Locally authored papers of the past 5 days

Hosking & Schekochihin (2021, Phys. Rev. X 11, 041005) have proposed that statistically isotropic decaying MHD turbulence without net magnetic helicity conserves the mean square fluctuation level of magnetic helicity in large volumes -- or, equivalently, the integral over space of the two-point correlation function of the magnetic-helicity density, denoted $I_H$. Formally, the conservation and gauge invariance of $I_H$ require the vanishing of certain boundary terms related to the strength of long-range spatial correlations. These boundary terms represent the ability (or otherwise) of the turbulence to organise fluxes over arbitrarily large distances to deplete or enhance fluctuations of magnetic helicity. In this work, we present a theory of these boundary terms, employing a methodology analogous to that of Batchelor & Proudman (1956, Philos. Trans. R. Soc. A 248, 369) to determine the relevant asymptotic forms of correlation functions. We find that long-range correlations of sufficient strength to violate the conservation of $I_H$ cannot develop dynamically if the evolution equation for the magnetic vector potential is chosen to be local in space. Likewise, we find that such correlations cannot develop for a wide class of gauge choices that make this equation non-local (including the Coulomb gauge). Nonetheless, we also identify a class of non-local gauge choices for which correlations that are sufficiently strong to violate the conservation of $I_H$ do appear possible. We verify our theoretical predictions for the case of the Coulomb gauge with measurements of correlation functions in a high-resolution numerical simulation.

To support the development of the data processing pipeline and the scientific performance assessment for the Cool Planet Imaging Coronagraph (CPI-C) on the China Space Station Telescope (CSST), we have developed the end-to-end instrument simulation program, CPISM. This paper details the core modules of CPISM that simulate the CPI-C instrument, focusing on the simulation of the high-contrast imaging optical system and the visible-band science camera. We modeled key optical components, such as the transmission apodizing filter, the wavefront corrector, and the focal plane mask using the HCIPy package. A $10^{-8}$ contrast dark hole region, consistent with design specifications, was simulated using the Electric Field Conjugation (EFC) optimization method, and broadband observation effects were considered. For the science camera, which is an electron multiplying charge-coupled device (EMCCD), we established a detailed model encompassing photon collection, charge transfer, electron multiplication (EM), and readout processes, based on test data. This model simulates complex instrumental features including dark current, charge transfer efficiency, clock-induced charge, multiplication noise factor, and various readout effects like striping and drift. We also proposed and validated an improved statistical model for the EM process to enhance simulation efficiency. CPISM can generate simulated images containing rich instrumental details, closely similar to the expected real observational data, thus laying the foundation for the development and verification of CPI-C data processing algorithms and preparations for future scientific research.

The adsorption of volatile molecules onto dust grain surfaces fundamentally influences dust-related processes, including condensation of gas-phase molecules, dust coagulation, and planet formation in protoplanetary disks. Using advanced {\it ab-initio} density functional theory with r$^2$SCAN+rVV10 van der Waals functionals, we calculate adsorption energies of H$_2$, H$_2$O, and CO on carbonaceous (graphene, amorphous carbon) and silicate (MgSiO$_3$) surfaces. Results reveal fundamentally different adsorption mechanisms: weak physisorption on carbonaceous surfaces ($|\Delta\epsilon_{\rm ad}|\sim 0.1-0.2~{\rm eV}$) versus strong chemisorption on silicates ($|\Delta\epsilon_{\rm ad}|\sim 0.5-1.5~{\rm eV}$) via coordination bonds. Kinetic Monte Carlo simulations incorporating these energies demonstrate divergent surface evolution: carbonaceous grains exhibit distinct condensation radius compared to silicates, while the cocrystal of H$_2$O and CO significantly increases the desorption temperature of CO. The actual radii of gas-phase molecule depletion could thus be a comprehensive result of temperatures, chemical compositions, and even evolution tracks. Meanwhile, silicates maintain chemisorbed molecular coatings throughout most disk regions. Such dichotomy in surface coverage could also provide a natural mechanism for carbon depletion in inner planetary systems.

The `kernel' of the classical Kuiper belt was discovered by Petit et al. (2011) as a visual overdensity of objects with low ecliptic inclinations and eccentricities at semimajor axes near 44 AU. This raises the question - are there other structures present in the classical Kuiper belt? If there are, clustering algorithms applied to orbits transformed into free elements may yield the best chance of discovery. Here, we derive barycentric free orbital elements for objects in the classical Kuiper belt, and use the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to identify a new structure, which we dub the inner kernel, located at $a \sim 43 \mathrm{\; AU}$ just inward of the kernel ($a \sim 44 \mathrm{\; AU}$), which we also recover. It is yet unclear whether the inner kernel is an extension of the kernel or a distinct structure. Forthcoming observations, including those by the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) may provide further evidence for the existence of this structure, and perhaps resolve the question of whether there are two distinct structures.

