Modelling the invisible part of the cross-section

Written simultaneously with, and spookily relevant to, my previous post. And yes, he really does know a lot of people who design parton showers.

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I am on day 3 – or is it 4, I lose count – of a quarantine since coming down with mild flu-like symptoms on Tuesday.  For about half of that time I have been asleep, but the other half has seen a parade of infuriating posts on social media, many of which come from people who should know better.  The theme of the moment seems to be that anyone who has installed matplotlib and knows how to call numpy.exp() is getting the crayons out and making predictions by extrapolating trends from very little information.  It is dismaying indeed to see people who design parton showers for a living point at a scaling law and cry “we’re doomed” without asking what its region of validity is.  And if you think this comment is aimed specifically at you, you are wrong because everyone is doing this right now and I…

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Reinterpretation of Data

Good data can be interpreted many ways. (So can bad data of course, but that’s boring.)

This is not a comment on any recent epidemiology modelling, though I suppose there may be some issues in common. Here in my little loft-space, with my various teleconference options now augmented by “Teams”, I am still carrying on with particle physics, while my daughter has online lessons downstairs and my son frets about cancelled A levels. And my wife is still at school teaching.

https://twitter.com/jonmbutterworth/status/1240364496473989121

But yes, particle physics, if you fancy a break.

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The science stories that shaped 2019

In the Observer, with a contribution from me.

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Book Shambles

jonb_shambles_s7-150x150I did a Book Shambles with Robin Ince and Josie Long, which you can hear here. Hear hear.

Ostensibly we were discussing “A Map of the Invisible”, though it starts with me denying the allotment allegation, and pretty much shambles on from there to cover quite a lot of ground

 

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