Deep-neural-network extraction of unpolarized transverse-momentum-dependent parton distributions in $b_T$ space from Drell-Yan data

Abstract: We present a physics-informed deep-neural-network extraction of unpolarized transverse-momentum-dependent parton distribution functions (TMDPDFs) in impact-parameter space from Drell--Yan data. The perturbative contribution is computed with a resummed $W$ term using $\mathrm{N}<sup>{3}\mathrm{LL}$ evolution, strict-NLO hard and operator-product-expansion matching, and smooth profile scales at small and large $b_T$. A compact feature-wise linear modulation network learns only a shared nonperturbative factor $F_{NP}(x,b_T)$; the collinear PDFs, hard factor, evolution kernel, matching coefficients, and Fourier--Bessel transform remain fixed. The primary result is a smooth light-flavor $b_T$-space TMD ensemble and its cross-section-level validation. The reported $k_T$ distributions are regularized finite-$b_T$ Hankel transforms, not independent momentum-space fits. As a separate robustness test, a smooth finite-$Y$ transition is applied to 24 additional Tevatron points extending to $q_T/Q\simeq0.30$. The nominal 329-point fit is unchanged, and the results remain stable when $F_{\rm NP}$ is held fixed while the transition profile is varied. An independent 122-bin Tevatron $\mathrm{N}<sup>{3}\mathrm{LL}+\mathrm{NNLO}$ $W+Y$ grid provides a direct perturbative benchmark. A separate $W+Y$ candidate using the specified non-LHCb finite-$Y$ inputs is retained as an identifiability study.