We present improved cosmological constraints from a re-analysis of the Dark Energy Survey (DES) 5-year sample of Type Ia supernovae (DES-SN5YR). This re-analysis includes an improved photometric cross-calibration, recent white dwarf observations to cross-calibrate between DES and low redshift surveys, retraining the SALT3 light curve model and fixing a numerical approximation in the host galaxy colour law. Our fully recalibrated sample, which we call DES-Dovekie, comprises $\sim$1600 likely Type Ia SNe from DES and $\sim$200 low-redshift SNe from other surveys. With DES-Dovekie, we obtain $\Omega_{\rm m} = 0.330 \pm 0.015$ in Flat $\Lambda$CDM which changes $\Omega_{\rm m}$ by $-0.022$ compared to DES-SN5YR. Combining DES-Dovekie with CMB data from Planck, ACT and SPT and the DESI DR2 measurements in a Flat $w_0 w_a$CDM cosmology, we find $w_0 = -0.803 \pm 0.054$, $w_a = -0.72 \pm 0.21$. Our results hold a significance of $3.2\sigma$, reduced from $4.2\sigma$ for DES-SN5YR, to reject the null hypothesis that the data are compatible with the cosmological constant. This significance is equivalent to a Bayesian model preference odds of approximately 5:1 in favour of the Flat $w_0 w_a$CDM model. Using generally accepted thresholds for model preference, our updated data exhibits only a weak preference for evolving dark energy.

We address the origin of the Little Red Dots (LRDs) seen by JWST at cosmic morning ($z \!=\! 4 \!-\! 8$) as compact stellar systems with over-massive black holes (BHs). We propose that LRDs form naturally after feedback-free starbursts (FFB) in thousands of star clusters and following wet compaction. Analytically, we show how the clusters enable efficient dry migration of stars and BHs to the galaxy center by two-body segregation and dynamical friction against the disk. The clusters merge to form compact central clusters as observed. Mutual tidal stripping does not qualitatively affect the analysis. The young, rotating clusters are natural sites for the formation of BH seeds via rapid core collapse. The migrating clusters carry the BH seeds, which merge into central super-massive BHs (SMBHs). Compactions are required to deepen the potential wells such that the SMBHs are retained after post-merger gravitational-wave recoils, locked to the galaxy centers. Using cosmological simulations at different epochs, with different codes and physical recipes, we evaluate the additional growth of LRD-matching compact central stellar systems by global compaction events. Adding to the dry growth by cluster mergers, the compactions can increase the escape velocities to retain the SMBHs. The LRDs appear at $z \!\sim\! 8$, after the formation of FFB clusters, and disappear after $z \!\sim\! 4$ when the stellar mass is above $10^9 M_\odot$ by growing post-compaction blue disks around the nuclear LRDs. The LRD abundance is expected to be $\sim\! 10^{-5} \!-\! 10^{-4}\,{\rm Mpc}^{-3}$, increasing from $z \!\sim\! 4$ to $z\!\sim\! 8$.

We present a modified outflow model and its application to constrain ionized outflow properties of active galactic nuclei (AGNs). By adding a rotating disk component to the biconical outflow model of Bae & Woo, we find that models with a rotating disk require faster launching velocities ($\lesssim$ 1500 km s$^{-1}$) than outflow-only models to be consistent with the observed gas kinematics of local type 2 AGNs. We perform Monte Carlo simulations to reproduce the observed distribution of gas kinematics of a large sample ($\sim$ 39,000), constraining the launching velocity and opening angle. While the launching velocity is moderate for the majority of the local AGNs, the notable cases of 2 - 5 % show strong outflows with $V_{max} \sim 1000-1500$ km s$^{-1}$. By examining the seeing effect based on the mock integral field unit data, we find that the outflow sizes measured based on velocity widths tend to be overestimated when the angular size of the outflow is comparable to or smaller than the seeing. This result highlights the need for more careful treatments of the seeing effect in the outflow size measurement, yet it still supports the lack of global feedback by gas outflows for local AGNs.

