Related papers: Fair Universe Higgs Uncertainty Challenge
A brief review is given of recent developments related to the Higgs signal and its implications for supersymmetry in the supergravity grand unification framework. The Higgs data indicates that the allowed parameter space largely lies on…
The growing role of data science (DS) and machine learning (ML) in high-energy physics (HEP) is well established and pertinent given the complex detectors, large data, sets and sophisticated analyses at the heart of HEP research. Moreover,…
The FCC-ee offers powerful opportunities to determine the Higgs boson parameters, exploiting over $10^6$ ${\rm e^+e^- \to ZH}$ events and almost $10^5$ ${\rm WW \to H}$ events at centre-of-mass energies around 240 and 365 GeV. This essay…
Quantum computing offers a new paradigm for advancing high-energy physics research by enabling novel methods for representing and reasoning about fundamental quantum mechanical phenomena. Realizing these ideals will require the development…
We present the state-of-the-art of the effective field theory computation of the MSSM Higgs mass, improving the existing ones by including extra threshold corrections. We show that, with this approach, the theoretical uncertainty is within…
The standard cosmological model, known as the LambdaCDM model, has been successful in many respects, but it has some significant discrepancies, some of which have not been resolved yet. In measuring the Hubble-Lematre parameter, there is an…
In this talk I will begin by summarising the importance of the Higgs physics studies at the LHC. I will then give a short description of the pre-LHC constraints on the Higgs mass and the theoretical predictions for the LHC along with a…
This paper follows hep-ph/9611397, where updated calculations of cross sections and branching ratios relevant for Standard Model Higgs phenomenology at the LHC were presented. Here, we complete that study by carrying out an updated…
Even though the unified theory of electroweak interactions is very successful at low energies, there remains one part to be confirmed. It is the sector involving Higgs particles. Those Higgs particles are expected to be discovered. It has…
Next generation neutrino experiments will push the limits in our understanding of astroparticle physics in the neutrino sector to energies orders of magnitude higher than the current state-of-the-art high-energy neutrino experiment,…
Uncertainty is an important and fundamental concept in physics education. Students are often first exposed to uncertainty in introductory labs, expand their knowledge across lab courses, and then are introduced to quantum mechanical…
Uncertainty quantification (UQ) in scientific machine learning is increasingly critical as neural networks are widely adopted to tackle complex problems across diverse scientific disciplines. For physics-informed neural networks (PINNs), a…
A working group on detector simulation was formed as part of the high-energy physics (HEP) Software Foundation's initiative to prepare a Community White Paper that describes the main software challenges and opportunities to be faced in the…
Quantitative definitions of what is unfair and what is fair have been introduced in multiple disciplines for well over 50 years, including in education, hiring, and machine learning. We trace how the notion of fairness has been defined…
Uncertainty quantification is at the core of the reliability and robustness of machine learning. In this paper, we provide a theoretical framework to dissect the uncertainty, especially the \textit{epistemic} component, in deep learning…
Fair predictive algorithms hinge on both equality and trust, yet inherent uncertainty in real-world data challenges our ability to make consistent, fair, and calibrated decisions. While fairly managing predictive error has been extensively…
Academic challenges comprise effective means for (i) advancing the state of the art, (ii) putting in the spotlight of a scientific community specific topics and problems, as well as (iii) closing the gap for under represented communities in…
Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering. Many metric learning methods represent the input as a single point in the…
The supersymmetric grand unified theory where the $SU(5)$ gauge symmetry is broken by the Hosotani mechanism provides a natural solution to the so-called doublet-triplet splitting problem. At the same time, this model derives a general and…
I review selected topics in supersymmetry, including: effects of non-universality, high $\tanb$ and phases on SUSY signals; a heavy gluino as the LSP; gauge-mediated SUSY signals involving delayed decays; R-parity violation and the very…