Conveners
Statistical Methods for Physics Analysis in the XXI Century: H1b (Parallel H)
- Enrico Rinaldi (Quantinuum K. K.)
Statistical Methods for Physics Analysis in the XXI Century: H3b (Parallel H)
- Yung-Kyun Noh (Hanyang University)
Statistical Methods for Physics Analysis in the XXI Century: H4a (Parallel H)
- Tommaso Dorigo (Universita e INFN, Padova (IT))
Numerical methods for exploring high-dimensional parameter spaces are crucial across a wide variety of scientific fields, including:
- global fitting particle physics models (Frequentist likelihoods)
- constraining cosmological parameters (Bayesian posteriors)
- folding proteins (free energy landscapes)
- phase diagrams in chemistry and lattice field theory (partition
...
In a high energy physics experiment, a straightforward approach to estimating the dependency of a distribution of events on a nuisance parameter is to take the difference of histograms of the distribution coming out of a simulation before and after perturbing the value of the nuisance parameter. This is often done by perturbing one already simulated event at a time by a small post-simulation...
Quantifying tension between different experiments aiming to constrain the same physics is essential for validating our understanding of the world around us. A commonly used metric of tension is the evidence ratio statistic, R, corresponding to the ratio of a joint evidence to the product of individual evidences under a common model. R can be interpreted as the fractional increase in our...
We describe a software pipeline that models atmospheric gamma and hadron showers and their detection and reconstruction by an array of Cherenkov detectors on the ground, as well as the calculation of a utility function aligned with the scientific goals of the SWGO experiment. The variation of the utility with the position of each detector on the ground allows to perform stochastic gradient...
Particle physics experiments often rely on statistical hypothesis testing to determine statements of discovery, evidence, or exclusion, typically assessed through p-values, "number of sigmas." However, in many cases, the significance is evaluated using asymptotic formulae based on Wilk's Theorem, without guarantees that its conditions are fulfilled. Alternatively, p-values can be assessed...
It has been a long-standing problem to study parton showers with important quantum interference effects. In this work, we discuss quantum/classical veto algorithm with kinematical effects incorporated. Our veto algorithm could be of wider use in many other problems of Monte Carlo simulations with quantum interferece effects. This talk is based on Phys. Rev. A 109, 032432 (2024)...
We present a novel deep learning approach to rebuild the dense matter equation of state (EoS) for probing neutron star observables. By leveraging an automatic differentiation framework, our method solves inverse problems and achieves accurate EoS optimization. Through training a neural network on a comprehensive dataset, we develop a predictive EoS model that yields precise relationships...
The BESIII experiment locates at the BEPCII $e^+e^-$ collider in Beijing, China, running in a center-of-mass energy range from 1.84 GeV to 4.95 GeV. After 15 years of successful running of the experiment since 2009, BESIII has accumulated more than 50 fb$^{-1}$ of electron-positron annihilation data, which include 10 billions J/ψ events, 2.7 billions ψ(2S) events, 20 fb$^{-1}$ $D\bar{D}$...
Current noisy quantum computers can be already used to investigate properties
of quantum systems. Here we focus on lattice QED in (2+1)D including fermionic matter.
This complex quantum field theory with dynamical gauge and matter fields has similarities
with QCD, in particular asymptotic freedom and confinement.
We define a suitable setup to measure the static potential between two...
GAMBIT - the Global and Modular beyond-Standard Model Inference Tool - is an open-source package for performing global fits of beyond-Standard Model physics theories. I will present the design of the package and some highlights of recent results.
We present a method for computing energy spectra in quantum field theory by digital quantum simulation. We utilize a quantum algorithm called coherent imaging spectroscopy which quenches the ground state with an oscillating perturbation in time and then reads off the excited energies from the vacuum persistence probability following the quench. We demonstrate this method in the lattice...
We introduce Nuclear Co-Learned Representations (NuCLR), a deep learning model that predicts various nuclear observables, including binding and decay energies, and nuclear charge radii. The model is trained using a multi-task approach with shared representations and obtains state-of-the-art performance, achieving levels of precision that are crucial for understanding fundamental phenomena in...
In high energy physics, it is challenging to estimate precisely the trials factor for a resonance search with an unspecified mass. A relatively new approach is to model the significance derived from the likelihood ratio (fit a spectrum with and without the presence of the resonance in the statistical model) as a Gaussian Process. The knowledge of the covariance of the significance between...
The unfolding problem is to make inferences about the true particle spectrum based on smeared observations from a detector. This is an ill-posed inverse problem, where small changes in the smeared distribution can lead to large fluctuations in the unfolded distribution. The forward operator is the response matrix which models the detector response. In practice, the forward operator is rarely...
In recent years, machine learning and AI technologies have revolutionized physics, becoming essential in overcoming the enormous computational costs and time constraints faced by traditional methods. This talk will discuss their applications in lattice QCD and related fields. We will introduce new configuration generation methods for lattice QCD using gauge-covariant neural networks and...
