SUSY at The Compression Frontierresearch.kek.jp/people/nojiri/shingakujyutu/LeCompte.pdf ·...

SUSY at The Compression Frontier Tom LeCompte

Argonne Na*onal Laboratory

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Persuasion

Unusually, I am not here to persuade you of anything – other than that some ideas are maybe worth thinking about.

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Outline: Three Areas of Compression

  Compression from the Higgs Searches

  Compressed SUSY

  The future – and where our compuDng model is squeezing the physics

I am a member of the ATLAS collaboraDon, but am not represenDng them today. I am here as a “phenomenolgist”.

I would much rather this be a discussion than a “lecture”. Interrupt me as much as you like.

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Part I: Compression from the Higgs

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CERN Seminar in December:

These plots were shown at the CERN “jamboree”* in December.

The reacDon of some of the community was…

* Derived from a word meaning “a big headache.”

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A 125 GeV Higgs is too heavy for SUSY!

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There is No Need To Panic:

  First, the experiments are not saying that there is a 125 GeV Higgs. –  ATLAS: there is a window between 115.5-‐131 GeV

–  CMS: there is a window between 114 (LEP)-‐129 GeV (now 127 GeV)

–  A light Higgs can be anywhere in that window.

  Second, trying to line up the points of poorest exclusion between ATLAS and CMS implicitly asks the wrong quesDon. –  Even if there is a parDcle present, the point of poorest exclusion is not in general at the

center of mass of the peak.

At 1σ, a SM Higgs can be almost anywhere in the non-‐excluded region. At 2σ, it could preby much be anywhere.

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More Reason Not To Panic…

  Third, the ATLAS H γγ search excess is 1.5-‐2.0x what one expects from the SM. If one wants to interpret it as a Higgs, it leads to one of two possibiliDes: –  There is a component of this excess that is an upward staDsDcal fluctuaDon. In this case,

the inferred mass is altered by this fluctuaDon. -‐OR-‐ –  This is a non-‐SM Higgs.

–  In either case, one cannot properly apply the constraints of a 125 GeV SM Higgs on SUSY.

  Finally (and this will lead me into my next slide), these constraints apply to the MSSM, and there’s more to SUSY than the MSSSM.

We’re going to have to wait a few more months to get something more definite. (Also a message from the December seminar)

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More Reason Not To Panic…

  Third, the ATLAS H γγ search excess is 1.5-‐2.0x what one expects from the SM. If one wants to interpret it as a Higgs, it leads to one of two possibiliDes: –  There is a component of this excess that is an upward staDsDcal fluctuaDon. In this case,

the inferred mass is altered by this fluctuaDon. -‐OR-‐ –  This is a non-‐SM Higgs.

–  In either case, one cannot properly apply the constraints of a 125 GeV SM Higgs on SUSY.

  Finally (and this will lead me into my next slide), these constraints apply to the MSSM, and there’s more to SUSY than the MSSSM.

We’re going to have to wait a few more months to get something more definite. (Also a message from the December seminar)

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Part II: Compressed SUSY

Work done with Steve MarDn, Northern Illinois University Phys.Rev.D84:015004,2011 (hbp://arxiv.org/abs/1105.4304)

And follow-‐up (hbp://arxiv.org/abs/1111.6897)

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Some possibly unpopular thoughts

  SUSY may not be mSUGRA/CMSSM

  SUSY may not be MSSM

  SUSY may not even be SUSY! –  To me, “SUSY” represents an experimental search for a family of topologies that are

predicted in supersymmetric theories, but may have nothing to do with them. –  A common characterisDc is missing energy from undetected weakly or non-‐interacDng

parDcles. •  These may or may not have something to do with Dark Maber

–  Events can be characterized by what’s going on with the rest of the event: leptons, jets, etc. •  These may or may not give you peaks in an invariant mass distribuDon, but they are

likely to have some disDnguishing kinemaDcs.

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Compressed SUSY

  mSUGRA has great big gaps between the masses.

  This gives you energeDc leptons, jets and usually lots of missing ET.

  It’s almost as if this was designed to be easy to find at colliders.

  By compressing the spectrum you usually (more on this later) get less energeDc objects, less missing ET etc.

  The signal looks more like the background.

  Hard QCD radiaDon can be important.

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The “Easy” Thing To Do

  The LHC experiments have already published limits on mSUGRA.

