Applications of Jet Substructure to New Physics Searches
Jet substructure is a rapidly maturing subject at the LHC, with advances being made in the theory and measurements of jet properties. As our understanding of QCD jets improves, jet substructure is a potentially useful tool to search for beyond the Standard Model new physics at the LHC. This workshop will focus on recent progress on the theoretical understanding of the structure of jets in busy hadronic final states, the experimental status of substructure observables, and the applications to searches for new hadronic states at the LHC.
OVERVIEW/SUMMARY TALKS:
Thursday: Marcel Vos, Jay Wacker
Friday: Andreas Hinzmann, Gavin Salam
Saturday: Peter Loch, Michael Spannowsky
DISCUSSION GROUPS (AND POSSIBLE TALKING POINTS)
THURSDAY
Survey of parameter space relevant for substructure searches and substructure variables
- Status of theory space: what kinds of models are generally excluded or unconstrained? Are there gaps that could be covered in the immediate future?
- Resolved vs. substructure analyses: which are most useful in different scenarios? Can the techniques be combined to strengthen constraints or discovery reach?
- What are the most appropriate variables for substructure analyses? How can we better understand them?
- What new variables are out there, and what kinds of physics do we need new variables to probe?
Understanding current substructure tools in relation to new physics searchers
- Current jet substructure observables and tools
- Is there redundancy in current substructure observables? What are the correlations between them?
- How do variables behave over a broad range of pT scales? How does this affect background estimation?
- What new variables are out there, and what kinds of physics do we need new variables to probe?
- What is the best way to mitigate pile-up?
- How do we balance robustness vs. discrimination power of substructure observables (as well as observables in resolved analyses)?
FRIDAY
Data-driven estimates for substructure searches
- Current approaches to experimental searches and comparisons to recommendations from theory/pheno
- Methods for data-driven estimates used in these searches
- Correlations between observables and the impact on data-driven estimates
Comparison of substructure variables used in searches
- Comparison in the performance of different variables used in searches (current or future)
- What figures of merit should be used when comparing experimental efficacy and performance?
- To what extent are the effects of pile-up a limiting factor, and do searches require a more refined pile-up correction scheme (e.g. shape subtraction)?
- Can we use new observables such as jet charge or ISR tagging in a real search? Are systematic uncertainties in any way a limiting factor?
Speakers:
Haipeng An, Perimeter Institute
Cliff Burgess, Perimeter Institute & McMaster University
Timothy Cohen, Stanford University
David Curtin, Stony Brook University
Adam Davison, University College London
Andreas Hinzmann, Cern
Gregor Kasieczka, University of Heidelberg
Gordon Krnjaic, Perimeter Institute
David Krohn, Harvard University
Andrew Larkoski, Massachusetts Institute of Technology
Matt LeBlanc, TRIUMF
Peter Loch, University of Arizona
Timothy Lou, Princeton University
Matthew Low, University of Chicago
Gavin Salam, Cern
Sebastian Schaetzel, University of Heidelberg
Philip Schuster, Perimeter Institute
Yanwen Shang, Perimeter Institute
Jessie Shelton, Harvard University
Michael Spannowsky, Durham University
Maximilian Swiatlowski, Stanford University
Carlos Tamarit, Perimeter Institute
Emily Thompson, Cern
Natalia Toro, Perimeter Institute
Brock Tweedie, Boston University
Marcel Vos, Instituto De Fisica Corpuscular
Jacob Wacker, Stanford University
Lian Tao Wang, University of Chicago
Itay Yavin, Perimeter Institute & McMaster University
Scientific Advisors:
Philip Schuster
Natalia Toro
Jacob Wacker
Organizers:
Eder Izaguirre
David Miller
Brian Shuve