Locally authored papers of the past 5 days

The nearby ($d = 7.7$ pc) M4V star GJ~3378 is a target of our radial velocity (RV) exoplanet survey of fully convective stars in the Solar neighborhood with the near-infrared spectrograph HPF on the Hobby-Eberly Telescope (HET) at McDonald Observatory. Recently, Moutou et al.~(2024) announced the discovery of an $m\sin i = 5.26^{+0.94}_{-0.97} M_\oplus$ planet, GJ 3378b, with an orbital period of $24.73 \pm 0.06$ days, based on SPIRou RV data. Here, we present our HPF RVs for GJ 3378, as well as additional Doppler spectroscopy from the extreme precision NEID Spectrometer on the WIYN telescope at Kitt Peak National Observatory. We have analyzed the HPF+NEID RVs jointly with the published RVs from the CARMENES and SPIRou spectrometers. We present an orbital model for GJ 3378b that differs significantly from the Moutou et al.~solution. The joint RV model reduces the orbital period to $P = 21.45 \pm 0.01$d and the minimum mass to $m \sin i = 2.3 \pm 0.4 M_\oplus$. The shortened orbital distance remains within the conservative circumstellar liquid-water habitable zone (HZ), while the reduced mass increases the likelihood that the planet has a terrestrial composition. The revised planet properties place it near the ``cosmic shoreline," where planets in the HZs of M dwarfs may lose their atmospheres due to radiative stripping.

During the propagation of cosmic rays in the solar system, the Sun will block those particles and form a shadow whose position and depth are very important probe of the magnetic fields in the Sun's corona, in the interplanetary space, and the Earth's vicinity. In this work we carry out Monte Carlo studies of the Sun shadow, with a novel approach to take into account daily variations of the coronal and interplanetary magnetic field models. This treatment is suitable for studies of short-term variations of the Sun shadow, which become detectable by the Large High Altitude Air Shower Observatory (LHAASO) experiment. Two different coronal magnetic field models, the Potential Field Source Surface (PFSS) and Current Sheet Source Surface (CSSS) models, with observational time-varying photospheric magnetic fields as boundary conditions, are studied in this work. The interplanetary magnetic fields are then derived using the Parker spiral model based on the coronal ones. Furthermore, both the coronal and interplanetary magnetic field strengths are corrected using the Parker Solar Probe (PSP) measurements. We compare the simulation results with the daily observations of Sun shadow by LHAASO in 2021, and find that the CSSS model generally shows better consistency of the displacement of the Sun shadow than the PFSS model.

SS 433 is a microquasar whose relativistic jets precess every ~162 days, providing a laboratory for jet-interstellar medium interactions. We present a comprehensive analysis of 16 years of Fermi Large Area Telescope data (August 2008-September 2024) of the SS 433/W50 field, using events in the 0.3-300 GeV range and employing pulsar gating to mitigate contamination from the bright nearby pulsar PSR J1907+0602. We detect the GeV source 4FGL J1913.2+0512 (TS = 45, where TS denotes the likelihood-ratio Test Statistic) with a power-law spectrum (photon index 2.61 +- 0.08) and confirm a GeV excess at the western lobe (TS = 17). The eastern lobe of SS 433 is hinted at with lower significance. One additional GeV excess, Fermi J1909.6+0552 (TS = 20; TS = 28 over 0.1-300 GeV), located outside the SS 433 / W50 system, is revealed after gating. Exposure-corrected Lomb-Scargle periodograms and precessional phase-folded light curves show a ~162-day modulation in 4FGL J1913.2+0512. This periodicity is prominent during the first 10 years of the mission (2008-2018) but disappears thereafter, with the phase-folded flux concentrated in precessional phases 0.0-0.5. Over the full 16-year dataset, the modulation remains detectable but with reduced significance, consistent with dilution by the later non-modulated epoch. These results indicate that the efficiency and/or geometry of gamma-ray production in the SS 433 environment evolves on multi-year timescales.

