1. Search for magnetic monopoles with ANTARES
By Nicolas PICOT CLEMENTE
CNRS/Université de la Méditerranée/CPPM

2. Outlines
Introduction
Magnetic monopole signal in ANTARES
Analysis
Conclusion

3. Detection principle gč m n p, a p nm g 43°
Sea floor p nm
p, a g
Reconstruction of m trajectory (~ n) from timing and position of PMT hits
interaction
Cherenkov light from m
3D PMT array
Simulation de l’interaction nNmX
Section efficace vs énergie
Absorption dans la Terre
Simulation des bruits de fond
muons atmosphériques
muons induits par n atmosphériques

4. Detection principle gč M.M.
3D PMT array
Cherenkov light from M.M. gč
Sea floor
M.M.
Reconstruction of MM trajectory from timing and position of PMT hits

5. Introduction of magnetic monopoles
Initially introduced by Dirac in 1931:
Make symmetric Maxwell’s equations. e-
M.M.
Imply the quantization of the electric charge.
Magnetic charge is given by
The smallest magnetic charge is the Dirac charge gD, where k=1.

6. Introduction of magnetic monopoles
’t Hooft and Polyakov in 1974:
Any unified gauge theory in which U(1)E.M. is embedded in a spontaneously broken semi-simple gauge group necessarily contains M.Ms.
Transition example with the minimal GUT group:
MM appear with charge g=gD at the first transition.
In this typical case the monopole mass is about ~ 1016 GeV with a radius of the order ~ cm.
Predicted magnetic monopole’s masses : to GeV (depending on the unified gauge group).

7. Acceleration of magnetic monopoles in the Universe
Energy gain in a magnetic coherent field:
Magnetic monopoles with masses below 1014 GeV could be relativistic (with extragalactic sheets expecting to dominate the spectrum).
M.M.
Estimated energy loss when crossing the Earth is ~ 1011 GeV.
M.M. with masses up to about 1014 GeV are expected to cross the Earth and be relativistic.

8. Magnetic monopole’s signal in ANTARES
Direct Cherenkov emission  > 0.74 :
nsea water ~ 1.35
Direct Cherenkov g from a MM with g=gD.
x 8500
Number of photons emitted by a MM with the minimal charge gD ~ 68.5 e, compared to a muon of same velocity is about ~ 8500 more!
Cherenkov g from a m.

9. Magnetic monopole’s signal in ANTARES
Direct Cherenkov emission  > 0.74 :
nsea water ~ 1.35
Direct Cherenkov g from a MM with g=gD.
g from MM
x 8500
Number of photons emitted by a MM with the minimal charge gD ~ 68.5 e, compared to a muon of same velocity is about ~ 8500 more!
Cherenkov g from delta-rays.
Cherenkov g from a m.
g from d-rays
g from m
Indirect Cherenkov emission  > 0.51 :
The energy transferred to electrons allows to pull out electrons (d-rays), which can emit Cherenkov light.

10. Signal examples for upgoing particles
Time
Elevation
Line number
Hit
Upgoing neutrino event.

11. Signal examples for upgoing particles
Time
Elevation
Line number
Hit
Upgoing magnetic monopole event with b ~ 0.99.
Upgoing neutrino event.

12. Technical aspect
Magnetic monopole simulation
Trigger efficiency
Reconstruction algorithm

13. Magnetic monopole Simulation
Monte Carlo Muon:
Monte Carlo Monopole:
Charge e
Equivalent electric charge g=68.5e
Velocity c
Velocity variable
d-rays
Optimisation of the CAN generation:
Generation area
Detector area
3 Labsorption
Simulation of MMs at different velocities (0.51 < b < 1).
12 Labsorption

14. Triggers L1 Trigger implemented:
= 1 local coincidence of 2 OMs in 20ns
or one large amplitude hit.
Trigger implemented:
The 3N = in the detector within 2.2s,
causaly relied.

15. Triggers L1 Trigger implemented:
= 1 local coincidence of 2 OMs in 20ns
or one large amplitude hit.
Trigger implemented:
The 3N = in the detector within 2.2s,
causaly relied.
The 2T3: Coincidence of 2 T3 clusters within 2,2 ms (developped at CPPM)
Cluster T3 or
100 ns
200 ns

16. Triggers L1 Trigger implemented: Dedicated trigger (not implemented):
= 1 local coincidence of 2 OMs in 20ns
or one large amplitude hit.
Trigger implemented:
The 3N = in the detector within 2.2s,
causaly relied.
The 2T3: Coincidence of 2 T3 clusters within 2,2 ms, (developped at CPPM).
Cluster T3
Dedicated trigger (not implemented): or
The 3S: in the detector within 3.5 ms, causaly relied.
Is the 3S need to be implemented ?
100 ns
200 ns

17. Trigger efficiency of the detector
Trigger Efficiency: (Evt Triggered) / (Evt > 5 hits)
The 2T3 trigger is much more efficient,
no need to implement the dedicated 3S trigger.

