Machine Learning in High Energy Physics

Contribution List

9. Statistically Learning the Next Standard Model from LHC Data

Wolfgang Waltenberger (Austrian Academy of Sciences (AT))

12/09/2022, 09:00

Seminars (morning)

Despite the large amount of data generated by the LHC so far, searches for new physics have not yet provided any clear evidence of beyond the Standard Model (BSM) physics. Most of these experimental searches focus on exclusive channels, looking for excesses in specific final states. However, new physics could manifest as a dispersed signal over many channels. It therefore becomes increasingly...

2. Finding exotic hadronic signatures at the LHC with artificial intelligence

Benedikt Maier (CERN)

12/09/2022, 10:15

Seminars (morning)

The most powerful hadron collider in existence, the LHC, smashes bunches of protons at unprecedented energies and rates. The CMS detector takes snapshots of these collisions, in which we must accurately disentangle and reconstruct the particles and their origins in order to hypothesize about potential effects arising from new, yet undiscovered particles or forces. Some of the most intriguing...

3. Normalizing Flows at the LHC --- Preparing for the Future

Dr Claudius Krause (Rutgers University)

12/09/2022, 14:15

Seminars (afternoon)

LHC run 3 just started and in the years leading up to 2040, we will see a 20-fold increase in available data. This forthcoming dataset will have enormous potential for a deeper understanding of the Standard Model and possible physics beyond it.
In my talk, I will highlight how advancements in modern Machine Learning can help speed up crucial bottlenecks and open new avenues for model-agnostic...

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