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The masses of Population III stars are largely unconstrained since no simulations exist that take all relevant primordial star formation physics into account. We perform the first suite of radiation magnetohydrodynamics (RMHD) simulations of Population III star formation, with the POPSICLE project. Compared to control simulations that only include magnetic fields (MHD), protostellar ionizing and dissociating feedback, or neither, the RMHD simulation best resembles the MHD simulation during the earliest stages of collapse and star formation. In $5000\,\rm{yrs}$, the mass of the most massive star is $65\,\rm{M_{\odot}}$ in the RMHD simulation, compared to $120\,\rm{M_{\odot}}$ in simulations without magnetic fields. This difference arises because magnetic fields act against gravity, suppress mass transport, and reduce compressional heating. The maximum stellar mass of Population III stars is thus already limited by magnetic fields, even before accretion rates drop to allow significant protostellar radiative feedback. Following classical main sequence stellar evolution with MESA reveals that it is difficult to create Population III stars with masses larger than $600\,\rm{M_{\odot}}$ in typical dark matter minihaloes at $z \gtrsim 20$, with maximum stellar masses $\sim 100\,\rm{M_{\odot}}$ more likely due to expected negative feedback from both magnetic fields and stellar radiation. This work lays the first step in building a full physics-informed mass function of Population III stars., Comment: 5 pages + appendix, submitted to MNRAS

Crop row detection has garnered significant interest due to its critical role in enabling navigation in GPS-denied environments, such as under-canopy agricultural settings. To address this challenge, we propose RowDetr, an end-to-end neural network that utilizes smooth polynomial functions to delineate crop boundaries in image space. A novel energy-based loss function, PolyOptLoss, is introduced to enhance learning robustness, even with noisy labels. The proposed model demonstrates a 3% improvement over Agronav in key performance metrics while being six times faster, making it well-suited for real-time applications. Additionally, metrics from lane detection studies were adapted to comprehensively evaluate the system, showcasing its accuracy and adaptability in various scenarios., Comment: Code will be open sourced upon publication

We model emissivities of the HCN and CO $J=1-0$ transitions using measured properties of clouds found in normal star forming galaxies and more extreme systems. These models are compared with observations of HCN and CO $J=1-0$ transitions. We combine these model emissivities with predictions of gravoturbulent models of star formation, explore the impact of excitation and optical depth on CO and HCN emission, and assess if observed HCN/CO ratios track the fraction of gravitationally-bound dense gas, $f_\mathrm{grav}$, in molecular clouds. Our modeled HCN/CO ratios and emissivities are consistent with measurements from observations. CO emission shows a range of optical depths across different environments, from optically thick in normal galaxies to moderately optically thin in extreme systems. HCN is only moderately optically thick, with significant subthermal excitation in both normal and extreme galaxies. We find an anticorrelation between HCN/CO and $f_\mathrm{grav}$ as predicted by gravoturbulent models of star formation. Instead this ratio tracks gas at moderate densities ($n>10^{3.5}\ \mathrm{cm}^{-3}$), which is below the standard dense gas threshold of $n>10^{4.5}\ \mathrm{cm}^{-3}$. Variations in CO emissivity depend strongly on optical depth, due to variations in the dynamics of the cloud gas. HCN emissivity depends more strongly on excitation, and thus does not directly track variations in CO emissivity. We conclude that a single line ratio, such as HCN/CO, will not consistently track the fraction of gravitationally-bound, star-forming gas if the critical density for star formation varies in molecular clouds. This work highlights important uncertainties that need to be considered when observationally applying an HCN conversion factor in order to estimate the dense (i.e. $n>10^{4.5}\ \mathrm{cm}^{-3}$) gas content in nearby galaxies., Comment: Accepted for publication in A&A

Turbulence, magnetic fields and radiation feedback are key components that shape the formation of stars, especially in the metal-free environments at high redshifts where Population III stars form. Yet no 3D numerical simulations exist that simultaneously take all of these into account. We present the first suite of radiation-magnetohydrodynamics (RMHD) simulations of Population III star formation using the adaptive mesh refinement (AMR) code FLASH. We include both turbulent magnetic fields and ionizing radiation feedback coupled to primordial chemistry, and resolve the collapse of primordial clouds down to few au. We find that dynamically strong magnetic fields significantly slow down accretion onto protostars, while ionizing feedback is largely unable to regulate gas accretion because the partially ionized \ion{H}{ii} region gets trapped near the star due to insufficient radiative outputs from the star. The maximum stellar mass in the HD and RHD simulations that only yield one star exceeds $100\,\rm{M_{\odot}}$ within the first $5000\,\rm{yr}$. However, in the corresponding MHD and RMHD runs, the maximum mass of Population III star is only $60\,\rm{M_{\odot}}$. In other realizations where we observe widespread fragmentation leading to the formation of Population III star clusters, the maximum stellar mass is further reduced by a factor of few due to fragmentation-induced starvation. We thus conclude that magnetic fields are more important than ionizing feedback in regulating the mass of the star, at least during the earliest stages of Population III star formation, in typical dark matter minihaloes at $z \approx 30$., Comment: 20 pages, 16 figures. Submitted to MNRAS