Stellar streams from disrupted globular clusters are excellent probes of dark matter (DM) subhalos. Observed Milky Way streams display a remarkable diversity of features: spurs, gaps, kinks, cocoons, and density variations, many attributed to subhalo encounters. But how much of this diversity arises from the host itself? We simulate $\sim$15,000 globular cluster streams across four Milky Way-mass halos from the FIRE-2 cosmological simulations, evolved in basis function expansion potentials capturing the evolving disk, halo, and large-scale structure while excluding small-scale perturbers such as DM subhalos and giant molecular clouds. We find that roughly three quarters of streams develop complex features from the host potential, such as spurs, kinks, and cocoon-like envelopes. Even the smoothest streams exhibit 10--25\% width variation along their track and host overdensities and gaps at scales of ${\sim}2^\circ$, squarely in the $1^\circ$--$5^\circ$ range predicted for subhalo-induced gaps. Pericentric distance is the primary predictor of stream morphology, with ${\sim}15$ kpc separating smooth from disturbed streams and circular orbits beyond $\sim$20 kpc producing the smoothest streams. Only $\sim$70 out of $\sim$15,000 streams are free of detectable wiggles in the track at any scale. Analogs to observed features, such as the GD-1 spur and the ATLAS--Aliqa Uma kink, emerge even without the presence of subhalos. As next-generation surveys (LSST, Euclid, and Roman) resolve stream structure across hundreds of streams, the baseline established here, streams evolved without small-scale perturbers, becomes essential for extracting DM substructure constraints.

We present a generalized neutrino luminosity function for protons accelerated in the X-ray coronae of supermassive black holes in Seyfert-like galaxies. A major uncertainty in assessing the diffuse neutrino contribution of these systems is the underlying particle acceleration physics. We address this using a theoretical acceleration framework informed by plasma kinetic simulations, enabling a more self-consistent connection between coronal conditions, nonthermal proton populations, and neutrino production. In this picture, the neutrino luminosity depends primarily on the coronal X-ray luminosity and magnetization, and only weakly on black hole mass. We find that the cosmologically integrated emission from these systems can account for the sub-PeV diffuse extragalactic neutrino flux observed by IceCube. We further argue that, although diffusive confinement is relatively well understood, the magnetic field topology near black holes naturally allows for cosmic ray-driven outflows near the X-ray corona. Such outflows may accompany additional efficient neutrino production at the PeV-level and influence the dynamics of the innermost galactic environment.

The Sagittarius (Sgr) Stream is produced by the ongoing disruption of the Sgr dwarf spheroidal (dSph) galaxy and is thought to contain multiple wraps that were stripped during different pericentric passages. In this study, we introduce a neural-network--based method trained on $N$-body simulations to infer the stripping time of Sgr Stream stars directly from their phase-space coordinates. We combine spectroscopic data from SEGUE, APOGEE DR17, and LAMOST DR7 LRS with \textit{Gaia} EDR3 astrometry and distance estimates from the latest \texttt{StarHorse} catalog to identify high-quality Sgr Stream members. Applying our method to these stars, we measure a clear metallicity gradient with stripping time, well described by a linear relation with slope $\sim 0.3~\mathrm{dex~Gyr^{-1}}$. We further predict the stripping times of globular clusters previously suggested to originate from the Sgr dSph. M 54, Terzan 7, Terzan 8, and Arp 2 exhibit stripping times consistent with being currently bound to the Sgr remnant. Pal 12, Whiting 1, and NGC 2419 are inferred to have been stripped $0.9 \pm 0.1$, $1.1 \pm 0.2$, and $2.1 \pm 0.2$ Gyr ago, respectively. For NGC 4147 and NGC 5634, whose membership in the Sgr system remains uncertain, our analysis suggests stripping times of $1.1 \pm 0.4$ and $1.1 \pm 0.1$ Gyr, respectively, if they are ultimately confirmed as genuine Sgr members. These results demonstrate that data-driven models of dynamical stripping histories offer a promising approach for reconstructing the formation and chemical evolution of the Sgr Stream.

Nitrogen-bearing polycyclic aromatic hydrocarbons (N-PAHs) are key precursors to complex organic molecules in both the interstellar medium and the nitrogen-rich planetary atmospheres. Despite the recent detections of nitrogen-functionalized astromolecules, their formation pathways remain an open question. The discrepancies between their predicted and observed abundances point to unknown mechanism that govern their evolution in the astrophysical environments. Employing an ion trap technique and electronic structure calculations, we unravel multiple barrier-less reactions between gas-phase pyrimidine cations (C$_4$H$_4$N$_2^+$) and acetylene (C$_2$H$_2$) which form an hitherto unreported endocyclic- N-PAHs (C$_8$H$_7$N$_2^+$). The present measurements on reactions involving a double-nitrogen subsituted aromatic heterocycle have implications to the astrochemistry of both the Titan's atmosphere and interstellar medium.

The dynamical evolution of binary asteroid systems is deeply influenced by spin-orbit resonances. However, their domains of influence and mutual interactions remain elusive, in particular in the space where multiple resonant modes coexist. In such regimes, the standard single-resonance approach is intrinsically limited and fails to capture the true coupled dynamics. To overcome this, we develop a global Hamiltonian framework based on elliptic expansions of the spin-orbit coupling model, enabling the numerical construction of comprehensive resonant networks. Concentrating on a representative synchronous region that encompasses synchronous spin-orbit, spin-spin, spin-orbit-spin, and doubly synchronous resonances, we study the dynamical boundaries of different resonant modes in a systematical manner. Crucially, we identify a secondary resonance structure arising from the strong nonlinear coupling between the synchronous resonances of the primary and secondary asteroids. Ultimately, this study provides a reliable parameter-space atlas, which is helpful for predicting the long-term evolutionary pathways of binary asteroid systems.

