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Search for magnetic monopoles with ANTARES

Explore the search for magnetic monopoles in ANTARES, reconstruction of trajectories, detection principles, and signal analysis with Cherenkov light emission. Learn about magnetic monopole simulation, trigger efficiency, and technical aspects.

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Search for magnetic monopoles with ANTARES

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  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 3D PMTarray p, a nm p m Cherenkov light from m gč nm g 43° Sea floor m interaction Reconstruction of m trajectory(~ n)from timing and position of PMT hits n

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

  5. Introduction of magnetic monopoles e- M.M. Initially introduced by Dirac in 1931: Make symmetric Maxwell’s equations. 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 appearwith charge g=gDat the first transition. In this typical case the monopole mass is about ~ 1016GeVwith a radius of the order ~ 10-28 cm. Predicted magnetic monopole’s masses : 108 to 1017 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). 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. M.M.

  8. Magnetic monopole’s signal in ANTARES Direct Cherenkov emission  > 0.74 : nseawater~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 : nseawater~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 Line number Hit Elevation Time Upgoing neutrino event.

  11. Signal examples for upgoing particles Line number Hit Elevation Upgoingmagnetic monopole eventwithb ~ 0.99. Time 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 12 Labsorption Simulation of MMs at different velocities (0.51 < b < 1).

  14. Triggers L1 = 1 local coincidence of 2 OMs in 20ns or one large amplitude hit. Trigger implemented: The 3N = 5 in the detector within 2.2s, causaly relied.

  15. Triggers L1 = 1 local coincidence of 2 OMs in 20ns or one large amplitude hit. Trigger implemented: The 3N = 5 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 = 1 local coincidence of 2 OMs in 20ns or one large amplitude hit. Trigger implemented: The 3N = 5 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: 7 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 (developpedat CPPM) Aart 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  µ 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 Atm. muons Atm. neutrinos up. Atm. neutrinos do. Distribution of the reconstructedzenith angle for atmospheric background events and for upgoing monopoles. Upgoing magnetic monopoles Selection of only upgoing reconstructed events (qzen < 90°). M.M. with b~0.75 M.M. with b~0.85 M.M. with b~0.99

  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 Distribution of the fit quality factor L for upgoingreconstructedatmospheric background events and monopoles. M.M. with b~0.85 M.M. with b~0.99

  25. Definition of discriminative variables Large amount of light Selection applied on the number of cluster of hitted floors (T3). Cluster T3 or 100 ns 200 ns Atm. muons Atm. neutrinos up. Atm. neutrinos do. M.M. withb~0.75 M.M. withb~0.99 Distribution of the number of cluster of hittedfloorsT3 for upgoingreconstructedatmospheric 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. 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. ExamplewithL > -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.

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