Long-Lived Superparticles with Hadronic Decays at the LHC Zhen Liu Talk based on work with B. Tweedie, arxiv:1503.05923 Pheno 2015 Symposium PITT-PACC,

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Long-Lived Superparticles with Hadronic Decays at the LHC Zhen Liu Talk based on work with B. Tweedie, arxiv: Pheno 2015 Symposium PITT-PACC, University of Pittsburgh Fermi National Accelerator Laboratory

THINKING OUTSIDE THE BEAMPIPE See also in Greg Landsberg’s plenary talk, and also Preema Rennee PAIS’s parallel talk for efforts from experimental side.

OBJECT Space of All Models explicitly searched for by ATLAS/CMS actual LHC search sensitivity (expand interpretation) theoretical LHC sensitivity (expand search strategies / refine SM predictions) actual LHC search sensitivity (expand interpretation) theoretical LHC sensitivity (expand search strategies / refine SM predictions)

CMS displaced dijets (tracker) ATLAS low-EM jets (HCAL) ATLAS muon spectrometer vertices* CMS charged stable particles CMS displaced dileptons CMS displaced electron & muon ATLAS displaced muon + tracks Applied to all models Applied to models with leptonic decays * 7 TeV, 2 fb -1 Hadronic R-parity violation gauge mediation mini-split ** All via direct pair-production OVERVIEW OF OUR STUDY Our selection of signals covers a large range of displace decay topologies, including 1j+MET, 2j+MET, 3j+MET, 2j, 3j, as well as heavy flavors, making it easy for theoretists to estimate exclusions for their own models in concern.

A EXAMPLE OF DISPLACED DIJET q q q q LSP g q standard 2j2j + MET showered 1j + MET q q 3j q

A TYPICAL EFFICIENCY MAP Meade, Reece, Shih (2010) Depend on lifetime, how is the particle being produced (How Lorentz factor distributes), the decay distributes shape differently at different layers of the detector. Efficiency map for RPV stop decays into light jet pairs in the CMS displaced dijet analysis. Lines at increase of 100 GeV Low mass suffers more for cuts on jet energy High mass approaches constant efficiency shape Low efficiency at low lifetime (cut to remove SM) (Shift in peak due to Lorentz Factor)

With detailed simulation and our own modeling of the displacement, after carefully calibrating with existing searches, we can derive the limits from many search of our simplified models. EXAMPLE 1: GMSB STOP Dijet search has very good sensitivity reach, lepton plus tracks searches also sensitive to leptonic top- and b-decays. HCAL and muon spectromeater searches sensitive to higher lifetimes but so far suffers large efficiency cost. Optimization may provide additional information, e.g., heavy neutral displaced particles. FULL lifetime coverage up to 500 GeV!

MINI-SPLIT GLUINO ➞ 2J + LSP g ~ q* ~ q EM-neutral LSP q Arkani-Hamed & Dimopoulos (2004) Arvanitaki, Craig, Dimopoulos, Villadoro (2012) Arkani-Hamed, Gupta, Kaplan, Weiner, Zorawksi (2012)

MINI-SPLIT GLUINO ➞ 2J + LSP The dijet in the final boosts the efficiencies for displaced dijet searches. The prompt jets+MET searches also covers a range of lifetime in the low mass, as fractions of long- lived particles decay promptly (boundary in dashed lines indicates possible extrapolation. FULL lifetime coverage up to >1 TeV! This figure shows one extreme case with large mass splitting between the LSP and NLSP. How about a bit compressed?

MINI-SPLIT GLUINO ➞ 2J + LSP In case of compressed spectra (right panel) Most searches rely on visible SM partcles greatly reduced due to energy cuts (necessary to cut away SM backgrounds from non-prompt decay and cosmic rays, etc) Heavily charged stable particle search remains the same as no decays are required. Different displaced search channels are more complementary, more important.

Major classes of well-motivated SUSY models exhibit displaced decays RPV, GMSB, mini-split broad range of possible particle spectra and decay topologies only a handful covered by explicit searches Existing displaced decay searches can be extremely powerful “weird looking” ⇒ tiny backgrounds non-SUSY searches cover lots of SUSY model space Understanding the full LHC potential requires careful recastings for many models map ranges/overlaps in coverage identify gaps/ambiguities/opportunities we have made some first steps in this direction Summary

Thank you! Enjoy Pheno Enjoy RUN 2