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New telescope to be the ‘GOTO’ for gravitational wave events

GOTO Telescop image credit - STFC

“In our specific case, we’re after discovering new spider pulsars by looking for the periodic signature of the heated star orbiting them. Some display unexpected changes and brighten up as mass suddenly start flowing from the companion star towards the pulsar. We don’t understand this behaviour as it occurs quickly (somewhere between days to weeks) and has only been observed a couple of times. Having “eyes” scanning the sky is exactly what we think could help us uncover their secrets.”

GOTO has received £3.2 million of funding from the Science and Technology Facilities Council (STFC) to deploy the full-scale facility.

Long hypothesised as a by-product of the collision and merger of cosmic behemoths such as neutron stars and black holes, gravitational waves were finally detected directly by the Advanced LIGO (Laser Interferometry Gravitational-wave Observatory) in 2015.

GOTO is designed to fill this observational gap by searching for optical signals in the electromagnetic spectrum that might indicate the source of the GW – quickly locating the source and using that information to direct a fleet of telescopes, satellites and instruments at it.

As most GW signals involve the merger of massive objects, these ‘visual’ cues are extremely fleeting as must be located as quickly as possible, which is where GOTO comes in.

The idea is that GOTO will act as sort of intermediary between the likes of LIGO, which detect the presence of a gravitational wave event, and more targetable multi-wavelength observatories that can study the event’s optical source.

Professor Danny Steeghs of the University of Warwick, GOTO’s Principle Investigator, said: “There are fleets of telescopes all over the world available to look towards the skies when gravitational waves are detected, in order to find out more about the source. But as the gravitational wave detectors are not able to pinpoint where the ripples come from, these telescopes do not know where to look.”

“If the gravitational wave observatories are the ears, picking up the sounds of the events, and the telescopes are the eyes, ready to view the event in all the wavelengths, then GOTO is the bit in the middle, telling the eyes where to look.”

Following the successful testing of a prototype system in La Palma, in Spain’s Canary Islands, the project is deploying a much expanded, second generation instrument.

Two telescope mount systems, each made up of eight individual 40 cm (16 inch) telescopes, are now operational in La Palma. Combined, these 16 telescopes cover a very large field of view with 800 million pixels across their digital sensors, enabling the array to sweep the visible sky every few nights.

These robotic systems will operate autonomously, patrolling the sky continuously but also focusing on particular events or regions of sky in response to alerts of potential gravitational wave events.

In parallel, the team is preparing a site at Australia’s Siding Spring Observatory, which will contain the same two-mount, 16 telescope system as the La Palma installation.

The plan is to have both sites operational this year to be ready for the next observing run of the LIGO/Virgo gravitational wave detectors in 2023.

The optical search for gravitational wave events is the next step in the evolution of gravitational wave astronomy. It has been achieved once before, but with GOTO’s help it should become much easier.

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Charting the Non-Linear Events in Life

Charting the Non-Linear Events in Life

Mathematician Eugenia Cheng explores the uses of math beyond the classroom. Read more columns here.

Many of us have experienced grief recently, having lost loved ones to the pandemic or suffered other tragedies. It is often said that grief isn’t linear; it doesn’t plod in a predictable straight line, but takes terrible twists and turns, rearing up without warning.

I can think of few things as unmathematical as grief, yet we use this oddly mathematical term “linear” to discuss it. Linear algebra is essentially the math of things that move in straight lines. It is a very stringent requirement. Like grief, most things are not linear. But there are other types of behavior we can look for in mathematical functions and real life.

If a function keeps going in generally the same direction, though not in a straight line, it’s called monotonic. Exponentials and logarithms are monotonic but not linear. Some people argue that rates of taxation should be monotonic with income, so that someone who earns more doesn’t pay a lower effective tax rate; however, this is often not the case in practice. People who want to lose or gain weight often get frustrated that body weight is not monotonic across time, but fluctuates up and down with things like fluid retention. Sometimes a monotonic function can be derived, despite fluctuations, by taking averages across chunks of time, say a week, or a month.

Charts are not just about drawing pretty pictures, but finding ways to understand key features about a function quickly.

The behavior of functions is much easier to recognize visually than in a formula, so we draw graphs to help us. Turning a function into a graph is an amazing process of translating something abstract and invisible into something that allows us to invoke our visual intuition. When we draw a function out on a graph we can immediately see other features beyond linearity and monotonicity, such as whether or not it has gaps or sharp corners. If it has no gaps it is called continuous, and if it has no corners it is called smooth.

Calculus provides techniques for figuring what the graph is going to look like beyond plotting points, because even if you plot millions of points you might miss some feature in between those points. The idea is to understand how abstract features give rise to the visual features (corners, gaps and so on): It’s not just about drawing pretty pictures, but finding ways to understand key features about a function quickly.

We might also do the reverse and take some data, plot the graph, and then try and fit a function to it. We can then use the function to predict what will happen in the future. This is what is done with data in the pandemic: Case numbers provide data that can be plotted in a graph, and then a function can be found that approximates those numbers. As the function can be applied to points in time beyond the data we have now, this gives us a way of estimating what will happen in the future. It’s not an exact science, because many non-linear functions that start off with the same shape behave differently later.

Reverse-engineering a function to fit some experimental data can also give us insight into causality. The structure of the function may indicate to us some basic principles at work, such as the laws of physics. Tracking the motion of planets enabled mathematicians to fit their path to an ellipse shape, with the motion dependent on a planet’s position relative to the sun at any moment. This is just a mathematical formula, but hints at the sun’s physical role in influencing the movement of the planets.

Saying grief isn’t linear is a severe understatement, as it’s not even monotonic, continuous, or smooth. Math gives us ways to carefully distinguish between different scenarios and sequences to delineate any situation. This doesn’t make grief go away, but it can be cathartic to have ways to depict its unpredictable trajectory.

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