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.