The escape fraction of Lyman-continuum (LyC) photons (fesc) is a key parameter for determining the sources of cosmic reionization at z ≥ 6. At these redshifts, owing to the opacity of the intergalactic medium, the LyC emission cannot be measured directly. However, LyC leakers during the epoch of reionization could be identified using indirect indicators that have been extensively tested at low and intermediate redshifts. These include a high [O III]/[O II] flux ratio, high star-formation surface density, and compact sizes. In this work, we present observations of 29 4.5 ≤ z ≤ 8 gravitationally lensed galaxies in the Abell 2744 cluster field. From a combined analysis of JWST-NIRSpec and NIRCam data, we accurately derived their physical and spectroscopic properties: our galaxies have low masses (log(M⋆)∼8.5), blue UV spectral slopes (β ∼ −2.1), compact sizes (re ∼ 0.3 − 0.5 kpc), and high [O III]/[O II] flux ratios. We confirm that these properties are similar to those characterizing low-redshift LyC leakers. Indirectly inferring the fraction of escaping ionizing photons, we find that more than 80% of our galaxies have predicted fesc values larger than 0.05, indicating that they would be considered leakers. The average predicted fesc value of our sample is 0.12, suggesting that similar galaxies at z ≥ 6 have provided a substantial contribution to cosmic reionization.
The COVID-19 pandemic has caused serious consequences in the last few months and fighting against the epidemic has been the most important and crucial thing. With effective prevention and control methods, the epidemic has been gradually under control in some countries and it is essential to ensure safe work resumption currently. Although some approaches are proposed to measure people's healthy conditions, such as filling in the health information forms or evaluating by people's travel records, they cannot provide a fine-grained assessment of the epidemic risk. In this paper, we propose a novel epidemic risk assessment method based on the granular data collected by communication stations. We first compute the epidemic risk of these stations in different intervals by combining the number of infected persons and the way they pass through the station. Then we calculate the personnel risk in different intervals according to the station trajectory of the queried person. This method could assess people's epidemic risk accurately and efficiently. We also conduct extensive simulations and the results verify the effectiveness of the proposed method.
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