Emission lines of FeI and NiI are commonly found in the coma of solar system comets, even at large heliocentric distances. These atoms are most likely released from the surface of the comet's nucleus or from a short-lived parent. The presence of these lines in cometary spectra is unexpected because the surface blackbody equilibrium temperature is too low to allow the sublimation of refractory minerals containing these metals. These lines were also found in the interstellar comet 2I/Borisov which has a NiI/FeI abundance ratio similar to that observed in solar system comets. On average, this ratio is one order of magnitude higher than the solar Ni/Fe abundance ratio. Here, we report observations of the new interstellar comet 3I/ATLAS, which were carried out with the ESO Very Large Telescope equipped with the UVES spectrograph. Spectra were obtained at six epochs, at heliocentric distances ranging from 3.14 to 2.14 au. NiI was detected at all epochs. FeI was only detected at heliocentric distances smaller than 2.64 au. We estimated the NiI and FeI production rates by comparing the observed line intensities with those produced by a fluorescence model. Comet 3I exhibits a high production rate of NiI atoms as well as a high NiI/FeI ratio, making it exceptional when compared to solar system comets and 2I/Borisov. Additionally, we found that the NiI/FeI ratio decreases rapidly with decreasing heliocentric distance, suggesting that comet 3I could soon become indistinguishable from solar system comets in this respect. We interpreted these observations assuming that the NiI and FeI atoms were released through the sublimation of Ni(CO)$_4$ and Fe(CO)$_5$ carbonyls, which supports the presence of these species in the cometary material.

Standard techniques for differentially private estimation, such as Laplace or Gaussian noise addition, require guaranteed bounds on the sensitivity of the estimator in question. But such sensitivity bounds are often large or simply unknown. Thus we seek differentially private methods that can be applied to arbitrary black-box functions. A handful of such techniques exist, but all are either inefficient in their use of data or require evaluating the function on exponentially many inputs. In this work we present a scheme that trades off between statistical efficiency (i.e., how much data is needed) and oracle efficiency (i.e., the number of evaluations). We also present lower bounds showing the near-optimality of our scheme.

We present a novel and practically significant problem-Geo-Contextual Soundscape-to-Landscape (GeoS2L) generation-which aims to synthesize geographically realistic landscape images from environmental soundscapes. Prior audio-to-image generation methods typically rely on general-purpose datasets and overlook geographic and environmental contexts, resulting in unrealistic images that are misaligned with real-world environmental settings. To address this limitation, we introduce a novel geo-contextual computational framework that explicitly integrates geographic knowledge into multimodal generative modeling. We construct two large-scale geo-contextual multimodal datasets, SoundingSVI and SonicUrban, pairing diverse soundscapes with real-world landscape images. We propose SounDiT, a novel Diffusion Transformer (DiT)-based model that incorporates geo-contextual scene conditioning to synthesize geographically coherent landscape images. Furthermore, we propose a practically-informed geo-contextual evaluation framework, the Place Similarity Score (PSS), across element-, scene-, and human perception-levels to measure consistency between input soundscapes and generated landscape images. Extensive experiments demonstrate that SounDiT outperforms existing baselines in both visual fidelity and geographic settings. Our work not only establishes foundational benchmarks for GeoS2L generation but also highlights the importance of incorporating geographic domain knowledge in advancing multimodal generative models, opening new directions at the intersection of generative AI, geography, urban planning, and environmental sciences.

Large Language Model (LLM)-based multi-agent systems are increasingly applied to automate computational workflows in science and engineering. However, how inter-agent dynamics influence reasoning quality and verification reliability remains unclear. We study these mechanisms using an AutoGen-based multi-agent framework for linear-elastic Finite Element Analysis (FEA), evaluating seven role configurations across four tasks under a fixed 12-turn conversation limit. From 1,120 controlled trials, we find that collaboration effectiveness depends more on functional complementarity than team size: the three-agent Coder-Executor-Critic configuration uniquely produced physically and visually correct solutions, while adding redundant reviewers reduced success rates. Yet three systematic failure modes persist: (1) affirmation bias, where the Rebuttal agent endorsed rather than challenged outputs (85-92% agreement, including errors); (2) premature consensus caused by redundant reviewers; and (3) a verification-validation gap where executable but physically incorrect code passed undetected. No agent combination successfully validated constitutive relations in complex tasks. Building on theories of functional diversity, role differentiation, and computational validation, we propose actionable design principles: (i) assign complementary agent roles, (ii) enforce multi-level validation (execution, specification, physics), and (iii) prevent early consensus through adversarial or trigger-based interaction control. These findings establish a principled foundation for designing trustworthy LLM collaborations in engineering workflows.

