Deployment of unsupervised learning in the search for new physics at the LHC with the ATLAS detector
Sakarias Frette: The standard model is the most accurate theory to date, with incredible precision measurements done at multiple detectors. It has however some shortcommings, not being able to explain phenomena such as the hierarchy problem, gravity, dark matter, etc.. Additional theories have been put forward to try to cover these issues, but for now, it has yielded no luck.
The strategy until recently has been to take such a model, and do a targeted search, resulting in large exclusion plots and no new physics. This is effective and fast for a single model, but very biased, and takes a lot of time if you want to try on 100 og 1000 models.
My thesis will instead try to apply a semi unsupervised technique to separate out anomalous data such that we can reduce the uninteresting SM background and focus on possible new physics that might be hidden in the data. The data analysis model used is an autoencoder.
As you might know, some of our master's students are about to defend their thesis soon. Until then, we are arranging a series of 8 weekly open sessions to practice for their presentation and share their research with the rest of the department.
This coming Friday (March 10 at 4:00 PM), we will have the first presentation by Sakarias Frette, where he will talk about Deployment of unsupervised learning in the search for new physics at the LHC with the ATLAS detector.
The presentations shall have the typical 30-minute exposition + a round of questions of around 15 minutes. It would be significant to have a good number of PhDs and individuals interested in the field so that the question round can be engaging and serve as good practice for the student. Make sure to attend if the topic seems interesting.
Published Mar. 7, 2023 10:23 AM
- Last modified Mar. 7, 2023 10:23 AM