  Can these limits be adapted to more compressed spectra?

  Step #1 is to come up with explicit compressed models:

–  c = 0 is mSUGRA-‐like

–  c= 1 is total compression (gauginos degenerate)

Here “easy” means “without a dedicated search”.

There is nothing magical about these choices; our goal was to come up with a spectrum that was very different than mSUGRA.

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Getting acceptances

  Step #2 is to calculate acceptances for these models.

  We used MadGraph/MadEvent (generaDon) + Pythia (fragmentaDon) + PGS (simulaDon of an ATLAS-‐like detector) –  ATLAS made public a number of their own points that we could tune and compare to.

This was extraordinarily useful, and I’d like to encourage this for every analysis. –  Typically, we were good to ~15% or beber, and the limitaDon seemed to be the staDsDcs

on the ATLAS side (more later) –  Cross-‐secDons were normalized to Prospino cross-‐secDons

  We ran four producDon models: –  Gluino/gluino, gluino/squark, squark/squark, and events with stops and/or sboboms

–  allowing up to 1 extra jet (at the matrix element level)

  And we got our first surprise:

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The Surprise!

  The acceptance for moderate compression (say c < 0.5) is comparable to (and someDmes even a lible bigger than) the mSUGRA-‐like case.

  This contradicts our intuiDon that increasing compression means reduced acceptance.

  That reducDon in acceptance doesn’t seem to “switch on” unDl c > 0.5 or so.

These are the three of the four signal regions in the 35 pb-‐1 ATLAS 0-‐lepton paper plus the 1-‐lepton paper signal region.

More compression

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What Is Happening

At low compression (mSUGRA-‐like) we have large gaps between parDcle masses.

At moderate to high compression, the gaps become smaller, but the average masses shix upwards. This provides sufficient Q for the decay to be visible, provided c is not too large.

This is a statement about acceptance, not sensiDvity. In general, cross-‐secDons will fall as the mass spectrum shixs upwards. However, for this model, since the bulk of the cross-‐secDon is through parDcles whose masses we fix, that doesn’t happen.

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What Else Is Happening

  With increasing compression, both Meff and MET get small   However, Meff gets smaller faster – so MET/Meff can actually grow

–  More events can pass this cut, so the acceptance can grow… –  …provided c does not get too big.

  At very high compression, both become sox and the signal melts into the background.

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Setting Limits

  Step #3: Take the published ATLAS fiducial cross-‐secDon limits and Step #2, and convert to a limit in the m(gluino-‐c) plane. –  We slightly changed the axes

for ease of interpretaDon. –  We could just have easily used

CMS limits; we used ATLAS simply because it was familiar to us.

–  This limit-‐sezng was possible because the experiments published fiducial cross-‐secDon limits. This should be strongly encouraged. I’ve switched to the 1 {-‐1 0-‐lepton paper limits,

which are more stringent than the 35 pb-‐1 limits.

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Features of Interest For most model points, Region A (two jets) has the most sensiDvity

At high compression, other search regions do a lible beber than Region A.

Region C (four jets, low MET and Meff) does poorly, largely because it has a large background relaDve to the other points.

Many surviving models have m(gluino) > 1050 GeV and/or m(LSP) > 600 GeV.

Even at high compression, m(gluino) > 600 GeV.

This is not a model independent statement, simply a shorthand descripDon of the limits.

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Another Model – Heavy Winos

  Here we move the wino mass up to several TeV, effecDvely decoupling them.

  The decay energy is now shared amongst few parDcles – acceptances go up.

  In this family of models gluino decays to stop + top are kinemaDcally forbidden.

  Decays are –  Leptonic decays are rare.

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Limits in Heavy Wino Model Many surviving models have m(gluino) > 1100 GeV and/or m(LSP) > 600 GeV.

Even at high compression, m(gluino) > 600 GeV.

This is not a model independent statement, simply a shorthand descripDon of the limits.

Limits tend to be a lible more stringent than in the baseline case, due to parDDoning the energy amongst fewer jets.

At very high mass, the cross-‐secDons are all small, and ulDmately one hits the wall.

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Yet Another Model – Heavy Squarks

  Here we move the squark mass up to several TeV, effecDvely decoupling them.

  The decay energy is now shared amongst many parDcles – –  Decays go through many

intermediate jets –  acceptances go down.