As part of the Galactic Bulge Time Domain Survey (GBTDS), the Nancy Grace Roman Galactic Exoplanet Survey (RGES) will use microlensing to discover cold outer planets and free-floating planets unbound to stars. NASA has established several science requirements for the GBTDS to ensure RGES success. A key advantage of RGES is Roman's high angular resolution, which will allow detection of flux from many host stars. One requirement specifies that Roman must measure the masses and distances of 40% of detected planet hosts with 20% precision or better. To test this, we simulated microlensing events toward the GBTDS fields and used Fisher matrix analysis to estimate light curve parameter uncertainties. Combining these with Roman imaging observables (lens flux, relative lens-source proper motion), we estimated the achievable precision of lens mass and distance measurements. Using pyLIMASS, a publicly available code for estimating lens properties, we applied this analysis to 3,000 simulated events. Assuming the Cassan et al. (2012) exoplanet mass function, we find that >40% of host stars meet the required 20% precision threshold, confirming that the GBTDS can satisfy the mission requirement. We validated our approach by comparing our inferred lens masses and distances to empirical measurements from detailed image-constrained light curve modeling of historical microlensing events with Hubble and Keck follow-up imaging. Our results agree within roughly 1 sigma, demonstrating that both approaches yield consistent and reliable mass and distance estimates, and confirming the robustness of our simulations for Roman-era microlensing science.

We report VLT spectroscopy of the interstellar comet 3I/ATLAS (C/2025 N1) from $r_{\rm h}\!\simeq\!4.4$ to $2.85$ au using X-shooter (300-550 nm, $R\!\simeq\!3000$) and UVES (optical, $R\!\simeq\!35k-80k$). The coma is dust-dominated with a fairly constant red optical continuum slope ($\sim$21-22\%/1000Å). At $r_{\rm h}\!\simeq\!3.17$ au we derive $3\sigma$ limits of Q({\rm OH})<7.76\times10^{23}\ {\rm s^{-1}}, but find no indications for [O I], C$_2$, C$_3$ or NH$_2$. We report detection of CN emission and also detect numerous Ni I lines while Fe I remains undetected, potentially implying efficiently released gas-phase Ni. From our latest X-shooter measurements conducted on 2025-08-21 ($r_{\rm h} = 2.85$\,au) we measure production rates of $\log~Q(\mathrm{CN}) = 23.61\pm 0.05$ molecules s$^{-1}$ and $\log~Q$(Ni) $= 22.67\pm0.07$ atoms s$^{-1}$, and characterize their evolution as the comet approaches perihelion. We observe a steep heliocentric-distance scaling for the production rates $Q(\mathrm{Ni}) \propto r_h^{-8.43 \pm 0.79}$ and for $Q(\mathrm{CN}) \propto r_h^{-9.38 \pm 1.2}$, and predict a Ni-CO$_{(2)}$ correlation if the Ni I emission is driven by the carbonyl formation channel. Energetic considerations of activation barriers show that this behavior is inconsistent with direct sublimation of canonical metal/sulfide phases and instead favors low-activation-energy release from dust, e.g. photon-stimulated desorption or mild thermolysis of metalated organics or Ni-rich nanophases, possibly including Ni-carbonyl-like complexes. These hypotheses are testable with future coordinated ground-based and space-based monitoring as 3I becomes more active during its continued passage through the solar system.