Large foundation models (FMs) are transforming Earth science by integrating heterogeneous multimodal data, such as multi-platform imagery, gridded reanalysis data, diverse geophysical and geochemical observations, and domain-specific text, to support tasks ranging from basic perception to advanced scientific discovery. This paper provides a unified review of Earth science foundation models (Earth FMs) through two complementary dimensions: depth, which traces the evolution of model capabilities from perception to multimodal reasoning and agentic scientific workflows, and breadth, which summarizes their expanding applications across the atmosphere, hydrosphere, lithosphere, biosphere, anthroposphere, and cryosphere, as well as coupled Earth system processes. Using this framework, we review representative multimodal Earth foundation models and compile more than 200 datasets and benchmarks spanning diverse Earth science tasks and modalities. We further discuss key challenges in multimodal data heterogeneity, scientific reliability and continual updating, scalability and sustainability, and the transition from foundation models to agentic and embodied Earth intelligence, and outline future directions toward more integrated, trustworthy, and actionable AI Earth scientists. Overall, this paper offers a structured roadmap for understanding the development of Earth foundation models from both capability depth and application breadth.

The origin of the large-scale poloidal magnetic field required to power relativistic jets in collapsars remains uncertain. While such a field may be inherited during PNS collapse, the efficiency of this process is unclear, motivating an in situ mechanism to generate poloidal fields out of the predominantly toroidal fields produced by stellar differential rotation. We present the first 3D general-relativistic magnetohydrodynamic collapsar simulations initialized with toroidal magnetic field profiles that closely follows those of pre-collapse stellar models. As the toroidal field in the disk becomes dynamically important, it seeds the dynamo, producing coherent poloidal magnetic loops that appear at $\sim \mathcal{O}(100)$ gravitational radii and are then advected inward along paths that may deviate from the disk midplane. The resulting poloidal fields thread the black hole (BH) and launch highly variable, wobbling relativistic jets on timescales of order seconds, with the onset depending on the initial magnetic field and the plasma circularization radius. Although the jets are highly variable and misaligned with the BH spin axis, they sustain $\gtrsim 10^{50}$ erg s$^{-1}$, comparable to that inferred for long gamma-ray bursts (LGRB). We identify magnetic-flux inversions driven by the stochastic dynamo, leading to the formation of striped jets that could be imprinted in LGRB light curves. These results demonstrate that the accretion0disk dynamo provides a robust pathway for jet production in collapsars across a broad range of progenitors.

High-precision ground-based observations of the inner corona (1.05-2.0 R_sun) are fundamentally constrained by instrumental stray light, particularly the additive background from dynamic dust accumulation on the objective lens. To address this issue, we propose a correction method for the Spectral Imaging Coronagraph (SICG) based on dual-path real-time monitoring and forward physical modeling. By simultaneously imaging the objective lens surface, we obtain deterministic prior information on dust distribution. We construct a physical point-spread function using optical defocus parameters and reconstruct the nonuniform scattering background via convolution. Model parameters are retrieved through data-driven inversion constrained by polar coronal holes. The method demonstrates excellent robustness under varying contamination conditions. After correction, the rms noise in the polar background is reduced by approximately 67% on average, and the signal-to-background ratio improves by a factor of up to 3.7 under heavy contamination conditions. Comparisons with space-based Solar Dynamics Observatory/Atmospheric Imaging Assembly observations indicate that the corrected images recover the morphological structures of streamers with high fidelity. Further radial intensity analysis reveals that the correction process successfully restores the hydrostatic exponential decay characteristic of inner coronal radiation. The fitted decay coefficient corresponds to a plasma temperature of approximately 2.0 MK, consistent with the characteristic formation temperature of the Fe XIV 530.3 nm line. These results demonstrate that the method effectively eliminates the dominant systematic bias in ground-based observations, providing a reliable data foundation for high-precision coronal thermodynamic and dynamic research with the SICG.

