2018-10-22
15:22 |
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The Black Hole Information Paradox and Beyond
/ Strominger, Andrew (speaker) (Harvard University)
We begin with an introduction to Hawking’s so-far-unresolved black hole information paradox. Recent insights into the deep infrared structure of gravity and gauge theory connecting soft theorems, asymptotic symmetries and the memory effect which have had an impact on this paradox are described. [...]
2018 - Streaming video.
Theory Colloquium
External link: Event details
In : The Black Hole Information Paradox and Beyond
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2018-10-22
13:45 |
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2018-10-22
13:41 |
Detaljerad journal - Similar records
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2018-10-19
10:53 |
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Our Chief Weapon in Programming
/ Van Eerd, Tony (speaker) (Christie Digital)
If I had time to convey just one* piece of programming advice, what would it be? Over the years I've often wondered "If I could only follow one bit of programming advice, what would it be", and have asked many people the same. I think I have narrowed down the one* rule I follow and have actually always followed, and which I find has helped me write correct, fast, understandable, maintainable code. [...]
2018 - 1:05:05.
CERN Computing Seminar
External link: Event details
In : Our Chief Weapon in Programming
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2018-10-19
10:42 |
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Learning New Physics from a machine
/ Wulzer, Andrea (speaker) (CERN)
We propose using neural networks to detect data departures from a given reference model, with no prior bias on the nature of the new physics responsible for the discrepancy. The model-independent nature of our approach, and its ability to deal with rare signals such as those expected at the LHC, is quantitatively assessed in toy examples..
2018 - 0:41:15.
Machine Learning; IML Machine Learning Working Group: unsupervised searches and unfolding with ML
External links: Talk details; Event details
In : IML Machine Learning Working Group: unsupervised searches and unfolding with ML
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2018-10-19
10:34 |
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2018-10-19
10:29 |
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2018-10-19
10:23 |
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2018-10-18
17:05 |
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2018-10-18
16:11 |
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Guiding New Physics Searches with Unsupervised Learning
/ De Simone, Andrea (speaker) (SISSA)
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..
2018 - 0:47:08.
Machine Learning; IML Machine Learning Working Group: unsupervised searches and unfolding with ML
External links: Talk details; Event details
In : IML Machine Learning Working Group: unsupervised searches and unfolding with ML
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Detaljerad journal - Similar records
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