18. Muon reconstruction algorithms BBFit (developped at CPPM)
Aart
BBFit (developped at CPPM)
Both are based on the minimisation of time residuals, i.e.:
Texp-Tobs
5 steps applied with a likelihood maximisation.
2 fit applied, a track fit and a bright point fit (c² minimisation).
Use alignment and compass information.
Alignment and compass doesn’t take into consideration.
Gives an angular resolution of about 0.3 ° for Em ~ 10 TeV.
Gives an angular resolution of about 1 ° for Em ~ 10 TeV.
Long to process.
Used for point source search, …
Very fast.
Used online,for diffuse flux, …

19. Analysis

20. Analysis strategy
The analysis follow the diffux flux ANTARES blinding policy.
Cuts optimisations using the Model Rejection Factor (MRF) on MonteCarlo data.
Real and MonteCarlo data comparison with a sample of data Expected sensitivity obtained.
Apply cuts on the remaining data. Limit obtained.

21. Atmospheric background light in sea water p  p
ANTARES much more sensitive to upgoing magnetic monopoles.
upgoing
downgoing
Only upgoing magnetic monopoles considered for instance.

22. Analysis using Aart’s algorithm
for fast upgoing monopoles (b > 0.74)

23. Definition of discriminative variables
Misreconstructed
M.M. with b~0.75
M.M. with b~0.85
M.M. with b~0.99
Atm. muons
Atm. neutrinos up.
Atm. neutrinos do.
Distribution of the reconstructed zenith angle for atmospheric background events and for upgoing monopoles.
Upgoing magnetic monopoles Selection of only upgoing reconstructed events (qzen < 90°).

24. Definition of discriminative variables
Remove most of misreconstructed events with the fit quality factor L.
Atm. muons
Atm. neutrinos up.
Atm. neutrinos do.
M.M. with b~0.75
M.M. with b~0.85
M.M. with b~0.99
Distribution of the fit quality factor L for upgoing reconstructed atmospheric background events and monopoles.

25. Definition of discriminative variables
Large amount of light Selection applied on the number of cluster of hitted floors (T3).
Atm. muons
Atm. neutrinos up.
Atm. neutrinos do.
M.M. with b~0.75
M.M. with b~0.99
Cluster T3 or
100 ns
200 ns
Distribution of the number of cluster of hitted floors T3 for upgoing reconstructed atmospheric background events and monopoles.

26. Model Rejection Factor (MRF)
Upper limit calculation with 90% C.L. :
, with m90 coming from Feldman-Cousins tables, T the data taking period and Seff the effective area .
m90 is obtained with the number of expected background events b and the number of detected events n known.
Valid for low statistics (poissonian distributions).
Sensitivity calculation with 90% C.L. :
,with
Sum over the number of detected events (not known), considering a poissonian distribution.
The MRF consists in the minimisation of the sensitivity applying various cuts.

27. Different optimal T3 cut found for each velocity.
MRF optimisation
Discriminative variables :
T3, the number of cluster of hitted floors.
L, the fit quality factor.
Optimisation of the 90% C.L. sensitivity as a function of the couple (L,T3) cut applied.
Example with L > -6:
Different optimal T3 cut found for each velocity.
The best integrated sensitivity gives the best couple (L,T3) cut.

28. Data/MC comparison Sample of ~ 10 days of 12-line data taken.
Best match between data and MC observed from L > -5.5.
Which is corroborated with the T3 distributions.

29. Expected 90% C.L. sensitivity with the 12-line ANTARES detector with Aart’s algorithm
The best integrated 90% C.L. sensitivity is found for T3 > 110, with L > -5.5, after one year of data taking with the 12-line detector, expecting ~ 1.07 background events.
Results from the requirement that galactic magnetic fields must be conserved

30. Consistency check using BBFit algorithm

31. Definition of discriminative variables
Primary cut: we keep only tracks with tc² < bc².
Upgoing magnetic monopoles Selection of only upgoing reconstructed events (qzen < 90°).
Remove most of misreconstructed events with the track fit quality factor tc².
Large amount of light Selection applied on the number of storeys (NHit) used in the track fit.

32. MRF optimisation MRF example:
The best integrated sensitivity is found for the couple (tc²,NHit)=(16,65).

33. Data/MC comparison
We considere a good match between data and MC until tc² ~ 8.
Corroborated with the NHit distributions.

34. Expected 90% C.L. sensitivity with the 12-line ANTARES detector with BBFit algorithm
The best integrated 90% C.L. sensitivity is found for NHit > 60, with tc² < 8, after one year of data taking with the 12-line detector, expecting ~ 1.6 background events.

35. Conclusion
The same study was done for different detector configurations.
Need to combine results.
Expected sensitivity based on a M.C. study.
Awaiting the collaboration approval to unblind data.
Complementary studies in progress
Slower magnetic monopoles.
Downgoing magnetic monopoles.