In recent years, the study of the Milky Way has significantly advanced due to extensive spectroscopic surveys of its stars, complemented by astroseismic and astrometric data. However, it remains disjoint from recent advancements in understanding the physics of the Galactic interstellar medium (ISM). This paper introduces a new model for the chemical evolution of the Milky Way that can be constrained on stellar data, because it combines a state-of-the-art ISM model with a Milky Way stellar disc model. Utilizing a dataset of red clump stars from APOGEE, known for their precise ages and metallicities, we concentrate on the last 6 billion years -- a period marked by Milky Way's secular evolution. We examine the oxygen abundance in the low-$\alpha$ disc stars relative to their ages and birth radii, validating or constraining critical ISM parameters that remain largely unexplored in extragalactic observations. The models that successfully reproduce the radius -- metallicity distribution and the age -- metallicity distribution of stars without violating existing ISM observations indicate a need for modest differential oxygen enrichment in Galactic outflows, meaning that the oxygen abundance of outflows is higher than the local ISM abundance, irrespective of outflow mass loading. The models also suggest somewhat elevated ISM gas velocity dispersion levels over the past 6 billion years compared to galaxies of similar mass. The extra turbulence necessary could result from energy from gas accretion onto the Galaxy, supernovae clustering in the ISM, or increased star formation efficiency per freefall time. This work provides a novel approach to constraining the Galactic ISM and outflows, leveraging the detailed insights available from contemporary Milky Way surveys., Comment: 20 pages, 9 figures. Accepted by MNRAS

Aphid infestations are one of the primary causes of extensive damage to wheat and sorghum fields and are one of the most common vectors for plant viruses, resulting in significant agricultural yield losses. To address this problem, farmers often employ the inefficient use of harmful chemical pesticides that have negative health and environmental impacts. As a result, a large amount of pesticide is wasted on areas without significant pest infestation. This brings to attention the urgent need for an intelligent autonomous system that can locate and spray sufficiently large infestations selectively within the complex crop canopies. We have developed a large multi-scale dataset for aphid cluster detection and segmentation, collected from actual sorghum fields and meticulously annotated to include clusters of aphids. Our dataset comprises a total of 54,742 image patches, showcasing a variety of viewpoints, diverse lighting conditions, and multiple scales, highlighting its effectiveness for real-world applications. In this study, we trained and evaluated four real-time semantic segmentation models and three object detection models specifically for aphid cluster segmentation and detection. Considering the balance between accuracy and efficiency, Fast-SCNN delivered the most effective segmentation results, achieving 80.46% mean precision, 81.21% mean recall, and 91.66 frames per second (FPS). For object detection, RT-DETR exhibited the best overall performance with a 61.63% mean average precision (mAP), 92.6% mean recall, and 72.55 on an NVIDIA V100 GPU. Our experiments further indicate that aphid cluster segmentation is more suitable for assessing aphid infestations than using detection models.

Using adaptive optics with the Multi-Unit Spectroscopic Explorer (MUSE) on the Very Large Telescope (VLT), the Middle Ages Galaxy Properties with Integral Field Spectroscopy (MAGPI) survey allows us to study the spatially resolved Universe at a crucial time of ~4 Gyr ago ($z$ ~ 0.3) when simulations predict the greatest diversity in evolutionary pathways for galaxies. We investigate the radial trends in the star formation (SF) activity and luminosity-weighted stellar ages as a function of offset from the star-forming main sequence (SFMS) for a total of 294 galaxies. Using both H$\alpha$ emission and the 4000 Angstrom break (i.e., D4000) as star formation rate (SFR) tracers, we find overall flat radial profiles for galaxies lying on and above the SFMS, suggestive of physical processes that enhance/regulate SF throughout the entire galaxy disc. However, for galaxies lying below the SFMS, we find positive gradients in SF suggestive of inside-out quenching. Placing our results in context with results from other redshift regimes suggests an evolution in radial trends at $z$ ~ 0.3 for SF galaxies above the SFMS, from uniformly enhanced SF at $z$ ~ 1 and $z$ ~ 0.3 to centrally enhanced SF at $z$ ~ 0 (when averaged over a wide range of mass). We also capture higher local SFRs for galaxies below the SFMS compared to that of $z$ ~ 0, which can be explained by a larger population of quenched satellites in the local Universe and/or different treatments of limitations set by the D4000-sSFR relation., Comment: 19 pages, 15 figures, 4 tables, accepted for publication in MNRAS

