387 citations · 1.3k across the 48 of their papers we have counts for
49 papers
An improved direct limit on the muon electric dipole moment
2 Collaboration, D. P. Aguillard, T. Albahri +192
A limit on the permanent electric dipole moment (EDM) of the positive muon is presented based on data from the Fermilab Muon g-2 Experiment taken between 2019 and 2020. The trackin…
Final Report on the Measurement of the Positive Muon Anomalous Magnetic Moment at Fermilab to 127 ppb
2 Collaboration, D. P. Aguillard, T. Albahri +192
This report details the final measurement of the muon magnetic anomaly, , by the Muon experiment at Fermi National Accelerator Laboratory (FNAL), using positiv…
Direct proton transfer on Ar supports the presence of a charge density bubble linked to a novel nuclear structure below Ca
Daniele Brugnara, Andrea Gottardo, Marlene Assiè +76
The Ar(He,d)K reaction was performed in inverse kinematics using a radioactive Ar beam produced by the SPIRAL1 facility at GANIL and a cryogenic He t…
On unveiling Buried Nuclei with JWST: a technique for hunting the most obscured galaxy nuclei from local to high redshift
I. García-Bernete, F. R. Donnan, D. Rigopoulou +12
We analyze JWST NIRSpec+MIRI/MRS observations of the infrared (IR) Polycyclic Aromatic Hydrocarbon (PAH) features in the central regions (0.26'' at 6 micron; 50-440 pc…
Machine Learning-Assisted Measurement of Lepton-Jet Azimuthal Angular Asymmetries in Deep-Inelastic Scattering at HERA
The H1 collaboration, V. Andreev, M. Arratia +141
In deep-inelastic positron-proton scattering, the lepton-jet azimuthal angular asymmetry is measured using data collected with the H1 detector at HERA. When the average transverse…
Learning Atoms from Crystal Structure
Andrij Vasylenko, Dmytro Antypov, Sven Schewe +4
Computational modelling of materials using machine learning, ML, and historical data has become integral to materials research. The efficiency of computational modelling is strongl…