EFA/DHCal development at NIU Vishnu V. Zutshi Northern Illinois University

V. Zutshi, Cornell LC Workshop Introduction Primarily interested in exploring the (semi)digital hadron calorimeter option in general, with scintillator as the active material in particular This talk focuses on the algorithm development aspects Results are preliminary 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop The Calorimeter 30 layer Silicon-Tungsten ECAL 5mm x 5mm transverse segmentation 34 layer Scintillator-Steel HCAL Transverse cell size varied between 2-9cm2 Cells are projective in q and f No support structures/dead spaces Immersed in 5T magnetic field 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop N vs. E 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop Sampling Weights Determined from 10 GeV charged pions Minimize: (1/N) S (E0 – aiLi)2 where E0 is the incident energy ai is the weight for layer i Li is the energy/number of hits in lyr i For simplicity i=2 considered here 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop Energy Resolution single threshold 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop Energy Resolution dual threshold 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop Energy Resolution 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop “Density” Need a hierarchy for cells, in the absence of an energy measurement Clumpiness of surroundings ? A simple-minded realization of this: di = k S (1/Rij) where Rij is the angular distance between cell ‘i’ and cell ‘j’ 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop Position Resolution Since Energy-Flow invariably involves associating clusters near an extrapolated track……. Best way to this in a digital calorimeter? Used charged pions in the HCAL Resolution is defined w.r.t. the extrapolated Monte Carlo generated track 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop Position Resolution 5 GeV p 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop 10 GeV p Measured relative to the energy weighted resolutions Cell area at first layer=2cm2 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop 10 GeV p Cell area at first layer=6cm2 Cell area at first layer=9cm2 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop Clustering Local density maxima chosen as seeds Membership of each cell in the seed clusters decided with a distance function Only unique membership considered here Calculate centroids Iterate till distortion is below some threshold 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop p0  gg Density weighted q-f High asymmetry 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop p0  gg Density weighted q-f 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop 10 GeV p0 Recon. energy Recon. mass 18% 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop 20 GeV p0 Recon. energy Recon. mass 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop Energy Asymmetry A = abs(Eg1 – Eg2)/(Eg1 + Eg2) 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop S+  pp0 p Density weighted q-f p0 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop S+  pp0 Eflow Cal only Erec/Egen 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop S+  np+ n ECAL Density weighted q-f HCAL p 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop “Jet” Reconstruction A 0.7 simple cone (no splits or merges) algorithm used Operates on a pt ordered list of final state, visible MC particles Pt weighted q-f used as centroid 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop Z  jj Generated mass 11/28/2018 V. Zutshi, Cornell LC Workshop

Further “Jet” Reconstruction Use the generated jet centroid as the seed Grab clusters within 0.7 of seed Extrapolate MC tracks into calorimeter Replace E with p for matched clusters 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop Jet Erec/Egen ~60% better Eflow Calorimeter only 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop Jet Erec/Egen Eflow digital (2cm2 cells) Eflow analog 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop jet-jet mass EF analog 2 cm2 cells 50% improvement over calo EF digital Calo only 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop jet-jet mass 6 cm2 cells Digital(1-bit) ~10% worse 11/28/2018 V. Zutshi, Cornell LC Workshop

Photon Reconstruction (in jets) 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop Short Term Goals Better track extrapolation taking into account the energy loss and scattering in the material Introduce particle id to supplement pattern recognition Alternative measures of density The long road to optimization 11/28/2018 V. Zutshi, Cornell LC Workshop

V. Zutshi, Cornell LC Workshop Summary A first version of a ‘digital’ Energy Flow algorithm implemented with encouraging results Detailed optimization studies beginning Not discussed here, but hardware prototyping studies proceeding Hope to cross-fertilize hardware design with algorithm development 11/28/2018 V. Zutshi, Cornell LC Workshop