distinguishing dark matter in direct detection€¦ · superbayes v 1.36 log(m χ /gev) log(c 0 1 m...
TRANSCRIPT
![Page 1: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/1.jpg)
Distinguishing Dark Matter in Direct Detection
Andrew Cheek Supervisor: David Cerdeño
In Collaboration with Miguel Peiró and Elias Gerstmayr
![Page 2: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/2.jpg)
Dark Matter ❖ Despite what my Facebook
status might imply. I’m still prescribing to the DM paradigm.
![Page 3: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/3.jpg)
Dark Matter ❖ Despite what my Facebook
status might imply. I’m still prescribing to the DM paradigm.
![Page 4: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/4.jpg)
Direct Detection of Dark Matter
![Page 5: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/5.jpg)
Direct Detection of Dark Matter
![Page 6: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/6.jpg)
How many counts does an experiment expect?
![Page 7: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/7.jpg)
So Non-Relativistic
Spin Independent and Spin Dependent
This limiting case simplifies the Lagrangian and gives two generic cross-sections
![Page 8: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/8.jpg)
Spin Independent and Spin Dependent is Not Completely General
Uncharged DM vector mediated.
Anapole DM
![Page 9: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/9.jpg)
The Effective Field Theory Formalism
❖ The EFT formalism is similar to current work at the high scale frontier. Just in non-relativistic limit.
❖ In an attempt to remain model independent, Fitzpatrick et al. introduced basic building blocks that are Galilean invariant and Hermitian.
![Page 10: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/10.jpg)
The list of NR Operators❖ Since we’re only interested in elastic scattering, this
formalism only considers four-field operators.
❖ Remembering the NR limit is being taken, we combine operators up to quadratic in momentum.
![Page 11: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/11.jpg)
EFT: The “New” Differential Rate❖ The differential rate now takes a different form.
❖ The operator behaviour is embedded into the new EFT form factors.
❖ The Form Factors are are defined by
![Page 12: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/12.jpg)
The Spectrum for Different Operators.
![Page 13: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/13.jpg)
If we detect DM now, NREFT signals will be degenerate.
Having complementarity of different targets means we can distinguish between the real DM model and one that purely mimics it.
![Page 14: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/14.jpg)
How Does The Degeneracy look in an reconstruction?
Need to simulate the new generation of experiment.
Come up with some interesting candidate signals for this setup.
![Page 15: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/15.jpg)
SuperBayeS v 1.36
log(mχ/GeV)
log(
C10 m
v2 )
Profile likelihood
Flat priors
0 1 2
−5
−4
−3
−2
−1SuperBayeS v 1.36
log(C10 mv
2)
log(
C100
mv2 )
Profile likelihood
Flat priors
−5 −4 −3 −2 −1
0
1
2
3
4
O1
O10
BP1(O1+O10)
0 5 10 15 200
5
10
15
20
25
Er [keV]
Cou
nts
How does this Look in an Reconstruction?Just XENON G2
![Page 16: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/16.jpg)
SuperBayeS v 1.36
log(mχ/GeV)
log(
C10 m
v2 )
Profile likelihood
Flat priors
0 1 2
−5
−4
−3
−2
−1
SuperBayeS v 1.36
log(C10 mv
2)
log(
C100
mv2 )
Profile likelihood
Flat priors
−5 −4 −3 −2 −1
0
1
2
3
4SuperBayeS v 1.36
log(mχ/GeV)
log(
C10 m
v2 )
Profile likelihood
Flat priors
0 1 2
−5
−4
−3
−2
−1
Combining Xenon and Germanium SuperBayeS v 1.36
log(C10 mv
2)
log(
C100
mv2 )
Profile likelihood
Flat priors
−5 −4 −3 −2 −1
0
1
2
3
4
![Page 17: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/17.jpg)
SuperBayeS v 1.36
log(mχ/GeV)
log(
C80 m
v2 )
Profile likelihood
Flat priors
0 1 2
−1
0
1
2
3
4SuperBayeS v 1.36
log(C10 mv
2)
log(
C80 m
v2 )
Profile likelihood
Flat priors
−5 −4 −3 −2 −1
−1
0
1
2
3
4
Complementarity May Not be Enough
![Page 18: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/18.jpg)
Annual modulation: a Potential Degeneracy Breaker
![Page 19: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/19.jpg)
Annual modulation: a Potential Degeneracy Breaker
![Page 20: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/20.jpg)
Annual modulation: a Potential Degeneracy Breaker
![Page 21: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/21.jpg)
SuperBayeS v 1.36
log(C10 mv
2)
log(
C80 m
v2 )Profile likelihood
Flat priors
−5 −4 −3 −2 −1
−1
0
1
2
3
4SuperBayeS v 1.36
log(mχ/GeV)
log(
C80 m
v2 )
Profile likelihood
Flat priors
0 1 2
−1
0
1
2
3
4
Annual modulation: a Potential Degeneracy Breaker
![Page 22: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/22.jpg)
SuperBayeS v 1.36
log(C10 mv
2)
log(
C80 m
v2 )Profile likelihood
Flat priors
−5 −4 −3 −2 −1
−1
0
1
2
3
4SuperBayeS v 1.36
log(mχ/GeV)
log(
C80 m
v2 )
Profile likelihood
Flat priors
0 1 2
−1
0
1
2
3
4SuperBayeS v 1.36
log(mχ/GeV)
log(
C80 m
v2 )
Profile likelihood
Flat priors
0 1 2
−1
0
1
2
3
4
Annual modulation: a Potential Degeneracy Breaker
![Page 23: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/23.jpg)
SuperBayeS v 1.36
log(C10 mv
2)
log(
C80 m
v2 )Profile likelihood
Flat priors
−5 −4 −3 −2 −1
−1
0
1
2
3
4SuperBayeS v 1.36
log(C10 mv
2)
log(
C80 m
v2 )Profile likelihood
Flat priors
−5 −4 −3 −2 −1
−1
0
1
2
3
4SuperBayeS v 1.36
log(mχ/GeV)
log(
C80 m
v2 )
Profile likelihood
Flat priors
0 1 2
−1
0
1
2
3
4SuperBayeS v 1.36
log(mχ/GeV)
log(
C80 m
v2 )
Profile likelihood
Flat priors
0 1 2
−1
0
1
2
3
4
Annual modulation: a Potential Degeneracy Breaker
![Page 24: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/24.jpg)
Then we were Scooped!
…
![Page 25: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/25.jpg)
Thank You!
![Page 26: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/26.jpg)
The Exclusion Plots for each Coefficient
![Page 27: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/27.jpg)
Simplified Models for EFTs
❖ Simplified models approach is where only operators at leading order and one single type of mediator is considered.
![Page 28: Distinguishing Dark Matter in Direct Detection€¦ · SuperBayeS v 1.36 log(m χ /GeV) log(C 0 1 m 2 v) Profile likelihood Flat priors 0 1 2 −5 −4 −3 −2 −1 SuperBayeS v](https://reader034.vdocuments.us/reader034/viewer/2022052013/6029d1367f41b8750c5fd72c/html5/thumbnails/28.jpg)
Recently it was shown that the standard SI interaction is contributed by 8 independent
coefficients.