free correlated flat-band electronic states.

cond-mat.mtrl-sci

The JWST weather report: Unravelling the atmospheric variability of isolated worlds using principal component analysis

Brown dwarf variability directly probes atmospheric dynamics beyond the Solar System, and recent JWST time-resolved spectroscopy has opened a new window into these processes. Principal component analysis (PCA) offers a data-driven framework to identify the dominant, independent patterns of spectral variability of variable targets without relying on prior atmospheric assumptions. SIMP 0136 is a young, T2.5, brown dwarf at the planetary-mass boundary, making it an ideal analogue for directly imaged exoplanets. We analysed one rotation of JWST/NIRSpec PRISM time-series spectroscopy to investigate the drivers of its variability using PCA. Two principal components are sufficient to reduce the residual spectra to the propagated noise floor, indicating that they capture the detectable coherent spectroscopic variability. The leading principal component captures broadband variability consistent with temperature changes, while the second traces chromatic variability linked to vertical cloud structure. The dominance of two components implies that the spectra can be described as mixtures of three distinct atmospheric states, whose relative contributions we mapped as a function of rotational phase. The observed spectra are described as evolving linear combinations of these states, indicating that the variability arises from the changing visibility of spatially distinct atmospheric regions. By projecting Sonora Diamondback forward models into the same principal component space, we found that the principal components capture a large fraction of the model variance, demonstrating that the same physical processes that govern SIMP-0136's observed variability also capture much of the model grid's variation. Our results establish PCA as a computationally efficient, physically interpretable framework for analysing JWST time-resolved spectroscopy of substellar atmospheres.

astro-ph.EP

Photometric Variability and Rotation of Beta Pictoris b from JWST NIRCam Coronagraphic Imaging

We report the detection of photometric variability in the directly imaged super-Jupiter $β$ Pictoris b. Using JWST NIRCam dual-band coronagraphic imaging, we conducted a 16-hour continuous photometric monitoring campaign in the F210M and F410M filters. We developed and validated a time-series photometry framework that combines PSF subtraction, principal component analysis for systematic noise removal, and injection-and-recovery tests to confirm signal fidelity. Both light curves show consistent sinusoidal variability at $\sim$5$σ$ and $\gg 5σ$ significance in the F210M and F410M bands, respectively. A joint sinusoidal fit yields a rotation period of $P_{\rm rot} = 9.00 \pm 0.13$ hr and variability amplitudes of $0.85 \pm 0.07\%$ and $0.89 \pm 0.04\%$ in F210M and F410M, respectively. The near-identical amplitudes and periods in both bands confirm a common astrophysical origin in a heterogeneous atmosphere. Combining $P_{\rm rot}$ with the previously measured projected rotational velocity, we constrain the line-of-sight spin axis inclination of $β$ Pic b. The result favors an equator-on viewing geometry, consistent with line-of-sight spin-orbit alignment: the planetary spin axis, orbital plane, debris disk, and stellar equator are all mutually aligned. This stands in sharp contrast to the large obliquities of wide-separation companions that are likely formed via gravitational fragmentation. Together with the system's young age, this observation provides independent dynamical evidence that $β$ Pic b formed via core accretion. This result constitutes the first detection of rotational modulation in a close-in, high-contrast exoplanet that likely formed via core accretion, demonstrating that time-series coronagraphic imaging with JWST opens a powerful new window onto the rotation, atmospheric dynamics, and spin-orbit architecture of this population.

astro-ph.EP

Trainable Log-linear Sparse Attention for Efficient Diffusion Transformers

Diffusion Transformers (DiTs) set the state of the art in visual generation, yet their quadratic self-attention cost fundamentally limits scaling to long token sequences. Recent Top-K sparse attention approaches reduce the computation of DiTs by compressing tokens into block-wise representation and selecting a small set of relevant key blocks, but still suffer from (i) quadratic selection cost on compressed tokens and (ii) increasing K required to maintain model quality as sequences grow. We identify that their inefficiency is due to the single-level design, as a single coarse level is insufficient to represent the global structure. In this paper, we introduce Log-linear Sparse Attention (LLSA), a trainable sparse attention mechanism for extremely long token sequences that reduces both selection and attention costs from quadratic to log-linear complexity by utilizing a hierarchical structure. LLSA performs hierarchical Top-K selection, progressively adopting sparse Top-K selection with the indices found at the previous level, and introduces a Hierarchical KV Enrichment mechanism that preserves global context while using fewer tokens of different granularity during attention computation. To support efficient training, we develop a high-performance GPU implementation that uses only sparse indices for both the forward and backward passes, eliminating the need for dense attention masks. We evaluate LLSA on high-resolution pixel-space image generation without using patchification and VAE encoding. LLSA accelerates attention inference by 28.27x and DiT training by 6.09x on 256x256 pixel token sequences, while maintaining generation quality. The results demonstrate that LLSA offers a promising direction for training long-sequence DiTs efficiently. Code is available at: https://github.com/SingleZombie/LLSA

cs.CV

Direct Imaging Discovery of Giant Exoplanet $β$ Pictoris d: A Decade-Long Game of Hide-and-Seek

