Linear Collider Workshop 2017, Strasbourg Characterisation of the SuperKEKB induced background with PLUME ladders D.Cuesta on behalf of the BEAST II collaboration October 24th 2017 Daniel Cuesta LCWS 2017 October 24th 2017 1 / 21
Outline 1 SuperKEKB & Belle II 2 Belle II commisionning : BEAST II 3 PLUME detector 4 Study of the machine induced background Daniel Cuesta LCWS 2017 October 24th 2017 2 / 21
1 SuperKEKB & Belle II 2 Belle II commisionning : BEAST II 3 PLUME detector 4 Study of the machine induced background Daniel Cuesta LCWS 2017 October 24th 2017 3 / 21
SuperKEKB Asymetric e + e nano beam circular collider aiming at delivering the highest instaneous luminosity ever reached : 8.10 35 cm 2 s 1 Beam caracteristics Energy : HER = 7 GeV LER = 4 GeV Size(x,y,z) : HER = (11 µm, 62 nm, 4.9 mm) LER = (10 µm, 48 nm, 4.7 mm) Current : HER = 2.62 A LER = 3.60 A Schedule Phase I : 2017 Single beams Phase II :Feb-July 2018 First collisions Phase III : End 2018 Physics run Daniel Cuesta LCWS 2017 October 24th 2017 4 / 21
Machine induced background Nano-beams and high lumonisity induce very large amount of background particles Single Beam Touschek : Elastic scattering between particles within the same bunch E 3 ρ σ Beam Gas : Coulomb scattering between beam particles and atoms inside the vacuum tube P I Synchroton : Radiation emitted by charged particles bended in a magnetic field E 2 B 2 Beam Beam Radiative BhaBha : e + e scattering with ISR or FSR Two photon pair QED production : e + e scattering with pair production Injection Noise : New bunches continously injected are unstable and lose particles Daniel Cuesta LCWS 2017 October 24th 2017 5 / 21
Belle II Scientific Motivation Look for quantum manifestations of Beyond SM physics with measurments of unprecedented precision with B and D mesons and τ leptons High precision detector Belle II vertex detector Daniel Cuesta LCWS 2017 October 24th 2017 6 / 21
Belle II Scientific Motivation Look for quantum manifestations of Beyond SM physics with measurments of unprecedented precision with B and D mesons and τ leptons High precision detector Belle II vertex detector Daniel Cuesta LCWS 2017 October 24th 2017 6 / 21
1 SuperKEKB & Belle II 2 Belle II commisionning : BEAST II 3 PLUME detector 4 Study of the machine induced background Daniel Cuesta LCWS 2017 October 24th 2017 7 / 21
Beam Exorcism for A STable belleii experiment Some numbers about Background 50% Energy deposition in calorimeter 90% Occupancy in the vertex detector Understand and control background processes is mandatory for Belle II physics program and for the safety and efficiency of the Belle II detector BEAST II Goals Commissioning of Belle II experiment Characterize backgrounds Validation of the inner tracker safe operation possibility Validate background simulation, further extrapolated up to highest luminosities Fine tune beam parameters and control background Daniel Cuesta LCWS 2017 October 24th 2017 8 / 21
BEAST II in inner tracker volume All Belle II detectors will take data but for safety reasons the Belle II inner tracker is not fully installed Inner tracker volume is equipped with : PXD FANGS CLAWS PLUME Daniel Cuesta LCWS 2017 October 24th 2017 9 / 21
1 SuperKEKB & Belle II 2 Belle II commisionning : BEAST II 3 PLUME detector 4 Study of the machine induced background Daniel Cuesta LCWS 2017 October 24th 2017 10 / 21
PLUME detector R&D in perspective of an inner tracker for the ILC made by a collaboration from Bristol university, DESY and IPHC http ://www.iphc.cnrs.fr/plume.html MIMOSA 26 CMOS sensor Provided by IPHC PICSEL group : Ladder 576x1152 pixels Pitch : 18.4 µm 2 Integration time : 115 µs Spatial resolution : 3 µm Used in EUDET beam telescope Double sided detection mini tracker 18x10 6 monolitic pixels Very low material budget : 0.4% X 0 Self stiffened Daniel Cuesta LCWS 2017 October 24th 2017 11 / 21
