Kinematics and strain rates of the Eastern Himalayan Syntaxis from new GPS campaigns in Northeast India

Newly acquired GPS data along transects across Himalaya in Eastern Himalayan Syntaxis (EHS) reveal a clockwise rotation of rigid micro-plate comprising part of Brahmaputra valley, NE Himalaya and Northern Myanmar that rotates about a pole located at 14.5°N, 100.8°E at an angular rate of 1.75 ± 0.12°/Myr. The EHS is being torn-off from the main Indian Plate as a rigid block around which the kinematic clockwise rotation of Tibetan GPS sites toward the Sichuan-Yunnan region occurs in the Eurasia fixed frame. The residual velocity field of the newly acquired data estimated after removing the rotation that minimizes the GPS rates around EHS show a clear NE motion of the EHS sites, indentation of the rigid Indian plate into a less rigid area of the Eurasian plate. Themost extensive EHS zones of compression and shortening are in the direction of indenter convergence, with average values ranging between ~50–100 nanostrain/year. Along the frontal segment of EHS, from NWto SE, the shortening rate is reduced from the local maximum value of 160 to ~80 nanostrain/year, thus indicating a possibly locked fault patch of Mishmi or Lohit thrusts, the southernmost part of segment activated during the large 1950 Assam earthquake, Mw 8.6. An elastic block-model was invoked to infer the average slip rates of sections around EHS and to estimate an average locking depth of ~15 km. The slip rate perpendicular to the locked sector of EHS reaches 32.4mm/year and permits to roughly infer a recurrence time of ~200 year for an earthquake as energetic as the 1950 Assam event.Published15-261T. Deformazione crostale attivaJCR Journa

Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter

International audienceA novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated

Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter

International audienceA novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated

Using graph neural networks to reconstruct charged pion showers in the CMS High Granularity Calorimeter

International audienceA novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated