Substructure in the interstellar medium (ISM) is crucial for establishing the correlation between star formation and feedback and has the capacity to significantly perturb stellar orbits, thus playing a central role in galaxy dynamics and evolution. Contemporary surveys of gas and dust emission in nearby galaxies resolve structure down to $\sim 10\,$pc scales, demanding theoretical models of ISM substructure with matching fidelity. In this work, we address this need by quantitatively characterizing the gas density in state-of-the-art MHD simulations of disk galaxies that resolve pc to kpc scales. The TIGRESS-NCR framework we employ includes sheared galactic rotation, self-consistent star formation and feedback, and nonequilibrium chemistry and cooling. We fit simple analytic models to the one-point spatial, two-point spatial, and two-point spatio-temporal statistics of the surface density fluctuation field. We find that for both solar neighborhood and inner-galaxy conditions, (i) the surface density fluctuations follow a log-normal distribution, (ii) the linear and logarithmic fluctuation power spectra are well-approximated as power laws with indices of $\approx -2.2$ and $\approx -2.8$ respectively, and (iii) lifetimes of structures at different scales are set by a combination of feedback and effective pressure terms. Additionally, we find that the vertical structure of the gas is well-modeled by a mixture of exponential and sech$^2$ profiles, allowing us to link the surface density statistics to those of the volume density and gravitational potential. We provide convenient parameterizations for incorporating realistic ISM effects into stellar-dynamical studies and for comparison with multi-wavelength observations.
We present a novel experiment to investigate the spectroscopic factor of the $^{15}$C ground state for the first time using single-neutron $removal$ transfer reactions on $^{15}$C. Two consistent spectroscopic factors were derived from the (p, d) and (d, t) reactions, which were subsequently used to deduce the $^{14}$C(n, $\gamma$)$^{15}$C reaction cross section and the corresponding stellar reaction rate. A typical cross section of (3.89 $\pm$ 0.76) $\mu$b is determined at $E_\mathrm{_{c.m.}}$ = 23.3 keV. At the temperature range of 0.01-4 GK, our new reaction rate is 2.4-3.7 times higher than that of the first direct measurement and 20\%-25\% lower than that of the most recent direct measurement, respectively. Moreover, it is interesting that we can associate a long-standing nuclear structure issue, i.e., the so-called ``quenching'' effect, with this astrophysically relevant reaction. Finally, motivated by astrophysical interests of this reaction decades ago, implications of our new rate on several astrophysical problems are evaluated using state-of-the-art theoretical models. Our calculations demonstrate that the abundances of $^{14}$N and $^{15}$N can be enhanced in the inner regions of asymptotic giant branch (AGB) stars, though with minimal impact on the chemical compositions of the interstellar medium. In the inhomogeneous Big Bang nucleosynthesis, the updated reaction rate can lead to a $\sim 20\%$ variation in the final yields of $^{15}$N in neutron rich regions. For the $r$-process in the core-collapse supernovae, a slight difference of $\sim 0.2\%$ in the final abundances of heavy elements with $A > 90$ can be found by using our new rate.
Improving gamma-hadron separation is one of the most effective ways to enhance the performance of ground-based gamma-ray observatories. With over a decade of continuous operation, the High-Altitude Water Cherenkov (HAWC) Observatory has contributed significantly to high-energy astrophysics. To further leverage its rich dataset, we introduce a machine learning approach for gamma-hadron separation. A Multilayer Perceptron shows the best performance, surpassing traditional and other Machine Learning based methods. This approach shows a notable improvement in the detector's sensitivity, supported by results from both simulated and real HAWC data. In particular, it achieves a 19\% increase in significance for the Crab Nebula, commonly used as a benchmark. These improvements highlight the potential of machine learning to significantly enhance the performance of HAWC and provide a valuable reference for ground-based observatories, such as Large High Altitude Air Shower Observatory (LHAASO) and the upcoming Southern Wide-field Gamma-ray Observatory (SWGO).
We present the discovery and validation of a super-Earth planet orbiting the M dwarf star TOI-1846 (TIC 198385543). The host star(Kmag = 9.6)is located 47 pc away and has a radius of Rs=0.41+/-0.01R_Sun,a mass of Ms=0.40+/-0.02M_Sun and an effective temperature of Teff=3568+/-44K. Our analyses are based on joint modelling of TESS photometry and ground-based multi-color photometric data. We also use high-resolution imaging and archival images, as well as statistical validation techniques to support the planetary system nature. We find that TOI-1846b is a super-Earth sized planet with radius of Rp=1.79+/-0.07R_Earth and a predicted mass of Mp=4.4+1.6-1.0M_Earth (from the Chen & Kipping relation) on a 3.9 d orbit, with an equilibrium temperature of Teq=589+/-20K (assuming a null Bond Albedo) and an incident flux of Sp=17.6+/-2.0S_Earth. Based on the two RV measurements obtained with the TRES spectrograph and high-resolution imaging, a non-planetary transiting companion is excluded. With a radius of ~1.8R_Earth, TOI-1846b is within the sparsely populated radius range around 2R_Earth known as the radius gap (or radius valley). This discovery can contribute to refining the precise location of the radius valley for small planets orbiting bright M dwarfs, thereby enhancing our understanding of planetary formation and evolution processes.