Flexible and accurate interpolation schemes using machine learning could be of great benefit for many use-cases in numerical simulations and post-processing, such as temporal upsampling or storage reduction. In this work, we adapt the physics-informed token transformer (PITT) network for multi-channel data and couple it with Fourier neural operator (FNO). The resulting PITT FNO network is trained for interpolation tasks on a dataset governed by the Euler equations. We compare the performance of our machine learning model with a linear interpolation baseline and show that it requires $\sim6-10$ times less data to achieve the same mean square error of the interpolated quantities. Additionally, PITT FNO has excellent mass and energy conservation as a result of its physics-informed nature. We further discuss the ability of the network to recover fine detail using a spectral analysis. Our results suggest that loss of fine details is related to the decreasing correlation time of the data with increasing Fourier mode which cannot be resolved by simply increasing Fourier mode truncation in FNO.

In our previous work, we applied the ICCF-Cut method to the continuum reverberation mapping (CRM) of six active galactic nuclei (AGNs) based on the published Swift data. Extending this work, we perform a systematic AGN CRM study utilizing the Swift archive. We enlarge our sample with eight additional AGNs at $z<0.05$ with high-cadence ($<3$ days) and multiband photometric observations. Time series analysis of these light curves shows two main results: (1) The interband lags are broadly consistent with $\tau \propto \lambda^{4/3}$, while the average interband lags are larger than those predicted by the standard thin accretion disk model. (2) For most targets, there exists a $U$ band lag excess, which is probably due to the diffuse continuum emission from the broad-line region (BLR). We employ the ICCF-Cut method to extract the possible diffuse continuum component from the $U$ band light curves and calculate the diffuse continuum lags ($\tau_{\rm cut}$), which are generally consistent with the lags ($\tau_{\rm jav}$) derived by the JAVELIN Photometric Reverberation Mapping Model. Further analysis with our sample indicates a positive correlation between the diffuse continuum region size and the BLR size ($R_{\rm DCR}-R_{\rm BLR}$ relation), as well as another correlation with the luminosity ($R_{\rm DCR}-L$ relation). These findings provide further evidence for a significant contribution of diffuse continuum emission from the BLR to the AGN continuum lags.

In September 2017, a high-energy neutrino event detected by the IceCube Neutrino Observatory (IceCube-170922A) was associated, at the $3\sigma$ level, with a gamma-ray flare from the blazar TXS 0506+056. Cosmic rays that are accelerated in astrophysical sources can escape from their jets and interact with background radiation fields. Interactions with the extragalactic background light can produce pions and hence neutrinos, while interactions with the cosmic microwave background predominantly drive inverse Compton scattering, contributing to electromagnetic cascades in intergalactic space. The resulting secondary gamma-ray emission can be detected with high-energy gamma-ray telescopes. Here, we report on a new search for such cosmogenic cascade emission from the blazar TXS 0506+056, using a combined data set from the Fermi-Large Area Telescope and VERITAS. We compare the gamma-ray spectrum and neutrino observations with the predictions of cosmic-ray induced cascades in intergalactic space. The observed gamma-ray spectrum is modeled as a combination of the primary spectrum and the cascade spectrum. We apply a Monte Carlo simulation with a $\Delta\chi^2$-based likelihood analysis to jointly determine the best-fit parameters of a proton emission spectrum describing the data and derive constraints on the proton escape luminosity. Assuming a log-parabola primary photon spectrum, we find consistency with a proton injection spectral index of $\alpha_{p} \simeq 2.0$ and a cutoff energy of $E_{p,\text{max}} \simeq 1.3 \times 10^{16}$ eV, and constrain the isotropic proton escape luminosity to $1 \times 10^{44}$ erg s$^{-1}$ $\lesssim L_{p, esc} \lesssim 3 \times 10^{45}$ erg s$^{-1}$ at the 90 % confidence level.

We use JWST/NIRCam and MIRI imaging acquired by the Feedback in Emerging extrAgalactic Star clusTers (FEAST) program along with archival HST imaging to map ionized gas (Pa$\alpha$, Br$\alpha$, and H$\alpha$) and Polycyclic Aromatic Hydrocarbon (PAH) emission (3.3 and 7.7 $\mu$m) across a sample of four nearby galaxies (NGC 5194, 5236, 628, and 4449). These maps are utilized to calibrate the PAH features as star formation rate (SFR) indicators in 40 pc size regions around massive emerging young star clusters (eYSCs). We find a tight, sub-linear (power-law exponent, $\alpha{\,}{\sim}{\,}0.8$) relation between the PAH luminosities (3.3 and 7.7 $\mu$m) and SFR (extinction corrected Pa$\alpha$) in near solar metallicity environments. PAH destruction in more intense ionizing environments and/or variations in the age of our sources may drive the deviation from a linear relation. In the metal-poor environment of NGC 4449 (${\sim}$1/3 Z$_{\odot}$), we see substantial deficits in the PAH feature strengths at fixed SFR and significantly higher scatter in the PAH-SFR relations. We determine that the 3.3/7.7 $\mu$m PAH luminosity ratio increases towards lower metallicity environments. This is interpreted as a result of a shift in the size distribution towards smaller PAHs at lower metallicities, possibly due to inhibited grain growth. Focusing on the regions in NGC 4449, we observe a decreasing 3.3/7.7 $\mu$m ratio towards higher SFR, which could indicate that small PAHs are preferentially destroyed relative to larger PAHs in significantly sub-solar metallicity conditions. We estimate that ${\sim}$2/3 of the PAH emission in typical local star-forming galaxies is excited by older stars and unrelated to recent ($<$10 Myr) star formation.

