About
I will describe an approach to search for new phenomena in data, by detecting discrepancies between two datasets. These could be, for example, a simulated standard-model background, and an observed dataset containing a potential hidden signal of New Physics. I will propose a new statistical test, built upon a test statistic which measures deviations between two samples, using a Nearest Neighbors approach to estimate the local ratio of the density of points. The test is model-independent and non-parametric, requiring no knowledge of the shape of the underlying distributions, and it does not bin the data, thus retaining full information from the multidimensional feature space. As a by-product, the technique is also a useful tool to identify regions of interest for further study. As a proof-of-concept, I will show the power of the method when applied to synthetic Gaussian data, and to a simulated dark matter signal at the LHC..
0:00:00 Slide 1
0:01:03 Slide 2
0:01:43 Slide 3
0:03:15 Slide 4
0:05:05 Slide 5
0:05:57 Slide 6
0:07:31 Slide 7
0:07:51 Slide 8
0:08:22 Slide 9
0:10:45 Slide 10
0:12:08 Slide 11
The talk
Credits and licence
- Institution
- CERN
- Department
- Machine Learning
- Speaker
- Andrea De Simone
- Year
- 2018
- Language
- English
- Subjects
- Computer science
- Source
- CERN
- Licence
- CERN, as CERN states it. CERN's terms
- Attribution
- © CERN. CERN provides its audiovisual media free of charge for educational and informational use, credited to CERN; this lecture is shown in CERN's own player.
- Who can watch
- CERN gives no rating. Invisible Network shows it to Tweens profiles and older, judged from what the source says about it. How courses are placed