Thanks for visiting Imaging and Machine Vision Europe.

You're trying to access an editorial feature that is only available to logged in, registered users of Imaging and Machine Vision Europe. Registering is completely free, so why not sign up with us?

By registering, as well as being able to browse all content on the site without further interruption, you'll also have the option to receive our magazine (multiple times a year) and our email newsletters.

Machine learning to help Higgs experiment

Share this on social media:

The University of California, Irvine, (UCI) has published findings in Nature Communications that suggest that algorithmic deep learning with digital images could greatly increase the odds of finding the Higgs boson – the recently confirmed particle that could explain fundamental principles of the universe such as why some particles have mass.

In computer experiments using carefully structured simulated data, the UCI researchers' methods resulted in an 8 per cent increase in the detection of these particles.

Machine learning, the umbrella term that encompasses deep learning, is a branch of artificial intelligence whereby computers are taught to recognise patterns in data. Finding Higgs boson particles requires sorting out relevant data from huge amounts of background noise.

Pierre Baldi, Chancellor's Professor of computer science at UCI, commented: ‘It's very difficult to write from scratch a program that can recognise elephants in images – or Higgs bosons in collider data. But we can provide to the computer many examples of images with and without elephants, or accelerator data with and without Higgs bosons, and let the computer learn automatically from these examples.’

In May, Cern and other organisations launched the Higgs boson machine learning challenge, a competition to develop machine-learning techniques to improve the analysis of Higgs data.

The competition is run through Kaggle and applicants are asked to design a system that can separate simulated images into either ‘tau tau decay of a Higgs boson’, or ‘background’. The competition closes on the 15 September, with the top three scores receiving cash prizes.

Baldi, along with computer science PhD student Peter Sadowski and associate professor of physics and astronomy Daniel Whiteson, found quicker, more efficient ways to analyse data obtained from particle accelerators or colliders to better detect rare particles.

Currently, physicists devise by hand mathematical formulas that they apply to the data to derive the features they're looking for, which are then fed to machine learning programs. By employing recent advances in deep learning, in which computers learn automatically at multiple processing levels, the UCI team eliminated the need for the time-consuming manual creation of those formulas.

‘These new, smarter deep learning networks have shown themselves to be better at finding hints of new particles than past machine learning methods – and than physicists with years of experience,’ Whiteson said. ‘They don't need any help from human insight, achieving a level of automatic learning which has been a long-standing goal in high-energy physics.’

Further information:

Recent News

24 October 2019

Imec says the new production method promises an order of magnitude gain in fabrication throughput and cost compared to processing conventional infrared imagers

04 October 2019

Each pixel in Prophesee’s Metavision sensor only activates if it detects a change in the scene – an event – which means low power, latency and data processing requirements

18 September 2019

3D sensing company, Outsight, has introduced a 3D semantic camera that combines lidar ranging with hyperspectral material analysis. The camera was introduced at the Autosens conference in Brussels

16 September 2019

OmniVision Technologies will be showing an automotive camera module at the AutoSens conference in Brussels from 17 to 19 September, built using OmniVision’s OX03A1Y image sensor with an Arm Mali-C71 image signal processor