We developed a Python package GEHONG to mock the three-dimensional spectral data cube under the observation of an ideal telescope for the Integral Field Spectrograph of the Chinese Space Station Telescope (CSST-IFS). This package can generate one-dimensional spectra corresponding to local physical properties at specific positions according to a series of two-dimensional distributions of physical parameters of target sources. In this way, it can produce a spatially resolved spectral cube of the target source. Two-dimensional distributions of physical parameters, including surface brightness, stellar population, and line-of-sight velocity, can be modeled using the parametric model or based on real observational data and numerical simulation data. For the generation of one-dimensional spectra, we have considered four types of spectra, including the stellar continuum spectra, ionized gas emission lines, AGN spectra, and stellar spectra. That makes GEHONG able to mock various types of targets, including galaxies, AGNs, star clusters, and HII regions.

The Multi-Channel Imager (MCI), one of the instruments aboard the China Survey Space Telescope (CSST), is designed to simultaneously observe the sky in three filters, covering wavelengths from the near-ultraviolet (NUV) to the near-infrared (NIR). With its large field of view ($7.5^{\prime}\times7.5^{\prime}$), MCI is particularly well-suited for observing galaxy clusters, providing a powerful tool for investigating galaxy evolution, dark matter and dark energy through gravitational lensing. Here we present a comprehensive simulation framework of a strong lensing cluster as observed by MCI, aiming to fully exploit its capabilities in capturing lensing features. The framework simulates a strong lensing cluster from the CosmoDC2 catalog, calculating the gravitational potential and performing ray-tracing to derive the true positions, shapes and light distribution of galaxies within the cluster field. Additionally, the simulation incorporates intra-cluster light (ICL) and spectral energy distributions (SEDs), enabling further strong lensing analyses, such as ICL seperation from galaxy light and mass reconstruction combining strong and weak lensing measurements. This framework provides a critical benchmark for testing the MCI data pipeline and maximizing its potential in galaxy cluster research.

This study presents a comprehensive end-to-end simulation analysis of the optical imaging performance of the China Survey Space Telescope (CSST) under in-orbit conditions. An integrated system model incorporating five static and two dynamic error sub-models was established. Wavefront errors were calculated for each sub-model and compared to the integrated system error to quantify the individual contributions to image degradation. At the detector level, wavefront error, point spread function (PSF), and ellipticity were evaluated across the full field of view (FOV). The average radius of 80\% encircled energy (REE80) of the PSF under full-error conditions was determined for 25 field points, yielding a value of 0.114 arcseconds. Furthermore, the calculations indicate a correlation between the wavefront distribution and the ellipticity distribution within the optical system. By optimizing the wavefront distribution, it is possible to adjust the ellipticity distribution of the PSF across the full FOV. The end-to-end simulation approach adopted in this paper provides a theoretical foundation for improving the image quality in large-aperture, off-axis space telescopes.

The Chinese Space Station Survey Telescope (CSST) is a flagship space-based observatory. Its main survey camera is designed to conduct high spatial resolution near-ultraviolet to near-infrared imaging and low-resolution spectroscopic surveys. To maximize the scientific output of CSST, we have developed a comprehensive, high-fidelity simulation pipeline for reproducing both imaging and spectroscopic observations. This paper presents an overview of the simulation framework, detailing its implementation and components. Built upon the GalSim package and incorporating the latest CSST instrumental specifications, our pipeline generates pixel-level mock observations that closely replicate the expected instrumental and observational conditions. The simulation suite integrates realistic astrophysical object catalogs, instrumental effects, point spread function (PSF) modeling, and observational noises to produce accurate synthetic data. We describe the key processing stages of the simulation, from constructing the input object catalogs to modeling the telescope optics and detector responses. Furthermore, we introduce the most recent release of simulated datasets, which provide a crucial testbed for data processing pipeline developments, calibration strategies, and scientific analyses, ensuring that CSST will meet its stringent requirements. Our pipeline serves as a vital tool for optimizing CSST main survey strategies and ensuring robust cosmological measurements.

