We present observations and an interpretative model of the dust environment of the Main-Belt Comet P/2010 F5 (Gibbs). The narrow dust trails observed can be interpreted unequivocally as an impulsive event that took place around 2011 July 1 with an uncertainty of ±10 days, and a duration of less than a day, possibly of the order of a few hours. The best Monte Carlo dust model fits to the observed trail brightness imply ejection velocities in the range 8-10 cm s–1 for particle sizes between 30 cm and 130 μm. This weak dependence of velocity on size contrasts with that expected from ice sublimation and agrees with that found recently for (596) Scheila, a likely impacted asteroid. The particles seen in the trail are found to follow a power-law size distribution of index ≈ –3.7. Assuming that the slowest particles were ejected at the escape velocity of the nucleus, its size is constrained to about 200-300 m in diameter. The total ejected dust mass is > ~ 5 × 108 kg, which represents approximately 4%-20% of the nucleus mass.
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The cosmic evolution of the barred galaxy population provides key information about the secular evolution of galaxies and the settling of rotationally dominated discs. We study the bar fraction in the SMACSJ0723.37323 (SMACS0723) cluster of galaxies at z = 0.39 using the Early Release Observations obtained with the NIRCam instrument mounted on the JWST telescope. We visually inspected all cluster member galaxies using the images from the NIRCam F200W filter. We classified the galaxies into ellipticals and discs and determine the presence of a bar. The cluster member selection was based on a
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The amount and complexity of data delivered by modern galaxy surveys has been steadily increasing over the past years. New facilities will soon provide imaging and spectra of hundreds of millions of galaxies. Extracting coherent scientific information from these large and multi-modal data sets remains an open issue for the community and data-driven approaches such as deep learning have rapidly emerged as a potentially powerful solution to some long lasting challenges. This enthusiasm is reflected in an unprecedented exponential growth of publications using neural networks, which have gone
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It is well known that fullerenes – big, complex, and highly resistant carbon molecules with potential applications in nanotechnology – are mostly seen in planetary nebulae (PNe); old dying stars with progenitor masses similar to our Sun. Fullerenes, like C60 and C70, have been detected in PNe whose infrared (IR) spectra are dominated by broad unidentified IR (UIR) plateau emissions. The identification of the chemical species (structure and composition) responsible for such UIR emission widely present in the Universe is a mystery in astrochemistry; although they are believed to be carbon-rich
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