Skip to main content

Supernova - Or Graur ***

A solid entry in MIT Press's pocket-sized 'essential knowledge' series, introducing supernovas. (The author would not like my use of this plural: he sniffily comments that 'although "supernovas" is sometimes used in popular media, it is seldom used by astronomers'. This is because 'nova' comes from the Latin - which it does - but perhaps it's worth pointing out we are writing in English, not Latin.) A supernova can be one of several different types of collapsing/exploding stars: Or Graur gives us a good deal of detail on current best ideas on the different ways a supernova can form and behave.

Along the way, we are introduced to the history of our noticing supernovas, the role of star remnants in distributing the heavier elements across the universe and how astronomers use supernovas as standard candles to measure great distances (amongst other things). Graur is unusually flexible for an astronomer here, allowing that dark energy is based on distinctly uncertain data (derived from supernova observations), though elsewhere he refers to dark matter with no suggestion that this too has uncertainty about its existence. Unusually for these books there is a glossy colour plate section in the middle, allowing for much clearer images than is normally possible on conventional paper, which was real benefit.

Graur also makes it clear (with relish) that there are plenty of questions left for astronomers and astrophysicists still to answer about these phenomena. Superficially there is nothing very surprising in this book, but there is considerably more up-to-date detail than would usually be presented in a title pitched at the general reader. This has its good side - we find out, for example, about exotic supernova types that will not usually get a mention - but it also has a less useful aspect as there is, if anything, too much detail on each type, meaning that the writing can get more like a bullet-pointed fact sheet than a readable narrative.

There is a real problem, which Graur highlights without realising the consequences in his introduction. He tells us 'For too long, popular culture has focused on a handful of famous, eccentric, or controversial scientists... In reality, there are tens of thousands of scientists spread across the world... To combat this pernicious stereotype, I have sought to highlight the global and collaborative nature of astronomy and refrained from gossiping about the astrophysicists mentioned in this book.' Of course the collaborative aspect is true - but what Graur unfortunately seems to miss is that stories need insights into individual humans - by largely sticking to impersonal facts you also produce uninspiring writing. It's a paradox - we do need to emphasise the wide-ranging collaboration, but also to provide specific stories of real people's individual work if a book is to be accessible.

I read most of this book while in a public space, which highlighted to me the worst thing about the series format. Every few pages, a whole page is black with a short pull quote in large white letters. These don't add anything at all - and the quotes themselves are rarely thrilling (for example 'Today hundreds of astronomers routinely discover thousands of supernovae each year'). I found it quite embarrassing for these things to be visible to those around me, as if I were reading a children's book and rushed past them.

An effective, up-to-date summary for those who want more detail on supernovas than is usually found in a popular science book.

Paperback: 
Bookshop.org

  

Kindle 
Using these links earns us commission at no cost to you

Comments

Popular posts from this blog

David Spiegelhalter Five Way interview

Professor Sir David Spiegelhalter FRS OBE is Emeritus Professor of Statistics in the Centre for Mathematical Sciences at the University of Cambridge. He was previously Chair of the Winton Centre for Risk and Evidence Communication and has presented the BBC4 documentaries Tails you Win: the Science of Chance, the award-winning Climate Change by Numbers. His bestselling book, The Art of Statistics , was published in March 2019. He was knighted in 2014 for services to medical statistics, was President of the Royal Statistical Society (2017-2018), and became a Non-Executive Director of the UK Statistics Authority in 2020. His latest book is The Art of Uncertainty . Why probability? because I have been fascinated by the idea of probability, and what it might be, for over 50 years. Why is the ‘P’ word missing from the title? That's a good question.  Partly so as not to make it sound like a technical book, but also because I did not want to give the impression that it was yet another book

Vector - Robyn Arianrhod ****

This is a remarkable book for the right audience (more on that in a moment), but one that's hard to classify. It's part history of science/maths, part popular maths and even has a smidgen of textbook about it, as it has more full-on mathematical content that a typical title for the general public usually has. What Robyn Arianrhod does in painstaking detail is to record the development of the concept of vectors, vector calculus and their big cousin tensors. These are mathematical tools that would become crucial for physics, not to mention more recently, for example, in the more exotic aspects of computing. Let's get the audience thing out of the way. Early on in the book we get a sentence beginning ‘You likely first learned integral calculus by…’ The assumption is very much that the reader already knows the basics of maths at least to A-level (level to start an undergraduate degree in a 'hard' science or maths) and has no problem with practical use of calculus. Altho

Everything is Predictable - Tom Chivers *****

There's a stereotype of computer users: Mac users are creative and cool, while PC users are businesslike and unimaginative. Less well-known is that the world of statistics has an equivalent division. Bayesians are the Mac users of the stats world, where frequentists are the PC people. This book sets out to show why Bayesians are not just cool, but also mostly right. Tom Chivers does an excellent job of giving us some historical background, then dives into two key aspects of the use of statistics. These are in science, where the standard approach is frequentist and Bayes only creeps into a few specific applications, such as the accuracy of medical tests, and in decision theory where Bayes is dominant. If this all sounds very dry and unexciting, it's quite the reverse. I admit, I love probability and statistics, and I am something of a closet Bayesian*), but Chivers' light and entertaining style means that what could have been the mathematical equivalent of debating angels on