Engineering Papers⌕ Search

Engineering topics

Faroughy, D. A.

Publications and source records attributed to Faroughy, D. A..

Probing lepton flavor violation in meson decays with LHC data

Abstract In this letter, we use LHC data from the Drell–Yan processes $$pp\rightarrow \ell _i\ell _j$$ p p → ℓ i ℓ j (with $$i\ne j$$ i ≠ j ) to derive model-independent upper limits on lepton-flavor-violating meson decays. Our analysis is based on an Effective Field Theory (EFT) approach and it does not require a specific assumption regarding the basis of effective operators. We find that current LHC data (140 $$\textrm{fb}^{-1}$$ fb - 1 ) already provides competitive limits on $${\mathcal {B}}(B\rightarrow \pi e \tau )$$ B ( B → π e τ ) and $${\mathcal {B}}(B\rightarrow \pi \mu \tau )$$ B ( B → π μ τ ) with respect to the ones obtained through experimental searches at the B -factories. Moreover, we derive upper limits on several decays that have not been searched for experimentally yet, such as $$D^0\rightarrow e\tau $$ D 0 → e τ in the charm sector, and various semileptonic decays such as $$B\rightarrow \rho \mu \tau $$ B → ρ μ τ , $$B_s\rightarrow K \mu \tau $$ B s → K μ τ and $$B_s\rightarrow \phi \mu \tau $$ B s → ϕ μ τ . Lastly, we discuss the validity of the EFT description of LHC data and the impact of loop corrections in our analysis.

Descotes-Genon, S.↗

Snowmass 2021 Computational Frontier CompF03 Topical Group Report: Machine Learning

The rapidly-developing intersection of machine learning (ML) with high-energy physics (HEP) presents both opportunities and challenges to our community. Far beyond applications of standard ML tools to HEP problems, genuinely new and potentially revolutionary approaches are being developed by a generation of talent literate in both fields. There is an urgent need to support the needs of the interdisciplinary community driving these developments, including funding dedicated research at the intersection of the two fields, investing in high-performance computing at universities and tailoring allocation policies to support this work, developing of community tools and standards, and providing education and career paths for young researchers attracted by the intellectual vitality of machine learning for high energy physics.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