The first billion years of cosmic history represents the final frontier in assembling a coherent physical picture of early galaxy formation, and a remarkable progress in this area has been made in the last few years. We have carried out a detailed analysis of a gravitationally lensed galaxy A2744_YD4 at z = 8.38 behind the massive galaxy cluster Abell 2744. The photometric redshift of about 8, estimated from HST, VLT and Spitzer data, was confirmed by the detection of the Ly_alpha line at a redshift of z=8.38 in a deep VLT X-SHOOTER spectrum. The follow-up observations with the Atacama Large Millimeter/submillimeter Array (ALMA) detected a significant 1 mm continuum flux indicative of the presence of dust in a very young star-forming galaxy. The ALMA spectrum showed also ionized oxygen at the same redshift. This is the most distant, and hence earliest, detection of dust and oxygen in the Universe. A2744_YD4 contained an amount of dust equivalent to 6 million times the mass of our Sun, a total stellar mass of 2 billion times the mass of our Sun, and a star formation rate ~ 20 solar masses per year. The detection of dust in this early epoch of the Universe provides key information on when the first supernovae exploded and hence the time when the first stars appeared in the Universe.
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Dark matter is an invisible substance that makes up more than eighty percent of the matter content of the universe. We know of its existence due to its gravitational influence, being a key ingredient to understand everything from the large-scale evolution of the universe to the formation of galaxies like the Milky Way, of which we are part of . However, very little is known about its nature, which constitutes one of the greatest unsolved problems in contemporary physics. The fuzzy dark matter model has recently been studied as a promising candidate. In this model , it is postulated that dark
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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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