  Leptons can again be present.

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Acceptances Get More Complex   When energy is parDDoned among many jets with

many m(jj) constraints, things can get complicated.

  Part of this is the compeDng effects of raising the mass while at the same Dme lowering the mass difference.

  Part of this is due to having N jets in an event, M of which must pass certain cuts. –  Including the effects of jet merging

  For low compression, region E (4 jets, inclusive Meff) is best, but as the compression increases, B (3 jets) and then A (2 jets) become best.

35 pb-‐1 paper

1 {-‐1 paper

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Limits in Heavy Squark Model

Here we only get to a gluino mass limit around 450 GeV for high compression.

There really isn’t an LSP limit to speak of in this case.

Limits are much weaker than in the baseline case, due to parDDoning the energy amongst many jets.

The best limits are provided by different signal regions than in the previous two cases.

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One Final Model – Heavy Squarks and Winos

  Here we move both the squark and the wino mass up to several TeV, effecDvely decoupling them.

  The decay energy is now shared amongst an intermediate number of parDcles:

  As expected, the outcome is intermediate between the baseline (and heavy wino) case and the heavy squark case.

(for completeness)

(You might recognize this spectrum as a typical “simplified model.”)

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Some Intermediate Conclusions

  The mSUGRA searches, while not opDmized for Compressed SUSY, do a preby (maybe even surprisingly) good job.

  No single search region does the best for every model. –  It’s probably impossible to develop a “compressed signal region”, although it looks

possible to develop a “signal region good for some compressed models”.

  One deadly cut is MET/Meff. –  One can sharpen this by including more jets in Meff. –  The discussion about whether this cut is best applied to the selected jets or the event as

a whole is ongoing in the experiments now. It’s a delicate balance.

  UlDmately, the sensiDvity will be driven by when the signal dissolves into the background. –  Today, most backgrounds are ulDmately determined by the staDsDcs of control regions

(or regions to tune Monte Carlo). The situaDon will improve with more data, at least unDl we hit the true systemaDcs “wall”.

–  We haven’t considered techniques like Razor that we know improve S/B. To what degree will they help?

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Part III: The Future – and CompuDng Compression

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The Future

  Like it or not, the future of SUSY is compression –  Since mSUGRA is about the least compressed – most evenly spaced -‐ a spectrum one

can imagine, virtually everything else will be more compressed by comparison.

  mSUGRA can be thought of as a push outward into m0 – m½ space: toward higher masses.

  Compressed SUSY is fighDng a two-‐front war: –  Towards higher masses for models with moderate compression

–  Towards higher backgrounds for models with more compression

  FighDng a two-‐front war is not for the faint of heart.

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Exclusion Planes

  In mSUGRA, it’s natural to be discussing m0 and m½, or alternaDvely, m(squark) and m(gluino) –  I confess that I have a preference for

expressing limits in terms of physical quanDDes over parameters of the theory.

  In our compressed models, a more natural descripDon is in terms of this compression parameter “c”. –  m(gluino)-‐m(LSP) is essenDally the

same thing, expressed in terms of physical parameters.

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Why Exclusion Planes?

For a mSUGRA-‐like model, this is the sort of limit plot that’s used.

For our compressed models, this is a more suitable choice.

This is a third logical possibility, probably best for some class of models.

We’d really like to look at the exclusion surface in a 3-‐dimensional space.

Why not?

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How Experiments* Do Exclusions

* And wanna-‐be phenomenologists

BEFORE we have taken any data, we generate a “grid” of points in the exclusion plane.

The acceptance and efficiency for each point is calculated using Monte Carlo.

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Exclusions – Part II

AFTER we have taken the data, we see which points in the grid have been excluded.

X X X X X X X X

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Exclusions – Part III

If all four corners of a square are excluded, the enDre square is excluded.

If no corners of a square is excluded, no part of the square is excluded.

If 1, 2 or 3 corners of a square are excluded, the exclusion contour passes within that square.

X X X X X X X X

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Exclusions – Part IV

Some sort of interpolaDon is then done to infer the exclusion curve – e.g. linear interpolaDon followed by smoothing.

Remember –the only actual informaDon we have is at the grid points. X X X X

X X X X

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Some facts

  The models in LeCompte & MarDn required 80,000,000 events to be generated –  516 models in 4 categories (I am ignoring the 5th model in the paper)

–  Up to 4 producDon processes per event •  Gluino/guino, gluino/squark, squark/squark and stops and/or boboms

–  80,000-‐240,000 events per point •  Compressed models have low acceptance over much of the compression range, so we need a

lot of events.

–  It all adds up. But…

–  on a 200-‐core cluster, with modest contenDon, this can be done over a (long) weekend.

  The total number of MC events generated by a large collider experiment in one of their campaigns is of order a billion. –  An unfair calculaDon: 109 events / 102 papers = 107 MC events per paper

  A 2-‐D grid is well-‐matched to what the experiments can do today. A 3-‐D grid (10-‐20x as many points) is mathemaDcally possible, but not pracDcal.

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Working Smarter Why generate events here, where you have no chance to exclude them?

X X X X X X X X

Why generate events here, where they are probably already excluded?

Obviously this is risky; one doesn’t want to get too close to the exclusion line.

SDll, the benefit of doing this, plus pre-‐scaling the number of events in each producDon process by the cross-‐secDon can be substanDal – perhaps a factor of around 5.

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Working Smarter II

Once we have idenDfied the approximate region of the exclusion curve, we can generate more points near that curve.

AddiDonally, one can generate more events at each point (old and new).

If you could do this, one could start with a wide but sparse grid, and eliminate the risk from the previous “working smarter”

X X X X X X X X

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Why Don’t We Work Smarter?

  Today, we send the grid to the Grid.

  When we get it back, we can in principle iterate like I described, and send out a new set of jobs.

  A typical queue for a job this big can be weeks long – maybe a month. –  It’s not easy or fast to generate a billion events

–  While this grid is not a billion events, there are many other needs that need to be serviced – one can’t think of a single request in isolaDon.

  It’s simply not true that the grid is fast. The grid has a lot of capacity.

High capacity High speed

This is a problem we will need to solve if we ever want to go beyond a small number of 2D grids.

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Why Don’t We Work Smarter II?

  One reason why LeCompte & MarDn could run 80M events over a weekend while ATLAS is struggling with a few 10’s of million is because we used PGS.

  PGS reproduced ATLAS simulaDon to within ~15%. –  Some of this 15% was due to staDsDcal fluctuaDons in the ATLAS samples

–  Using PGS to interpolate between two nearby fully simulated points is probably good to a few percent.

–  QCD uncertainDes mean Prospino cross-‐secDons are probably good to (at best) 20%

  The experiments are hesitant about opening the door to PGS –  Concern that PGS will be misused – a concern that is not

enDrely unjusDfied. •  PGS will let us generate orders of magnitude more points than

we could otherwise. •  PGS can also facilitate lazy and sloppy analyses

This is not a trivial problem to solve. Any soluDon beginning with “Just” probably will not work.

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Summary

  mSUGRA is about the least compressed model imaginable.

  Whatever succeeds it is likely to be more compressed – there’s only one direcDon to go.

  “Ordinary” SUSY searches do surprisingly well with models with moderate compression.

  Models with more compression will require a more dedicated search. –  These searches oxen need to extend to regions of higher background.

–  There are two upcoming challenges: •  Advance the analysis techniques to reach parity with the mSUGRA searches •  Solve the compuDng problems associated with mulDdimensional grids

  It’s too soon to worry about the Higgs.

  Thanks to the organizers for inviDng me!

  Thanks to my colleague Steve MarDn, my ATLAS collaborators and my CMS friends

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ATLAS 1 fb-1 signal regions

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A Word on Backgrounds

  Consider Z+jets, followed by Z neutrinos.

  We will never get enough Z lepton events to fully constrain this: –  BF(Z vv) = 2 x BF(Z ee)

•  The Z has only the axial coupling to charged leptons, but the full vector coupling and the axial coupling to neutrinos.

–  Three neutrino families vs. two charged lepton families

–  Acceptance for neutrinos is 100%.

–  All in all, there is about a factor of 15x more Z neutrinos as Z leptons.

  The best we can do is use the events we have to constrain Monte Carlos –  The staDsDcal uncertainty will be driven by the number of events in the control region,

which means the uncertainDes are 4x larger than we would like.

–  This sDll falls like 1/sqrt(N), though.

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Published ATLAS/CMS Plots

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