TITLE: Constraining proton PDFs, strong coupling constant and top quark mass in global QCD fits: recent advances and implications

SPEAKER: Oleksandr Zenaiev (University of Hamburg)

ABSTRACT: The extraction of proton parton distribution functions (PDFs) is essential for precision predictions at hadron colliders like the LHC. Modern global PDF fits are performed at next-to-next-to-leading order in QCD and not only determine quark and gluon distributions but also simultaneously constrain key Standard Model parameters, including the strong coupling constant (alpha_S) and the top quark mass (m_t). I discuss the impact of recent LHC measurements of differential top quark pair production cross sections on the PDFs using the ABMP16 methodology. These measurements provide enhanced sensitivity to gluon PDFs at high momentum fractions and reduce uncertainties in alpha_S and m_t. The compatibility of different data sets and the compatibility of the fitted PDFs with the other global PDFs, is discussed. The resulting constraints have broad implications, in particular on the predictions for Higgs boson production at the LHC. In addition, I discuss recent developments in the open-source xFitter framework, which enhances PDF extraction methodologies.

DATE: WEDNESDAY, September 10th, 2025

TIME: 14:00

LOCATION: Theoretical Physics Seminar Room

Title: Quantum machine learning beyond parametrized quantum circuits: Quantum Gaussian Processes

Abstract: In this talk we will review the trajectory of quantum machine learning (QML)—from early proposals in the 1990s, through the dequantization wave around 2018, to recent critiques of variational QML approaches, including scaling barriers such as barren plateaus, proliferation of local minima, and classical simulability. Such analysis will reveal a fundamental truth behind QML: Thus far, we have tried building quantum learning models by slapping together the better and most advanced elements of quantum computing and classical machine learning. But this approach has failed us time and time again. As such, we propose a new path for QML, where we advocate going back to the basics of learning theory and instead using one of the simplest, most interpretable model: Gaussian Process (GPs). We will first show how certain quantum stochastic processes form genuine GPs, and we will then use the power of Bayesian statistics to efficiently solve learning and optimization tasks.

Reference: García-Martín, Diego, Martín Larocca, and M. Cerezo. Quantum neural networks form Gaussian processes. Nature Physics (2025): 1-7. https://www.nature.com/articles/s41567-025-02883-z

TITLE: Multifaceted Nu Insights: Harnessing MCMC to Inspect Neutrino Oscillations from Every Angle

SPEAKER: Jeremy Wolcott (Tufts University)

ABSTRACT: Neutrinos are among the most unusual of the fundamental particles known in modern physics. Besides interacting
with ordinary matter so rarely that supermassive detectors or extremely intense sources are required to even observe them, and being
separated from the other fundamental fermions by at least six orders of magnitude in mass, neutrinos’ “flavor oscillations” exhibit a rich
phenomenology that may at last give us hints as to where we should look beyond current theory for new fundamental insights. The discovery
of an underlying symmetry in the way the neutrino states interact with one another or the way the neutrinos’ masses are arranged, for
instance, or the violation of symmetries between neutrinos and their antimatter counterparts, could have profound consequences for both
particle physics and cosmology.

However, contemporary experiments attempting to access this phenomenology must grapple with its numerous degeneracies and multiple
degrees of freedom. In this talk, I will discuss how Bayesian Markov chain Monte Carlo (MCMC) is being used to simultaneously examine
many different aspects of neutrino oscillation measurements with an efficient computing approach. I will review its applications to current
data from the NOvA experiment at Fermilab, and show how we obtain insights into both the underlying physical system and our instrumental
setup. I will conclude with some thoughts about MCMC’s promise for future neutrino oscillation measurements.

Title: Conformal versus non-conformal two-Higgs-doublet model: phase
transitions and gravitational waves

Abstract: In this work, we conduct a comprehensive investigation of the
CP-conserving two-Higgs-doublet model, an extension of the Standard
Model. After taking theoretical and experimental constraints into
account, we analyse the dynamics of cosmological phase transitions in
the early Universe in both the conformal and non-conformal version of
this model. Nearly-conformal dynamics typically lead to strongly
supercooled phase transitions. Nevertheless, we find that the strongest
first-order phase transitions are found in the non-conformal model. We
then  demonstrate that the amount of supercooling is proportional to how
softly the classically scale invariance is broken at one-loop order.
Finally, we compute the power spectrum of the stochastic
gravitational-wave background associated to these first-order phase
transitions and show that only the non-conformal two-Higgs-doublet model
can provide a strong enough gravitational-wave signal to reach the
sensitivity of future space-based gravitational-wave detectors, such as
LISA, BBO, DECIGO, TianQin and Taiji.

Affiliation: School of Physics, University of Electronic Science and
Technology of China (UESTC), in Chengdu