The Cosmological Principle (CP) -- the notion that the Universe is spatially isotropic and homogeneous on large scales -- underlies a century of progress in cosmology. It is conventionally formulated through the Friedmann-Lema\^itre-Robertson-Walker (FLRW) cosmologies as the spacetime metric, and culminates in the successful and highly predictive $\Lambda$-Cold-Dark-Matter ($\Lambda$CDM) model. Yet, tensions have emerged within the $\Lambda$CDM model, most notably a statistically significant discrepancy in the value of the Hubble constant, $H_0$. Since the notion of cosmic expansion determined by a single parameter is intimately tied to the CP, implications of the $H_0$ tension may extend beyond $\Lambda$CDM to the CP itself. This review surveys current observational hints for deviations from the expectations of the CP, highlighting synergies and disagreements that warrant further study. Setting aside the debate about individual large structures, potential deviations from the CP include variations of cosmological parameters on the sky, discrepancies in the cosmic dipoles, and mysterious alignments in quasar polarizations and galaxy spins. While it is possible that a host of observational systematics are impacting results, it is equally plausible that precision cosmology may have outgrown the FLRW paradigm, an extremely pragmatic but non-fundamental symmetry assumption.

A significant performance inhibitor of free-space continuous variable quantum key distribution (CVQKD) is turbulence, which gives rise to wavefront phase and amplitude aberrations. We demonstrate that in a turbulent channel, during coherent state transmissions from a continuous-wave laser, that the interferometric visibility between the local oscillator (LO) and quantum signal decreases. A solution to this is incorporating adaptive optics at the receiver to correct phase and amplitude aberrations in the wavefronts of the received quantum signal. We demonstrate the increased interferometric visibility and decrease in its fluctuations in a 60 cm and 30 m turbulent channel when using adaptive optics through channel characterisation. In an ideal CVQKD system, we show that this leads to more precise and larger positive secret key rates, improving the performance of free-space CVQKD in turbulent channels.

Implicit Neural Representations (INRs) have emerged as a powerful approach for video representation, offering versatility across tasks such as compression and inpainting. However, their implicit formulation limits both interpretability and efficacy, undermining their practicality as a comprehensive solution. We propose a novel video representation based on deformable 2D Gaussian splatting, dubbed D2GV, which aims to achieve three key objectives: 1) improved efficiency while delivering superior quality; 2) enhanced scalability and interpretability; and 3) increased friendliness for downstream tasks. Specifically, we initially divide the video sequence into fixed-length Groups of Pictures (GoP) to allow parallel training and linear scalability with video length. For each GoP, D2GV represents video frames by applying differentiable rasterization to 2D Gaussians, which are deformed from a canonical space into their corresponding timestamps. Notably, leveraging efficient CUDA-based rasterization, D2GV converges fast and decodes at speeds exceeding 400 FPS, while delivering quality that matches or surpasses state-of-the-art INRs. Moreover, we incorporate a learnable pruning and quantization strategy to streamline D2GV into a more compact representation. We demonstrate D2GV's versatility in tasks including video interpolation, inpainting and denoising, underscoring its potential as a promising solution for video representation. Code is available at: \href{https://github.com/Evan-sudo/D2GV}{this https URL}.

We introduce a gradient-free framework for Bayesian Optimal Experimental Design (BOED) in sequential settings, aimed at complex systems where gradient information is unavailable. Our method combines Ensemble Kalman Inversion (EKI) for design optimization with the Affine-Invariant Langevin Dynamics (ALDI) sampler for efficient posterior sampling-both of which are derivative-free and ensemble-based. To address the computational challenges posed by nested expectations in BOED, we propose variational Gaussian and parametrized Laplace approximations that provide tractable upper and lower bounds on the Expected Information Gain (EIG). These approximations enable scalable utility estimation in high-dimensional spaces and PDE-constrained inverse problems. We demonstrate the performance of our framework through numerical experiments ranging from linear Gaussian models to PDE-based inference tasks, highlighting the method's robustness, accuracy, and efficiency in information-driven experimental design.