We report the direct imaging discovery of a third exoplanet in the $β$ Pictoris system. We detect $β$ Pictoris d ($β$ Pic d) in non-coronagraphic observations obtained with VLT/ERIS as well as multi-epoch archival datasets from JWST/NIRCam and VLT/SPHERE. Astrometric measurements over an 11-year baseline demonstrate that it is consistent with a gravitationally-bound source with orbital motion. Joint multi-planet orbit fits of all three planets in the system yield a semi-major axis of $26.0^{+2.2}_{-6.1}$ au and inclination $89.0^{+0.7}_{-0.6}$ deg for planet d. $β$ Pic d has a larger orbital semi-major axis than the other known planets in the system, but is coplanar with the inner two planets, and its orbit is consistent with sculpting the inner edge of the debris disk. $β$ Pic d has a contrast of $ΔL^{\prime}=12.11\pm0.15$ mag, with colors and luminosity that closely match those of 51 Eri b, another exoplanet in the $β$ Pictoris moving group. Its VLT/ERIS and JWST/NIRCam colors are distinct from those of free-floating planetary-mass objects of a similar age and temperature. Its red $F410M-F444W$ color indicates strong CO$_2$ absorption in its atmosphere and suggests significant enhancement in metals compared to free-floating objects. From the ATMO hot-start evolutionary models, we estimate an effective temperature of $600^{+45}_{-60}$ K and mass of $2.4\pm0.6$ $M_{\rm Jup}$, which also closely matches similar estimates for 51 Eri b. $β$ Pic d is among the lowest-mass exoplanets imaged from the ground. This discovery highlights the deep sensitivity achievable with ground-based imaging in the mid-infrared and the discovery potential of future high-contrast observations with the Extremely Large Telescope.

astro-ph.EP

Ideas Have Genomes: Benchmarking Scientific Lineage Reasoning and Lineage-Grounded Idea Generation

Scientific ideas rarely start from a blank page. They inherit mechanisms, repair known limitations, and recombine pieces of earlier work, much like biological genomes. Current benchmarks still say little about whether AI systems can follow this inheritance structure. We present IdeaGene-Bench (IG-Bench), a benchmark for scientific lineage reasoning and lineage-grounded idea generation. IG-Bench is organized around the IdeaGene framework: each paper or proposal is represented as a set of minimal, typed, evidence-grounded Idea Genome objects, and a GenomeDiff aligns these objects to record inheritance, mutation, loss, external import, and novel insertion under six operational evolutionary dynamics. The benchmark contains 1,961 golden lineage traces, 1,085 curated Idea Genome objects, and 920 pairwise GenomeDiff records across 10 scientific domains. It supports two evaluations. IG-Exam (42 task types, 1,029 instances) tests closed-form lineage reasoning across Idea Genome abstraction, inheritance tracing, evolutionary reasoning, and lineage verification. IG-Arena evaluates generation with a lineage-conditioned Population-Evolution Score(PES), asking whether a proposal can be inserted as a coherent descendant of a given lineage population: it should inherit the right Idea Genome objects, vary meaningfully from nearby work, and offer selection value for future research. Experiments on 14 LLM-based scientists expose a compositional bottleneck. The strongest system reaches only 27.3% exact accuracy on lineage reasoning, and structured lineage context reshuffles system rankings rather than helping every participant uniformly.

cs.AI

ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research

AI coding agents are increasingly used for scientific work, but their end-to-end autonomous research capability remains difficult to verify. We present ResearchClawBench, a benchmark for evaluating autonomous scientific research across 40 tasks from 10 scientific domains. Each task is grounded in a real published paper, provides related literature and raw data, and hides the target paper during evaluation. Expert-curated multimodal rubrics decompose the target scientific artifacts into weighted criteria, enabling evaluation of target-paper-level re-discovery while leaving room for new discovery. We evaluate seven autonomous research (auto-research) agents under a unified protocol and seventeen native LLMs through the lightweight ResearchHarness. Current systems remain far from reliable re-discovery: the strongest autonomous agent, Claude Code, averages 21.5, and the strongest ResearchHarness LLM, Claude-Opus-4.7, averages 20.7, with an LLM frontier mean of only 26.5. Error analysis shows that failures concentrate in experimental protocol mismatch, evidence mismatch, and missing scientific core. ResearchClawBench provides a reproducible evaluation frontier for measuring progress toward autonomous scientific research.

