Event reconstruction with free streaming data in the CBM experiment

1. Q Qmax Qthr Number of tracks arriving in a shadow of other tracks (within dead-time interval) • Track length (from MC) • Particle type and energy(from MC) • MPV(E), σ(E) of Landau distribution (gas specific, taken from HEED) Inefficiency = Number of primary electrons Total number of tracks Number of secondary electrons • Gas gain (from beam tests) Number of secondary electrons pad by pad • Segmentation • Track position with respect to pads • Spot radius (from beam tests) Evgeny Kryshen, PNPI, Russia • Mean noise in electrons, smeared with Gaussian + noise pad by pad CBM experiment • Parameters of readout electronics: • Dynamic range in electrons • Threshold in electrons Amplitude in ADC channels pad by pad • Time-stamp • Dead time Setup with MuCh detector Setup with RICH detector Clusterization • Time as 4th dimension ECAL TOF RICH or MUCH TRDs Parameters of cluster deconvolution algorithm Cluster deconvolution, hit finding PSD MVD +STS Timing challenges • New DAQ approach in HEP: self-triggered RO + free streaming data. • No event tags. Only time-stamp information for detector hits. The challenge – reconstruct and separate events from the time-stamp info. • Event rates up to 107 Hz (in average 100 ns interval between events) • Average number of tracks in CBM acceptance for MB events ~ 160 • Beam for CBM is effectively debunched – in the first approximation we can assume constant probability to have event in a given time slot -> exponential distribution of time between events • At 107 Hz, 10% of events will be separated by less than 10 ns STS track reconstruction and momentum determination MVD determination of secondary vertices RICH identification of electrons MuCh identification of muons TRD identification of electrons with momenta above 1.5 GeV/c TOF time-of-flight measurement needed for hadron identification ECAL measurement of photons and neutral particles PSD determination of the collision centrality Event reconstruction with free streaming data in the CBM experiment MuCh detector Distribution of STS and MUCH hits in time Digitization scheme in MUCH Timing inefficiencies Example: Inefficiency = 0.25 Inefficiency as function of radius: • Epoch approach • Event by event approach Examples of time slices Clusterization in time No time-stamp smearing 4 ns time-stamp smearing • Fired pads • Dead pads