International and U.S. strategies and protocols have identified the need to develop rapid-response spacecraft reconnaissance capabilities as a priority to advance planetary defense readiness. A space-based reconnaissance response is recommended for potential impactors as small as 50 m, making these small objects the most likely to trigger a space-based response and the ones that drive the reconnaissance capabilities needed. Even following the successful completion of the NEO Surveyor mission and Rubin Observatory survey efforts, roughly half of the 50-m near-Earth object (NEO) population will remain undiscovered. As a result, 50-m impactors may not be found with long warning times, and a rapid-response flyby mission may be the only reconnaissance possible. To develop a robust flyby reconnaissance capability for planetary defense, four major requirements are defined for a demonstration mission. 1. Enable a flyby of greater than 90 percent of the potential asteroid threat population. 2. Demonstrate the flyby reconnaissance for a 50 m NEO. 3. Obtain the information needed to determine if and where it would impact the Earth. 4. Determine key properties of the asteroid to inform decision makers. As commonly noted in the planetary defense community, in planetary defense, you do not pick the asteroid, the asteroid picks you. Thus, a planetary defense flyby reconnaissance demonstration mission is not about just flying by an asteroid, but rather it is about developing a robust capability for the objects that are most likely to require a short-warning-time, space-based response.
Using $10,\!080^3$ grid simulations, we analyze scale-dependent alignment in driven, compressible, no net-flux magnetohydrodynamic turbulence. The plasma self-organizes into localized, strongly aligned regions. Alignment spans all primitive variables and their curls. Contrary to incompressible theory, velocity-magnetic alignment scales as $\theta(\lambda) \sim \lambda^{1/8}$, where $\lambda$ is the scale, suggesting a distinct three-dimensional eddy anisotropy and a much higher critical transition scale toward a reconnection-mediated cascade.
High-energy gamma rays have been detected in the region of LHAASO~J2108+5157 by the Fermi--LAT, HAWC and LHAASO-KM2A observatories. Cygnus~OB2 in Cygnus--X has been confirmed as the first strong stellar cluster PeVatron in our Galaxy. Thus, the star--forming regions Kronberger~80 and Kronberger~82, located in the field of LHAASO~J2108+5157, are analyzed to evaluate their stellar population and potential as associated PeVatron candidates. A distance of 10~kpc is adopted for Kronberger~80, while $\sim$1.6~kpc is estimated for Kronberger~82. Based on stellar densities, we report that their cluster radii are 2.5$\arcmin$ and 2.0$\arcmin$, while IR photometry reveals poor stellar content in massive O-type stars in both cases. From optical data, the estimation of cluster ages are 5--12.6~Myr and $\lesssim$ 5~Myr, respectively. We conclude that, in contrast to the stellar content of Cygnus~OB2, it is unlikely that Kronberger~80 and Kronberger~82 are PeVatrons associated with LHAASO~J2108+5157. The presence of a PeVatron in this region remains a mystery, but we confirm that the two Kronberger regions are star--forming regions undergoing formation rather than evolution.
The evolution of large-scale structure, galaxies and the intergalactic medium (IGM) during the Epoch of Reionization (EoR) can be probed by upcoming Line Intensity Mapping (LIM) experiments, which sample in redshift and direction without needing to resolve individual galaxies. We predict the intensity and sources of hydrogen H$\alpha$ emission, dominated by radiative recombination following ionization by UV from the same massive stars that caused reionization, down to redshift 4.6, using the largest fully-coupled, radiation-hydro simulation of galaxy formation and reionization to date, Cosmic Dawn (CoDa) III. We compute the mean intensity and Voxel Intensity Distribution (VID) vs. redshift, including the relative contributions of galaxies and IGM. This will provide mock data to guide and interpret LIM experiments such as NASA's SPHEREx and proposed Cosmic Dawn Intensity Mapper (CDIM).
Eclipsing binary systems (EBs), as foundational objects in stellar astrophysics, have garnered significant attention in recent years. These systems exhibit periodic decreases in light intensity when one star obscures the other from the observer's perspective, producing characteristic light curves (LCs). With the advent of the Transiting Exoplanet Survey Satellite (TESS), a vast repository of stellar LCs has become available, offering unprecedented opportunities for discovering new EBs. To efficiently identify such systems, we propose a novel method that combines LC data and generalized Lomb-Scargle periodograms (GLS) data to classify EBs. At the core of this method is CNN Attention LSTM Net (CALNet), a hybrid deep learning model integrating Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and an Attention Mechanism based on the Convolutional Block Attention Module (CBAM). We collected 4,225 EB samples, utilizing their 2-minute cadence LCs for model training and validation. CALNet achieved a recall rate of 99.1%, demonstrating its robustness and effectiveness. Applying it to TESS 2-minute LCs from Sectors 1 to 74, we identified 9,351 new EBs after manual visual inspection, significantly expanding the known sample size. This work highlights the potential of advanced deep-learning techniques in large-scale astronomical surveys and provides a valuable resource for further studies on EBs.