PLUME in BEAST II PiXe DAQ with full remote control Peek support N2 dry air flow = 4L.min 1 Power consumption = 750 mw / sensor One ladder almost parallel to beam axis ( 2 ) One ladder inclined with 18 to scan the background at different radii Daniel Cuesta LCWS 2017 October 24th 2017 12 / 21
On-going integration on site at KEK : Fall 2017 Full system has been installed on the beam pipe and last tests are performed before inserting the system in the Belle II detector Daniel Cuesta LCWS 2017 October 24th 2017 13 / 21
1 SuperKEKB & Belle II 2 Belle II commisionning : BEAST II 3 PLUME detector 4 Study of the machine induced background Daniel Cuesta LCWS 2017 October 24th 2017 14 / 21
Background simulation BEAST II luminosity will be in the end about a few 10 34 cm 2 s 1 results of BEAST II will be extrapolated up to almost 10 36 cm 2 s 1 Goal validate the simulation and the variation of the background with the beam parameters so that we can trust such an extrapolation Method Generators (SAD, BHWide...) simulate beam circulation and physics processes Position and 4-momenta of lost particles given to Belle II simulation framework based on Geant4 Simulation sample production is still on going : background dependency to collimators and beam parameters makes it difficult to simulate Daniel Cuesta LCWS 2017 October 24th 2017 15 / 21
Information provided by PLUME Background analysis with PLUME ladders is based on correlation between background processes and hit patterns on PLUME Loop through PLUME Secondaries from showers in surrounding materials 1MeV Cross PLUME Primaries from IP 15MeV or E beam Only produce by BeamBeam Background characterization thanks to double-sided detection Rate in r,z and t Pattern recognition process identification Track reconstruction Momentum sensitivity and process identification Daniel Cuesta LCWS 2017 October 24th 2017 16 / 21
On-line analysis Thanks to PLUME geometrical configuration we can scan occupancy at different radii and z R [mm] Rate [Hits/11.5ms/cm 2 ] 120 110 100 90 80 70 60 50 40 100 50 0 50 100 Z [mm] 80 70 60 50 40 30 20 ] 2 Rate [Hits/11.5ms/cm Daniel Cuesta LCWS 2017 October 24th 2017 17 / 21
Pattern recognition : Method Implementation and optimisation of pattern recognition algorithm Pattern recognition algorithm is based on different variables like : Distance between hits on same module Distance between hits on opposite module Alignment Number of hits on ladder Then we made a dedicated study of these variable to get selection criterias Daniel Cuesta LCWS 2017 October 24th 2017 18 / 21
Pattern recognition : Preliminary performances Proportion of hits properly assigned OS : 77% TS : 73% OSL : 70% TSL : 74% True and Reconstructed hit pattern vs pattern category Daniel Cuesta LCWS 2017 October 24th 2017 19 / 21
Track Fitting Track Fitting One ladder = 2 Hits + IP constraint Most of particles induced by machine background have small momentum Important multiple scattering Challenge : Implement Covariance Matrix of Multiple scattering (on going work) Daniel Cuesta LCWS 2017 October 24th 2017 20 / 21
Track Fitting Track Fitting One ladder = 2 Hits + IP constraint Most of particles induced by machine background have small momentum Important multiple scattering Challenge : Implement Covariance Matrix of Multiple scattering (on going work) Daniel Cuesta LCWS 2017 October 24th 2017 20 / 21
Conclusion and Outlook Very High luminosity Large amount of Background Dedicated background study during Belle II commisioning : BEAST II PLUME in BEAST = First fully integrated CMOS pixaleted sensors in an e + e environment Daniel Cuesta LCWS 2017 October 24th 2017 21 / 21