Machine learning and data mining techniques are utiized for enhancement of the security of any network. Researchers used machine learning for pattern detection, anomaly detection, dynamic policy setting, etc. The methods allow the program to learn from data and make decisions without human intervention, consuming a huge training period and computation power. This paper discusses a novel technique to predict an upcoming attack in a network based on several data parameters. The dataset is continuous in real-time implementation. The proposed model comprises dataset pre-processing, and training, followed by the testing phase. Based on the results of the testing phase, the best model is selected using which, event class which may lead to an attack is extracted. The event statistics are used for attack

Galaxy gas kinematics are sensitive to the physical processes that contribute to a galaxy's evolution. It is expected that external processes will cause more significant kinematic disturbances in the outer regions, while internal processes will cause more disturbances for the inner regions. Using a subsample of 47 galaxies ($0.27

We present the first compelling evidence of shock-heated molecular clouds associated with the supernova remnant (SNR) N49 in the Large Magellanic Cloud (LMC). Using $^{12}$CO($J$ = 2-1, 3-2) and $^{13}$CO($J$ = 2-1) line emission data taken with the Atacama Large Millimeter/Submillimeter Array, we derived the H$_2$ number density and kinetic temperature of eight $^{13}$CO-detected clouds using the large velocity gradient approximation at a resolution of 3.5$''$ (~0.8 pc at the LMC distance). The physical properties of the clouds are divided into two categories: three of them near the shock front show the highest temperatures of ~50 K with densities of ~500-700 cm$^{-3}$, while other clouds slightly distant from the SNR have moderate temperatures of ~20 K with densities of ~800-1300 cm$^{-3}$. The former clouds were heated by supernova shocks, but the latter were dominantly affected by the cosmic-ray heating. These findings are consistent with the efficient production of X-ray recombining plasma in N49 due to thermal conduction between the cold clouds and hot plasma. We also find that the gas pressure is roughly constant except for the three shock-engulfed clouds inside or on the SNR shell, suggesting that almost no clouds have evaporated within the short SNR age of ~4800 yr. This result is compatible with the shock-interaction model with dense and clumpy clouds inside a low-density wind bubble., Comment: 12 pages, 5 figures, 1 table, accepted for publication in The Astrophysical Journal (ApJ)

Spiral structures are important drivers of the secular evolution of disc galaxies, however, the origin of spiral arms and their effects on the development of galaxies remain mysterious. In this work, we present two three-armed spiral galaxies at z~0.3 in the Middle Age Galaxy Properties with Integral Field Spectroscopy (MAGPI) survey. Taking advantage of the high spatial resolution (~0.6'') of the Multi-Unit Spectroscopic Unit (MUSE), we investigate the two-dimensional distributions of different spectral parameters: Halpha, gas-phase metallicity, and D4000. We notice significant offsets in Halpha (~0.2 dex) as well as gas-phase metallicities (~0.05 dex) among the spiral arms, downstream and upstream of MAGPI1202197197 (SG1202). This observational signature suggests the spiral structure in SG1202 is consistent with arising from density wave theory. No azimuthal variation in Halpha or gas-phase metallicities is observed in MAGPI1204198199 (SG1204), which can be attributed to the tighter spiral arms in SG1204 than SG1202, coming with stronger mixing effects in the disc. The absence of azimuthal D4000 variation in both galaxies suggests the stars at different ages are well-mixed between the spiral arms and distributed around the disc regions. The different azimuthal distributions in Halpha and D4000 highlight the importance of time scales traced by various spectral parameters when studying 2D distributions in spiral galaxies. This work demonstrates the feasibility of constraining spiral structures by tracing interstellar medium (ISM) and stellar population at z~0.3, with a plan to expand the study to the full MAGPI survey., Comment: 15 pages, 11 figures, 2 tables, accepted for publication in MNRAS

Aphid infestation poses a significant threat to crop production, rural communities, and global food security. While chemical pest control is crucial for maximizing yields, applying chemicals across entire fields is both environmentally unsustainable and costly. Hence, precise localization and management of aphids are essential for targeted pesticide application. The paper primarily focuses on using deep learning models for detecting aphid clusters. We propose a novel approach for estimating infection levels by detecting aphid clusters. To facilitate this research, we have captured a large-scale dataset from sorghum fields, manually selected 5,447 images containing aphids, and annotated each individual aphid cluster within these images. To facilitate the use of machine learning models, we further process the images by cropping them into patches, resulting in a labeled dataset comprising 151,380 image patches. Then, we implemented and compared the performance of four state-of-the-art object detection models (VFNet, GFLV2, PAA, and ATSS) on the aphid dataset. Extensive experimental results show that all models yield stable similar performance in terms of average precision and recall. We then propose to merge close neighboring clusters and remove tiny clusters caused by cropping, and the performance is further boosted by around 17%. The study demonstrates the feasibility of automatically detecting and managing insects using machine learning models. The labeled dataset will be made openly available to the research community.

