The brightest object in the universe is a quasar with a black hole inside that consumes one ‘sun’ per day.

A study discovers that J0529-4351 is a gigantic disk of gas and dust with a diameter of seven light-years

The light from the brightest known object took over 12 billion years to reach Earth, dating back to the universe’s infancy. This quasar, initially mistaken for a nearby star due to its intense light, was first observed in sky surveys in 1980 and again in 2022. Named J0529-4351, it was believed to be a sun. However, it was later identified as a quasar, a gigantic gas and dust disc with a seven- light-year diameter, formed around a hole with a mass exceeding 17 billion suns. The object consumes matter equivalent to our Sun daily and emits vast amounts of light that reach us from the dawn of the cosmos. A team of scientists led by Christian Wolf from the Australian National University recently published an analysis in Nature Astronomy, revealing that Quasar J0529-4351 is the fastest-growing and brightest of all known quasars. Quasars, or quasi-stellar objects, were first discovered with radiotelescopes in the late 1950s, often mistaken for nearby stars due to their distant and powerful nature. Over a million have since been identified, although they often hide in plain sight. In an automated analysis of data gathered by Gaia, a European Space Agency probe that has catalogued about a billion astronomical objects, J0529-4351 was initially misidentified as a star due to its extreme brightness. Its true nature was revealed last year through observations from the Australian National University’s 2.3-meter telescope at the Siding Spring Observatory. Scientists later precisely estimated the object’s distances, dimensions, and brightness using the X-shooter spectrograph of the Very Large Telescope (VLT) at the European Southern Observatory in Chile’s Atacama Desert. Mar Mezcua from the Institute of Space Sciences (ICE- CSIC) in Barcelona highlights that despite having vast amounts of data, many discoveries go unnoticed if not properly processed.

In the search for quasars, large areas of the sky are analyzed, and models, sometimes machine learning, are used to distinguish quasars from stars or other celestial objects. These models are trained with images of known and categorized objects, which can hinder new discoveries when objects deviate from the norm. Isabel Márquez from the Institute of Astrophysics of Andalusia, believes that the size of this object will be useful to test the relationships between mass and luminosity of distant black holes. The extremely bright quasar will help determine if the estimates used to calculate the sizes and other characteristics of black holes are accurate. The VLT’s instrument, GRAVITY+, used to measure the mass of black holes, will be updated using data from Quasar J0529-4351. The discovery of such large objects in the early stages of the universe shows its preference for forming massive objects in denser areas with more galaxies than now. Mezcua believes that such discoveries lend weight to the theory of seed holes, which could explain how such massive black holes formed so quickly. Discoveries like this one, and those made by the James Webb Space Telescope, which is detecting even older black holes than J0529-4351, are helping to reconstruct the history of the early cosmos, crucial for understanding its evolution into the universe we inhabit today. 

They use modern weather models, which have made remarkable progress in recent decades. The accuracy of a five-day forecast today is better than a oneday forecast in 1980. This is primarily due to a higher density of current atmospheric state observations. However, other mathematical disciplines, such as statistics, partial differential equations, numerical methods, and recently artificial intelligence, are also crucial. Detailed modeling of each specific vessel’s movement and fuel consumption per unit of time, based on navigation conditions, currents, wave height, angle of incidence, wind direction and intensity, etc., is possible using naval engineering and data science knowledge. With this information, mathematical optimization algorithms have been developed and are used today to find favorable maritime routes. Any prediction carries uncertainty, which must also be quantified and considered when optimizing navigation routes. Therefore, new dynamic and stochastic optimization algorithms must be developed to achieve robust results under any possible atmospheric evolution scenario. This is particularly important in a global warming scenario where the atmosphere is more unstable and extreme events are increasingly frequent. Recently, all these mathematical techniques have been integrated to optimize maritime routes in real time, based on weather and oceanographic predictions, with consumption models adapted to each vessel. This tool, intended to be a «Google Maps» of the ocean, is being developed by a multidisciplinary team, predominantly of Spanish researchers. It can save 5-10% of fuel on each trip, improve navigation safety by avoiding areas with adverse conditions, and reduce CO2 emissions. This is crucial as today, more than 80% of trade is transported by sea, which accounts for about 3% of global carbon dioxide emissions. Annual fuel expenditure amounts to 250 billion euros and represents more than 60% of the operating costs of maritime transport. 

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