cs.LG

Asymmetric nightside CO2 features, inefficient heat transport, and precise evolutionary constraints: Spectroscopic phase curves reveal the past and present of a white dwarf-brown dwarf binary

We present the first JWST phase curve of a white dwarf-brown dwarf binary, a NIRSpec PRISM observation of ZTFJ0038+2030. Short-period white dwarf-brown dwarf binaries provide unique laboratories to probe substellar atmospheres. Tidal locking drives hot Jupiter-like atmospheric dynamics in the brown dwarf. The system's formation history offers a window into planetary systems around post-main-sequence stars. We obtain a full-orbit phase curve of ZTF0038, including a total eclipse of the white dwarf, which enables us to separate the two components' emission throughout the entire orbit, and we model the brown dwarf's phase-resolved emission spectra using substellar atmosphere forward models and atmospheric retrievals. The PRISM spectrum covers ~80% of the brown dwarf's bolometric emission, enabling a nearly model-independent energy balance calculation, which yields a day-to-nightside heat transport efficiency of <10%. Inefficient heat redistribution is further supported by the phase curve shape and the nightside spectrum closely resembling non-irradiated mid-to-late T dwarfs. The spectroscopic phase curves reveal a stark nightside asymmetry associated with a strong CO2 absorption feature at 4.2 um, while the retrieved abundances indicate a longitudinally homogeneous distribution of CO2 as well as all other key species detected in the atmosphere. The precise internal luminosity measurement of the brown dwarf informs both the age of the WD-BD system (7.5-8.8 Gyr) and indicates a low common-envelope ejection efficiency. These data illustrate the exquisite opportunity to probe the three-dimensional processes of substellar atmospheres, connect substellar and exoplanet atmospheres, and probe the evolution of post-main-sequence planetary systems using WD-BDs.

astro-ph.SR

SciFig: Towards Automating Editable Figure Generation for Scientific Papers

High-quality methodology figures are central to scientific communication, yet they remain difficult and time-consuming to create. Such figures must distill a method's components and information flow into a clear, revisable diagram as the paper evolves. Existing methodology diagram automation systems typically face a trade-off between editability and visual quality: TikZ- or SVG-based methods produce editable structured outputs but often lack the richness of human-designed figures, while image-generation models produce polished raster outputs that are difficult to revise. We introduce SciFig, an end-to-end multi-agent framework for generating visually rich and fully editable methodology figures from scientific text. SciFig decomposes figure generation into planning, layout synthesis, component rendering, and iterative refinement, producing XML figures that can be edited in standard diagramming tools and refined through human or VLM feedback. We also introduce SciFig-Bench, a human-verified benchmark of 435 author-drawn methodology figures from 37 arXiv domains and 15 top-tier AI/ML venues, and SciFig-Eval, a four-axis evaluation protocol for measuring figure quality. Across seven single-agent and agentic baselines, SciFig achieves the best performance on all four SciFig-Eval axes and generates editable figures in about 10 minutes on average. Qualitative examples further show that SciFig can generalize beyond methodology figures to teaser diagrams and statistical plots. Dataset and code are available at: https://shramanpramanick.github.io/SciFig/.

cs.AI

A Scalable Path to Astrometric Exomoon Discoveries with the Nautilus Space Observatory

Moons orbiting exoplanets (exomoons) can be detected through the reflex motion they impart to their host planet, which is recoverable in relative star-planet astrometric time series. The signal grows with moon mass and orbital separation and decreases with distance, so the nearest and least massive imaged planets are the most favorable targets. Recovering small (<Earth-mass) moons requires continuous, long-baseline, high-precision monitoring that is only practical with a dedicated or nearly dedicated facility. Building on recent simulations of astrometric exomoon detection and of the resulting population yields, we argue that the scalable, replicable architecture of the Nautilus Space Observatory is uniquely suited to this problem, and we outline a staged campaign. In an initial phase, one or a few small apertures target the nearest imaged giant planets--a high-reward but low-probability search focused on the closest stars. As the array is built out, the astrometric noise floor decreases and the same technique extends the search to the nearest such systems among nearby stars of spectral type K and earlier. This would be performed in parallel with high-contrast imaging and spectral characterization of the host planets and in synergy with a companion starshade concept for imaging Earth-like planets around the same nearby stars. Nautilus thus provides a scalable path from the first detection of a nearby exomoon toward a systematic search for exomoons around the closest stars.