Aphid infestations can cause extensive damage to wheat and sorghum fields and spread plant viruses, resulting in significant yield losses in agriculture. To address this issue, farmers often rely on chemical pesticides, which are inefficiently applied over large areas of fields. As a result, a considerable amount of pesticide is wasted on areas without pests, while inadequate amounts are applied to areas with severe infestations. The paper focuses on the urgent need for an intelligent autonomous system that can locate and spray infestations within complex crop canopies, reducing pesticide use and environmental impact. We have collected and labeled a large aphid image dataset in the field, and propose the use of real-time semantic segmentation models to segment clusters of aphids. A multiscale dataset is generated to allow for learning the clusters at different scales. We compare the segmentation speeds and accuracy of four state-of-the-art real-time semantic segmentation models on the aphid cluster dataset, benchmarking them against nonreal-time models. The study results show the effectiveness of a real-time solution, which can reduce inefficient pesticide use and increase crop yields, paving the way towards an autonomous pest detection system.

Aphids are one of the main threats to crops, rural families, and global food security. Chemical pest control is a necessary component of crop production for maximizing yields, however, it is unnecessary to apply the chemical approaches to the entire fields in consideration of the environmental pollution and the cost. Thus, accurately localizing the aphid and estimating the infestation level is crucial to the precise local application of pesticides. Aphid detection is very challenging as each individual aphid is really small and all aphids are crowded together as clusters. In this paper, we propose to estimate the infection level by detecting aphid clusters. We have taken millions of images in the sorghum fields, manually selected 5,447 images that contain aphids, and annotated each aphid cluster in the image. To use these images for machine learning models, we crop the images into patches and created a labeled dataset with over 151,000 image patches. Then, we implement and compare the performance of four state-of-the-art object detection models.

We investigate the impact of environment on the internal mass distribution of galaxies using the Middle Ages Galaxy Properties with Integral field spectroscopy (MAGPI) survey. We use 2D resolved stellar kinematics to construct Jeans dynamical models for galaxies at mean redshift $z \sim 0.3$, corresponding to a lookback time of $3-4$ Gyr. The internal mass distribution for each galaxy is parameterised by the combined mass density slope $\gamma$ (baryons $+$ dark matter), which is the logarithmic change of density with radius. We use a MAGPI sample of 28 galaxies from low-to-mid density environments and compare to density slopes derived from galaxies in the high density Frontier Fields clusters in the redshift range $0.29

This document presents the initial scientific case for upgrading the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab (JLab) to 22 GeV. It is the result of a community effort, incorporating insights from a series of workshops conducted between March 2022 and April 2023. With a track record of over 25 years in delivering the world's most intense and precise multi-GeV electron beams, CEBAF's potential for a higher energy upgrade presents a unique opportunity for an innovative nuclear physics program, which seamlessly integrates a rich historical background with a promising future. The proposed physics program encompass a diverse range of investigations centered around the nonperturbative dynamics inherent in hadron structure and the exploration of strongly interacting systems. It builds upon the exceptional capabilities of CEBAF in high-luminosity operations, the availability of existing or planned Hall equipment, and recent advancements in accelerator technology. The proposed program cover various scientific topics, including Hadron Spectroscopy, Partonic Structure and Spin, Hadronization and Transverse Momentum, Spatial Structure, Mechanical Properties, Form Factors and Emergent Hadron Mass, Hadron-Quark Transition, and Nuclear Dynamics at Extreme Conditions, as well as QCD Confinement and Fundamental Symmetries. Each topic highlights the key measurements achievable at a 22 GeV CEBAF accelerator. Furthermore, this document outlines the significant physics outcomes and unique aspects of these programs that distinguish them from other existing or planned facilities. In summary, this document provides an exciting rationale for the energy upgrade of CEBAF to 22 GeV, outlining the transformative scientific potential that lies within reach, and the remarkable opportunities it offers for advancing our understanding of hadron physics and related fundamental phenomena., Comment: Updates to the list of authors; Preprint number changed from theory to experiment; Updates to sections 4 and 6, including additional figures