Improving the accuracy of photometric redshifts (photo-$z$) is essential for reliable statistical studies of cosmology and galaxy evolution. However, missing photometric bands are a common observational challenge that can significantly degrade photo-$z$ estimation accuracy. In this work, we present a systematic evaluation of data imputation methods aimed at improving photo-$z$ performance. We benchmark a range of representative machine learning (ML) and deep learning (DL) architectures, identifying k-nearest neighbors (KNN) and the attention-based SAITS model as the leading performers. These models are then applied to China Space Station Survey Telescope (CSST) mock data to assess their performance under realistic observational conditions. Our results show that KNN yields the highest accuracy under idealized missing completely at random (MCAR) conditions with complete training sets, whereas robustness tests reveal that SAITS significantly outperforms KNN when training data is incomplete or when applied to realistic mixed-mechanism scenarios. We find that domain consistency between training and testing missingness patterns is a prerequisite for optimal performance, highlighting the risks of domain shift in supervised regression tasks. Furthermore, our analysis demonstrates that while general imputation models are highly effective for MCAR and missing at random (MAR) data, they are detrimental when applied to missing not at random (MNAR) data arising from flux limits, as statistical models fail to capture the physical information inherent in these non-detections. Consequently, we advocate for more sophisticated architectures capable of disentangling stochastic missingness from physical non-detections to address these distinct mechanisms individually.

Today, the observable cosmos exhibits a remarkable degree of isotropy and plausibly began in a nearly isotropic initial state. The properties of the Lorentzian Chern-Simons-Kodama (CSK) functional can provide an understanding of this initial state. In gravity with a positive cosmological constant, the Chern-Simons-Kodama (CSK) wavefunctional is an exact, chiral solution of the quantum gravitational constraints. We suggest that the normalizability and other issues with this functional, if interpreted as a proper state of quantum gravity, instead suggest an embedding into a larger quantum gravitational completion, and recast the CSK functional as a gravitational sphaleron with observationally desirable properties. By perturbing around the dominant de Sitter saddle of the wavefunctional with appropriate quantum gravitational boundary conditions, we find that for a closed universe the system is dynamically driven to spatial isotropy, while all anisotropic modes acquire positive quadratic curvature and are Gaussian-suppressed. The decay of this sphaleron therefore proceeds along an isotropic channel, providing an intrinsic quantum-gravitational mechanism for dynamical isotropization. This isotropization effect is robust under the inclusion of a slow-roll inflaton, and no analogous isotropic sphaleron exists for spatially flat or hyperbolic geometries. Taken together, these results recast the Lorentzian CSK functional as a chiral sphaleron that naturally prepares an approximately isotropic de Sitter background for inflation. Beyond this phenomenological study, we further suggest that the CSK functional can be understood as a boundary functional for a class of anomaly-free objects, including a complexified generalization of the Hartle-Hawking state.

Fast surrogate models for expensive simulations are now essential across the sciences, yet they typically operate as black boxes. We present \texttt{GWAgent}, a large language model (LLM)-based workflow that constructs interpretable analytic surrogates directly from simulation data. Surrogate modeling is well suited to agentic workflows because candidate models can be quantitatively validated against ground-truth simulations at each iteration. As a demonstration, we build a surrogate for gravitational waveforms from eccentric binary black hole mergers. We show that providing the agent with a physics-informed domain ansatz substantially improves output model accuracy. The resulting analytic surrogate attains a median Advanced LIGO mismatch of $6.9\times10^{-4}$ together with an $\sim 8.4\times$ speedup in waveform evaluation, surpassing both symbolic regression and conventional machine learning baselines. Beyond producing an accurate model, the workflow identifies compact physical structure from the learned representation. As an astrophysical application, we use \texttt{GWAgent} to analyze the eccentricity of GW200129 and infer $e_{20\mathrm{Hz}}=0.099^{+0.063}_{-0.044}$. These results show that validation-constrained agentic workflows can produce accurate, fast, and interpretable surrogates for scientific simulations and inference.

Tensions often arise between different datasets in cosmology, and consistency tests can serve as a powerful tool for diagnosing potential issues. The density-shear Baryon Acoustic Oscillations (GI BAO) are the imprint of the BAO feature on the shear field induced by the large-scale tidal field. We highlight that GI BAO can provide a robust consistency check for the density BAO, shear measurement, and alignment model. Failure of this check hints at systematics in any of these parts. As an illustration, we present the first GI BAO measurement on photometric data, using the DES Y3 dataset. We find the GI BAO constraint on the BAO scale dilation parameter $\alpha $ to be $ 0.966 \pm 0.252 $ (1$\sigma$), in good agreement with the density BAO constraint, $ 0.966 \pm 0.037 $, thereby validating the density BAO, shear measurement, and the linear alignment model. Furthermore, we argue that combining the density BAO with the GI BAO yields results that are more resilient to systematic effects. Thanks to the massive data volumes of stage IV surveys, the GI BAO will play an even more prominent role as a consistency check.