astro-ph.IM

Nautilus Space Observatory: The Evolution of Planets and their Atmospheres

We are just beginning to explore the billion-year evolution from nascent planets in disks to mature planetary systems. Recent discoveries hint at demographic and atmospheric differences between young planets and their Gyr-old counterparts, but current facilities are limited - particularly in their ability to conduct statistical atmospheric studies over a broad period range. This white paper outlines compelling science achievable with the Nautilus Space Observatory, a proposed constellation of large-diameter space telescopes. We identify four primary scientific objectives: (1) determining the timescales over which planets evolve into sub-Neptunes and super-Earths; (2) tracking the temporal evolution of atmospheric mass-loss rates; (3) characterizing the evolution of the atmospheric mean molecular weight and C/O ratio; and (4) identifying the emergence of Helium-dominated worlds. Answering these questions requires the high spatial resolution, broad-wavelength coverage, large effective area, and parallelized multiple units that Nautilus provides. By isolating the physical processes that govern the evolution of planets and their atmospheres, these science objectives directly support NASA's Cosmic Origins and Exoplanet Exploration Programs.

astro-ph.IM

Toward Vibe Medicine: A Self-Evolving Multi-Agent Framework for Clinical Decision Support

In recent years, the advances of large language models and autonomous agents have revolutionized the healthcare field, facilitating diagnosis and improving treatment results. However, most existing AI systems rely on pre-trained knowledge and predefined pipelines, which struggle to learn dynamically from the interactive chat session history that contains patient outcomes and past failures. To address this limitation, we propose VIBEMed, a multi-agent framework with a built-in self-evolution mechanism and architecture-level safety sandbox for robust clinical decision support. The system integrates three specialized agents, including a Clinical Diagnostic Agent (CDA) for hypothesis generation, a Therapeutic Execution Agent (TEA) for treatment planning, and a Clinical Evolution Manager Agent (CEMA) that distills longitudinal clinical feedback into reusable knowledge, transforming multimodal patient information into personalized medical decisions. Through self-evolution mechanism, the framework enables iterative updates across memory, model behavior, and decision strategies, allowing the system to improve over time. Experimental results show that VIBEMed demonstrates superior performance through its evolving mechanism in complex clinical cases, particularly in tasks that require integrated decision-making and longitudinal planning. The framework also supports reliable end-to-end decisions in challenging scenarios such as oncology treatment planning, highlighting its feasibility in real-world clinical contexts. Overall, VIBEMed provides a practical path beyond static AI systems toward adaptive, experience-driven clinical decision support, demonstrating the value of combining multi-agent collaboration with continuous evolution for advancing precision medicine.

cs.AI

Ultraviolet Imaging of SR 12 c with HST/WFC3: Accretion and Variability of a Giant Planet at the End Stages of Growth

Many details of the gas accretion phase during giant planet formation remain untested. We present new 0.2$\unicode{x2013}$0.7 $μ$m UV-through-red optical imaging of the young, wide-orbit planetary-mass companion SR 12 c from the Wide Field Camera 3 (WFC3) instrument on board the Hubble Space Telescope. SR 12 c exhibits strong accretion-related continuum excess blueward of $\sim$5000 $\unicode{x212B}$ and clear signs of the Balmer jump at 3646 $\unicode{x212B}$. We derive a total accretion luminosity of 1.65 $\pm$ $0.19 \times 10^{-5} L_{\odot}$ and a mass accretion rate of 8 $\pm$ $2\times 10^{-12}$ M$_{\odot}$ yr$^{-1}$. Based on its mass and age, SR 12 c will not grow by an appreciable amount at its current accretion rate; it is at the end stages of assembly. No accretion variability is evident between the two epochs of the WFC3 observations spanning a month-long baseline, but the H$α$ emission line strength decreases by 90% compared to the reported flux from five years earlier. Combined with previous observations of SR 12 c, we assemble one of the most complete spectral energy distributions of a young giant planet to date, spanning the UV through sub-mm wavelengths (0.2$\unicode{x2013}$880 $μ$m). This adds SR 12 c to the small yet growing sample of planets with detailed accretion and disk constraints, which together are beginning to establish the diversity of timescales and physical processes governing the formation of giant planets.

astro-ph.EP