LUCID in the Sky: A stunning example of research in schools

26 February 2019 by Aprajita Verma

Map of electron flux using LUCID data (from Furnell et al. 2019).

Working with Oxford Physics Researcher Peter Hatfield, three sixth form students have published results from the space-borne Langton Ultimate Cosmic ray Intensity Detector radiation monitor (LUCID) using Machine Learning techniques. This is a wonderful example for research in schools from a conceptual design through to key scientific results building on a decade of work by school student researchers. Here Peter tells us more about LUCID and the Institute for Research in Schools (IRIS).

"The Institute for Research in Schools is a nationwide charity. Through it, I recently supported three sixth formers, Will Furnell, (an undergraduate at the University of Kent by the time of submitting the paper), Abhishek Shenoy and Elliot Fox to write and submit a scientific paper "First results from the LUCID-Timepix spacecraft payload onboard the TechDemoSat-1 satellite in Low Earth Orbit", Furnell et al., 2019, which was recently published by the peer reviewed journal Advances in Space Research, available here and also was featured up in the February issue of the Royal Astronomical Society's Astronomy & Geophysics journal.

The paper reports the operations of the Langton Ultimate Cosmic ray Intensity Detector (LUCID) radiation monitor, which operated from 2014-2017 on board TechDemoSat-1 (TDS-1), a technology demonstration satellite, launched in 2015, and stopped collecting data on 2017. The spacecraft was funded by the Technology Strategy Board and South East England Development Agency, and built by Surrey Satellite Technology Ltd (SSTL). Oxford Physics was also a key contributor to one of the other instruments aboard, the Compact Modular Sounder.

LUCID itself resulted from a UK Space Agency (then BNSC) competition in 2008 for school children to design an experiment to go on TDS-1. The project grew out of a 2008 school visit by Langton Boys School in Kent to CERN, where they heard a talk about Medipix detectors, a family of radiation detectors developed at CERN that had been used in the Large Hadron Collider and in medical applications. The LUCID proposal used these Medipix detectors in space as a radiation and cosmic-ray monitor. In collaboration with the Medipix Collaboration, and Surrey Satellite Technology Ltd (SSTL), over 100 secondary-school student researchers at the Langton Star Centre have worked on LUCID and the subsequent analysis of the data that is essentially student led.

In their paper, Furnell et al. describe the machine learning algorithm they developed to analyse the data and how they applied it to more than two million frames of radiation data to give a particle classification of every track measured. They then made radiation maps by particle type (a special feature of the detector used, the Timepix chip), finding some interesting science results, like a slightly higher particle flux on the `dayside' of the Earth. They also developed an online platform TAPAS where other student researchers around the country could upload their own radiation measurements taken through CERN@school and apply the machine learning algorithm. Beyond radiation IRIS students across the country have worked on dozens of other projects, including analysing Higgs Hunter data (in a project led by Oxford's Professor Alan Barr, https://youtu.be/TN2JWsoRxzE), and contributing to selecting James Webb Space Telescope targets with STFC.

If you are interested in getting in involved with the Institute for Research in Schools, it would be great to hear from you, either by emailing me (peter.hatfield@physics.ox.ac.uk) or the Director of IRIS, Prof. Becky Parker (beckyparker@researchinschools.org)."

First results from the LUCID-Timepix spacecraft payload onboard the TechDemoSat-1 satellite in Low Earth Orbit by Furnell, Shenoy, Fox and Hatfield is published by Advances in Space Research Volume 63, Issue 5, 1 March 2019, Pages 1523-1540.

Categories: IRIS | research in schools | LUCID | machine learning