BFKL NLL phenomenology of forward jets at HERA and Mueller Navelet jets at the Tevatron and the LHC

C. Royon

Home/Archive/arXiv

arXiv · 0706.1799

BFKL NLL phenomenology of forward jets at HERA and Mueller Navelet jets at the Tevatron and the LHC

C. Royon

View original source ↗Read source document ↗Export metadata ↓

Abstract

We perform a BFKL-NLL analysis of forward jet production at HERA which leads to a good description of data over the full kinematical domain. We also predict the azimuthal angle dependence of Mueller-Navelet jet production at the Tevatron and the LHC using the BFKL NLL formalism.

Explore related subjects

hep-ph

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

C. Royon. 2007-06-12. BFKL NLL phenomenology of forward jets at HERA and Mueller Navelet jets at the Tevatron and the LHC. https://arxiv.org/abs/0706.1799

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Bayesian Inference of the Critical Endpoint in 2+1-Flavor System from Holographic QCD

We present a Bayesian holographic model constructed by integrating the equation of state and baryon number susceptibility at zero chemical potential from lattice QCD. The model incorporates error estimates derived from lattice data. With this model, we systematically investigate the thermodynamic properties of the 2+1-flavor QCD system. Using Bayesian Inference, we perform precise calibration of the model parameters and determined the critical endpoint (CEP) position under the maximum a posterior (MAP) estimation to be $(T^{c},μ_{B}^{c})=(0.0859\;\mathrm{GeV},0.742\;\mathrm{GeV})$. Additionally, we predict the CEP positions within 68\% and 95\% confidence levels, yielding $(T^{c},\; μ_{B}^{c})_{68\%}$=$(0.0820\text{--}0.0889, 0.71\text{--}0.77)\;\mathrm{GeV}$ and $(T^{c},\; μ_{B}^{c})_{95\%}$=$(0.0816\text{--}0.0898,\; 0.71\text{--}0.79)\;\mathrm{GeV}$, respectively. Moreover, to validate the reliability and predictive power of our approach, we conduct a comprehensive comparison between our predictions and potential CEP locations proposed by other theoretical models. This work not only establishes a novel Bayesian framework for holographic modeling but also provides valuable insights and theoretical support for exploring phase transitions in strongly-interacting matter under extreme conditions.

hep-ph↗

arXiv2026

Entanglement in the $θ$-vacuum

We compute the entanglement entropy and the entanglement spectrum of the vacuum state in the massive Schwinger model at a finite $θ$ angle. By computing two-point correlation functions we show that the correlation length diverges in the critical region. We clarify the physical origin of entanglement entropy enhancement at $θ=π$ by relating it to the competition between distinct electric-flux vacuum branches. We show that the peak near $θ=π$ persists across the range of masses studied and corresponds to the point of maximal competition between distinct vacuum branches with opposite electric-field orientation, where quantum fluctuations due to fermion pair creation are maximized. While this entropy enhancement is generic, a pronounced compression of the low-lying entanglement spectrum at $θ=π$ distinguishes the near-critical case ($m/g\simeq0.33$). Using the Bisognano--Wichmann (BW) theorem, we construct a lattice BW entanglement Hamiltonian and compare it with the exact modular Hamiltonian obtained from the reduced density matrix. We observe agreement in the infrared sector, indicating that the entanglement Hamiltonian is well approximated by a spatially weighted Hamiltonian. Finally, we discuss possible applications to entanglement in topological insulators and quantum wires. In this work, we used the chirally rotated formulation of the Schwinger model Hamiltonian. We identify an additive vacuum-energy artifact induced by the discretization of the chirally rotated lattice and derive its exact free-lattice form to subtract it in the continuum limit.

hep-ph↗

arXiv2026

Proton Structure from Neural Simulation-Based Inference at the LHC

The precise determination of the parton distribution functions (PDFs) of the proton is an essential ingredient for LHC analyses, including for those at the upcoming High-Luminosity LHC. So far, PDFs are determined from global fits to binned low-dimensional data obtained from unfolded hard-scattering cross section measurements. In this work we demonstrate for the first time the feasibility of neural simulation-based inference (NSBI) for constraining the proton PDFs using a high-dimensional unbinned data set. Exploiting the full statistical power of unbinned data removes the loss of information inherited by the binning procedure. As a proof-of-concept, we determine the gluon PDF from simulated data of top quark pair production at the LHC with $\sqrt{s}=13$ TeV. Taking into account both experimental and theoretical systematic uncertainties in the detector-level features, we demonstrate how the NSBI pipeline achieves significant improvements in precision compared to low-dimensional binned analyses. Our results illustrate the potential of unbinned inference to reduce the reliance on coarse approximations of uncertainties and their correlations entering PDF determinations, hence contributing to a new paradigm of unbinned detector-level ML-assisted measurements at the LHC.

hep-ph↗