The high-precision {\it Gaia} data release 3 (DR3) enables the discovery of numerous open clusters in the Milky Way, providing an excellent opportunity to search for blue straggler stars in open clusters and investigate their formation and evolution in these environments. Using the member stars from literature open cluster catalogs, we visually inspected the color-magnitude diagram (CMD) of each cluster and selected cluster candidates that potentially host blue stragglers. We then reassessed cluster memberships using the {\tt pyUPMASK} algorithm with {\it Gaia} DR3 and performed isochrone fitting to derive physical parameters for each cluster, including age, distance modulus, mean reddening, and metallicity. Finally, we empirically identified straggler stars based on their positions relative to the best-fitting isochrone, zero-age main sequence (ZAMS), and equal-mass binary sequence on the CMD. In total, we identified 272 new straggler stars in 99 open clusters, comprising 153 blue stragglers, 98 probable blue stragglers, and 21 yellow stragglers. Compared to the reported blue straggler catalogs based on earlier {\it Gaia} data, our results increase the number of open clusters with stragglers in the Milky Way by 22.2\%, and the total number of blue stragglers by 11.2\%.

The detection of GW231123, a gravitational-wave (GW) event with exceptionally massive and rapidly spinning black holes, suggests the possible formation within an active galactic nucleus (AGN) disk, which provides a favorable environment for potentially generating an observable electromagnetic (EM) counterpart. We conduct a search for such a counterpart by crossmatching the GW localization with a comprehensive catalog of AGN flares from the Zwicky Transient Facility. Our analysis yields six plausible optical flare candidates that are spatially and temporally coincident with GW231123 and exhibit significant deviations from their AGN baseline flux. Although these candidates represent a crucial first step, their true nature remains inconclusive. Confirming any one of these flares via future observations would provide a landmark validation of the AGN formation channel and unlock the multi-messenger potential of this extraordinary merger.

Dark matter (DM) halos form hierarchically in the Universe through a series of merger events. Cosmological simulations can represent this series of mergers as a graph-like ``tree'' structure. Previous work has shown these merger trees are sensitive to cosmology simulation parameters, but as DM structures, the outstanding question of their sensitivity to DM models remains unanswered. In this work, we investigate the feasibility of deep learning methods trained on merger trees to infer Warm Dark Matter (WDM) particles masses from the DREAMS simulation suite. We organize the merger trees from 1,024 zoom-in simulations into graphs with nodes representing the merger history of galaxies and edges denoting hereditary links. We vary the complexity of the node features included in the graphs ranging from a single node feature up through an array of several galactic properties (e.g., halo mass, star formation rate, etc.). We train a Graph Neural Network (GNN) to predict the WDM mass using the graph representation of the merger tree as input. We find that the GNN can predict the mass of the WDM particle ($R^2$ from 0.07 to 0.95), with success depending on the graph complexity and node features. We extend the same methods to supernovae and active galactic nuclei feedback parameters $A_\text{SN1}$, $A_\text{SN2}$, and $A_\text{AGN}$, successfully inferring the supernovae parameters. The GNN can even infer the WDM mass from merger tree histories without any node features, indicating that the structure of merger trees alone inherits information about the cosmological parameters of the simulations from which they form.

In most particle acceleration mechanisms, the maximum energy of the cosmic rays can achieve is charge dependent. However, the observational verification of such a fundamental relation is still lack due to the difficulty of measuring the spectra of individual particles from one (kind of) source(s) up to very high energies. This work reports direct measurements of the carbon, oxygen, and iron spectra from ~ 20 gigavolts to ~ 100 teravolts (~ 60 teravolts for iron) with 9 years of on-orbit data collected by the Dark Matter Particle Explorer (DAMPE). Distinct spectral softenings have been directly detected in these spectra for the first time. Combined with the updated proton and helium spectra, the spectral softening appears universally at a rigidity of ~ 15 teravolts. A nuclei mass dependent softening is rejected at a confidence level of > 99.999%. Taking into account the correlated structures at similar energies in the large-scale anisotropies of cosmic rays, one of the most natural interpretations of the spectral structures is the presence of a nearby cosmic ray source. In this case, the softening energies correspond to the acceleration upper limits of such a source, forming the so-called Peters cycle of the spectra. The results thus offer observational verification of the long-standing prediction of the charge-dependent energy limit of cosmic ray acceleration.