Lebanon Valley College

We establish an approach to analyze the free hadron and transition (nonperturbative) regions of near-side energy-energy correlators (EECs) based on dihadron fragmentation functions (DiFFs). We introduce a (nonperturbative) function we call the "EEC DiFF" and explicitly show that expanding it for large relative transverse momentum between the two hadrons gives the O(α_{s}) expression for the "EEC jet" function used in the quark-gluon (perturbative) region. This connection indicates that a formal theoretical matching will be able to bridge the free-hadron region, transition, and quark-gluon regions and allow all of them to be analyzed simultaneously. We further derive a result valid for near-side EECs in the free hadron and transition regions of e^{+}e^{-} annihilation in terms of the EEC DiFF. Using a simple model for the function, we perform the first fit within the dihadron framework to experimental data in this regime. We find reasonable agreement with the measurements and reproduce the salient features of near-side EECs in the free hadron and transition regions.

This work introduces a novel, nonparametric pixel-based framework for the Bayesian inference and imaging of transverse momentum dependent (TMD) parton distributions. The methodology is built upon a fully differentiable framework that integrates TMD evolution with the Collins-Soper-Sterman formalism, enabling the simultaneous extraction of partonic distributions and the nonperturbative evolution kernel. To achieve efficient and exact sampling of the high-dimensional posterior, we leverage generative AI through a hybrid normalizing flow-driven Metropolis-Hastings approach. The framework is validated through multi-scale closure tests of increasing complexity, ranging from basic functional models to convoluted structure functions. Using singular value decomposition (SVD), we rigorously characterize the uncertainty of the reconstructed distributions and reveal the existence of null TMDs, which are functional components in the null space of the kernel that remain unconstrained by observables. The new framework provides the first integration of pixel-based discretization, generative AI, and SVD within a Bayesian context to solve the TMD inverse problem. This synergy between machine learning and multi-scale data removes inherent degeneracies and enables unbiased 3D partonic imaging.

From birdsong to human language, acoustic communication by vocal learners involves the concatenation of sounds into sequences. Sequences are more efficient for the producer and more accommodating to the capacities of receivers. Over development, the compression of syllables into rapid sequences (in terms of more syllables per second) may reflect both social learning and motor maturation. We tested whether sequence compression could be predicted uniquely by exogenous (i.e. social) and endogenous (i.e. motor) sources. In human infants, we found that (i) vocal sequences strongly engage adults, (ii) from 5 to 10 months, sequences compress, (iii) social feedback to 5-month-olds' sequences predicted sequence compression over development, and (iv) sequence compression over development predicted infants' vocabulary development. We next examined the extent to which compression develops in a paradigm in which we could separate social feedback from motor practice. In zebra finch (Taeniopygia guttata), we found that (i) motor development predicted sequence compression, but (ii) compression only predicted song maturity (similarity to tutor) when birds received contingent social feedback to their immature vocalizing. In addition, (iii) social feedback predicted finches' sequence compression. These findings demonstrate the potency of social feedback across two vocal learning species in the emergence of vocal efficiency. This article is part of the theme issue 'Mechanisms of learning from social interaction'.

We assess the impact of future measurements of dihadron production in semi-inclusive deep-inelastic scattering from the CLAS12 and proposed SoLID experiments at Jefferson Lab, as well as from the ePIC experiment at the future Electron-Ion Collider (EIC), on the transversity parton distribution functions (PDFs) and the corresponding tensor charges of the nucleon. To this end, we generate pseudo-data for these experiments for a proton target (CLAS12 and ePIC) and a ^3He target (SoLID and ePIC), and we include these pseudo-data in the JAMDiFF global analysis of existing experimental dihadron data. We find that future data from Jefferson Lab will significantly reduce uncertainties in the transversity PDFs in the region of intermediate-to-large quark momentum fractions x, while the EIC will provide strong constraints across the entire range of x, allowing for the first experimental test of the predicted small-x behavior of the transversity PDFs. In discussing the reduction of uncertainties in the tensor charges, we also compare the results from the data analyses with those from lattice QCD, highlighting scenarios in which compatibility or tension between the two would arise.

Images generated by artificial intelligence (AI) are becoming more realistic, with AI-generated images of adult faces indistinguishable from human faces. Can the same be said for AI-generated images of young children? We compared 76 participants’ discrimination of 72 images representing three types of stimuli: real photos, randomly selected AI images, and AI images curated with human oversight. Real photos were identified as real more frequently (78.6%) than Curated AI (40.3%) and Random AI (29.1%). However, Curated AI stimuli were judged as real more often than Random AI stimuli. Performance variability between images based on age, gender, and race suggests that AI better portrays children with specific characteristics. Images of toddlers and girls were judged as real more than infants and boys. Additional refinement is needed in training and testing text-to-image algorithms to realistically portray children and promote the ethical use of AI in developmental research.