activity
20192022
most citedReport on the ECFA Early-Career Researchers Debate on the 2020 European Strategy Update for Particle Physics

2 citations · 3 across the 2 of their papers we have counts for

collaborators

7 papers

hep-ph2022

Jets and Jet Substructure at Future Colliders

Ben Nachman, Salvatore Rappoccio, Nhan Tran +24

Even though jet substructure was not an original design consideration for the Large Hadron Collider (LHC) experiments, it has emerged as an essential tool for the current physics p…

physics.ins-det20211 cited

Results of the 2021 ECFA Early-Career Researcher Survey on Training in Instrumentation

ECFA Early-Career Researcher Panel, :, Anamika Aggarwal +74

The European Committee for Future Accelerators (ECFA) Early-Career Researchers (ECR) Panel was invited by the ECFA Detector R&D Roadmap conveners to collect feedback from the Europ…

physics.ins-det2020

Construction and commissioning of CMS CE prototype silicon modules

B. Acar, G. Adamov, C. Adloff +327

As part of its HL-LHC upgrade program, the CMS Collaboration is developing a High Granularity Calorimeter (CE) to replace the existing endcap calorimeters. The CE is a sampling cal…

physics.ins-det2020

The DAQ system of the 12,000 Channel CMS High Granularity Calorimeter Prototype

B. Acar, G. Adamov, C. Adloff +327

The CMS experiment at the CERN LHC will be upgraded to accommodate the 5-fold increase in the instantaneous luminosity expected at the High-Luminosity LHC (HL-LHC). Concomitant wit…

hep-ex20202 cited

Report on the ECFA Early-Career Researchers Debate on the 2020 European Strategy Update for Particle Physics

N. Andari, L. Apolinário, K. Augsten +118

A group of Early-Career Researchers (ECRs) has been given a mandate from the European Committee for Future Accelerators (ECFA) to debate the topics of the current European Strategy…

hep-ph2019

The Machine Learning Landscape of Top Taggers

G. Kasieczka, T. Plehn, A. Butter +24

Based on the established task of identifying boosted, hadronically decaying top quarks, we compare a wide range of modern machine learning approaches. Unlike most established metho…