High Pressure X-Ray Diffraction Study of UMn2Ge2

Uranium manganese germanide, UMn2Ge2, crystallizes in body-centered tetragonal ThCr2Si2 structure with space group I4/mmm, a = 3.993A and c = 10.809A under ambient conditions. Energy dispersive X-ray diffraction was used to study the compression behaviour of UMn2Ge2 in a diamond anvil cell. The sample was studied up to static pressure of 26 GPa and a reversible structural phase transition was observed at a pressure of ~ 16.1 GPa. Unit cell parameters were determined up to 12.4 GPa and the calculated cell volumes were found to be well reproduced by a Murnaghan equation of state with K0 = 73.5 GPa and K' = 11.4. The structure of the high pressure phase above 16.0 GPa is quite complicated with very broad lines and could not be unambiguously determined with the available instrument resolution

Low Temperature Neutron Diffraction Study of MnTe

Investigation of transport and magnetic properties of MnTe at low temperatures sInvestigation of transport and magnetic properties of MnTe at low temperatures showed anomalies like negative coefficient of resistance below 100K and a sharp rise in susceptibility at around 83K similar to a ferromagnetic transition. Low temperature powder neutron diffraction experiments were therefore carried out to understand the underlying phenomena responsible for such anomalous behavior. Our study indicates that the rise in susceptibility at low temperatures is due to strengthening of ferromagnetic interaction within the plane over the inter plane antiferromagnetic interactions.Comment: Appearing in J. Magn. Magn. Mate

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