Version 3 (modified by md987, 8 years ago) (diff)

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## Missing ET + Jets at the LHC

##### I. Collaboration
• Priscila de Aquino
• Fabio Maltoni
• Kentarou Mawatari
• Bettina Oexl
• Alexis Kalogeropoulos
• Benj Fuks
• Thomas in't Veld
• ....
##### II. Goal

To check if one could identify models from a specific signature. Imagine the LHC confirms the following signal:

p p > missing ET + jets

What could one say from this? We know there would be many different models that would fit into this signature. Therefore, there are a few questions we would like to answer:

• Can we divide these models into classes?
• If yes, what would be the best variable to separate these classes of models?
• Could we have a correspondence between real observables and these classes of theories?!
• What would be the best observables to do this job?
• What could we affirm for the invisible particles from thes observables? (For example, the number of particle? The spin? If it is massive or massless?)

To sumarize, the idea is to go from the signature -----> models, and not the usual other way around!

##### III. Strategy

First, we separe the production of invisible particles into 2 classes: production and decay.

Production: Pn => p p > X1 X2 .. Xn + jets (n from the number of invisibles)

Decay: Dn => p p > Y1 Y2 ... > X1 X2 .. Xn + jets

being X the invisible particles, and Y the parent particle which will generate the invisible particles.

Second, we identify classes of models separated into the definitions of production, decay and or a misture of both. In the last session here, we have a list of possible models, signatures divided into groups classified by product/decay.

On 4/11/2011 we have completed the table (section V) as much as possible, to organize the discussion how to start the analysis.

Third, we look for variables to distinguish models into the same classes. Examples are the mass (massive or massless) or spin of the invisible particle.

Finally, we look at observables and try to relate them to each class of models. We would like to find out if we have one (or more) observables that can be used to distinguish classes of models.

##### IV. References

[1] http://arxiv.org/abs/1108.1800 - Counting DM particles in LHC events

[2] http://arxiv.org/abs/1106.6199 - Model Independent jets plus missing energy searches

[3] http://arxiv.org/abs/1106.6199 - Monotops at the LHC

[4] http://arxiv.org/abs/1109.6014 - Large jet multiplicities and NP at the LHC

##### V. Organization of the project: models separated into classes

Model
 Signature Class Mass Spin

DD
RS
MGM
 graviton + jets Pn P1 P1 massive massive massless 2 2 2

Normal
SUSY
 (squark > neutralino + jet)(squark > neutralino + jet) (squark > neutralino + jets) neutralino 2*D1 P1.D1 1/2

USY GMSB
 (gluino> gravitino+jet) (gluino > gravitino+jet) (gluino> gravitino+jet) gravitino (squark > neutralino+jet) (R-parity violating) squark > (gluino > gravitino+jet) + jet (R-parity violating) neutralino + jets (?) 2*D1 P1.D1 D1 D1 P1 3/2 3/2 1/2 3/2 1/2

SM-like[[br]] || V + jet
 P1 1 4F interaction F + jet P1 1/2 S S + jet P1 0

M
 Z > v vbar + jets D2 1/2 4F + Dark g q > chi chibar jet P2 1/2
• We should take into account the possibility that the accompanying jets are either b's or coming from a top decay (like in the monotop paper).
• We should consider the most general form of effective interactions with dim=6 operators and in case dim=7 operators.....
• Let's also think about scenario's where dark matter is not only one kind of particle (like scalar+fermion).
##### VI. Summary of the discussions

04/11/2011

The idea is to start by Johan's paper (ref [2]), doing the same analysis that they did (as an example) with each of the classes of models given above. They have separated into 4 searches: monojet + missET, dijet + missET, 3jets + missET and >4jets + missET. They have plotted the differential cross section as a function of two varibles: missET and HT. Finally they have summarized their differential cross section results in a grid formed by both variables.

(1) The choice of variables

To begin our analysis, we need to check which are the optimal variables to choose. For sure missing ET is one of them. (Is HT the other one, as it was for Johan et al?) To find the other variable (to form the grid) we need to check the correlation btw variables.

Independent variables: missET, HT, Delta Eta, Delta Phi

(2) Counting Dark Matter

The signal must have jets + missET. Is missET formed by how many DM particles? Consider processes with 4FI (4-fermion-interactions): we could have processes with one DM particle as missET (q, q, Chi, q) or two (q, q, chi, chi). Can we find a strategy to differentiate both signals? In Giudice's paper (ref. [1]) they propose a way to do it based on Dn classes (see table above). Can we find a strategy to cound DM particles for Pn processes?