We present panchromatic observations and modeling of supernova (SN) 2020tlf, the first normal type II-P/L SN with confirmed precursor emission, as detected by the Young Supernova Experiment transient survey with the Pan-STARRS1 telescope. Pre-explosion emission was detected in $riz-$bands at 130 days prior to SN 2020tlf and persisted at relatively constant flux until first light. Soon after discovery, "flash" spectroscopy of SN 2020tlf revealed prominent narrow symmetric emission lines (v_w &lt; 300 km s$^{-1}$) that resulted from the photo-ionization of unshocked circumstellar material (CSM) shedded in progenitor mass loss episodes in the final weeks to months before explosion. Surprisingly, this novel display of pre-SN emission and associated mass loss occurred in a RSG progenitor with ZAMS mass of only 10-12 M$_{\odot}$, as inferred from nebular spectra. Modeling of the light curve and multi-epoch spectra with the non-LTE radiative transfer code CMFGEN and radiation-hydrodynamical (RHD) code HERACLES suggests a dense CSM limited to $r \approx 10^{15}$cm, and mass loss rate of$10^{-2}$M$_{\odot}$yr$^{-1}$. The subsequent luminous light-curve plateau and persistent blue excess indicates an extended progenitor, compatible with a RSG model with $R_{\star} = 1100$ R$_{\odot}$. Inferences from the limits on the shock-powered X-ray and radio luminosity are consistent with these conclusions and suggest a CSM density of \rho &lt; 2 \times 10^{-16} g cm$^{-3}$ for distances of $r \approx 5 \times 10^{15}$cm, as well as a mass loss rate of$\dot M<1.3 \times 10^{-5}\,\rm{M_{\odot}\,yr^{-1}}$ at larger distances. A promising power source for the observed precursor emission is the ejection of stellar material following energy disposition into the stellar envelope as a result of gravity waves emitted during either neon/oxygen burning or a nuclear flash from silicon combustion.

The IceCube neutrino telescope at the South Pole has measured the atmospheric muon neutrino spectrum as a function of zenith angle and energy in the approximate 320 GeV to 20 TeV range, to search for the oscillation signatures of light sterile neutrinos. No evidence for anomalous $\nu_\mu$ or $\bar{\nu}_\mu$ disappearance is observed in either of two independently developed analyses, each using one year of atmospheric neutrino data. New exclusion limits are placed on the parameter space of the 3+1 model, in which muon antineutrinos would experience a strong MSW-resonant oscillation. The exclusion limits extend to $\mathrm{sin}^2 2\theta_{24} \leq$ 0.02 at $\Delta m^2 \sim$0.3$\mathrm{eV}^2$ at the 90\% confidence level. The allowed region from global analysis of appearance experiments, including LSND and MiniBooNE, is excluded at approximately the 99\% confidence level for the global best fit value of $|$U$_{e4}|^2$.

This Letter presents the result of a 3+1 sterile neutrino search using 10.7 years of IceCube data. We analyze atmospheric muon neutrinos that traverse the Earth with energies ranging from 0.5 to 100 TeV, incorporating significant improvements in modeling neutrino flux and detector response compared to earlier studies. Notably, for the first time, we categorize data into starting and through-going events, distinguishing neutrino interactions with vertices inside or outside the instrumented volume, to improve energy resolution. The best-fit point for a 3+1 model is found to be at $\sin^2(2\theta_{24}) = 0.16$ and $\Delta m^{2}_{41} = 3.5$ eV$^2$, which agrees with previous iterations of this study. The result is consistent with the null hypothesis of no sterile neutrinos with a p-value of 3.1\%.

Unsustainable behaviors are challenging to prevent due to their long-term, often unclear consequences. Games offer a promising solution by creating artificial environments where players can immediately experience the outcomes of their actions. To explore this potential, we developed EcoEcho, a GenAI-powered game leveraging multimodal agents to raise sustainability awareness. These agents engage players in natural conversations, prompting them to take in-game actions that lead to visible environmental impacts. We evaluated EcoEcho using a mixed-methods approach with 23 participants. Results show a significant increase in intended sustainable behaviors post-game, although attitudes towards sustainability only slightly improved. This finding highlights the potential of multimodal agents and action-consequence mechanics to effectively motivate real-world behavioral changes such as raising environmental sustainability awareness.