The accelerating universe and machine learning

Over the last 25 years, our understanding of the universe has been dramatically enriched with the details of its evolution from the Big Bang some 14 billion years ago. One of the most pivotal of these moments in this series of discoveries is related to a special kind of star called ‘type 1a supernovae’.

These star systems reach a critical point and then undergo an explosion. The peak brightness of these events is fairly consistent and can be used to measure the expansion of the universe at that point in time, since it takes a long time for this light to arrive to observers on Earth.

The precision with which the expansion of the universe is measured using this method led to the discovery in 1998 that the universe is accelerating in its expansion. It was previously thought that the universe would be slowing down. The cause or driver of this acceleration we call “dark energy” led to the 2011 Nobel prize in physics for its discovery.

More recently, the precision of these measurements has continued to improve, and we now have unprecedented accuracy in the measurement of the rate of expansion of the universe, which we call the Hubble constant. The method outlined here gives a direct measurement of the Hubble constant since it is measured directly from observing stars. On the other hand, the Hubble constant can also be inferred by making observations of the early universe and the first light from that time.

We now have unprecedented accuracy in the measurement of the rate of expansion of the universe, which we call the Hubble constant

Inference from the early universe requires an understanding of the way the universe evolved over time, which we call the standard model of cosmology. The values of the Hubble constant, coming from both direct and early time measurements, gives different values, which has prompted a possible re-evaluation of our understanding of dark energy and how it affects the expansion of the universe over time.

An exciting new project, called CosmoLearn, based at the Institute of Space Sciences and the Department of Physics at the University of Malta, is working on using novel techniques in machine learning to develop new cosmological models that are data-driven and resolve this open question.

This convergence of new physics and novel applications of machine learning is being funded through the Research Excellence Programme at XjenzaMalta. The results of this project have been presented at international conferences and publications, as well as through the European CosmoVerse network (https://cosmoversetensions.eu.

Jackson Levi Said is a cosmologist at the Institute of Space Sciences and Astronomy, University of Malta.

Sound Bites

•         A preclinical study using stem cells to produce progenitor photoreceptor cells ‒ light-detecting cells found in the eye  ‒ and then transplanting these into experimental models of damaged retinas has resulted in significant vision recovery. This finding, by scientists at Duke-NUS Medical School, the Singapore Eye Research Institute and the Karolinska Institute in Sweden, marks a first step towards potentially restoring vision in eye diseases characterised by photoreceptor loss.

•         Scientists have created a new family of polymers capable of killing bacteria without inducing antibiotic resistance – a major step in the fight against superbugs like E. coli and MRSA.

For more soundbites, listen to Radio Mocha every Saturday at 7.30pm on Radju Malta and the following Monday at 9pm on Radju Malta 2 https://www.fb.com/RadioMochaMalta/.

DID YOU KNOW?

•         Quakebot is a software that automatically writes news articles for the LA Times when the US Geological Society detects a tremor in California.

•         NASA provides astronauts with liquid salt and pepper.

•         Chimpanzees are better at difficult tasks when they’re being watched by an audience.

•         St Lidwina of Schiedam is the patron saint of chronic pain and ice skating.

For more trivia, see: www.um.edu.mt/think.