Related papers: Publication patterns in HEP computing
As a classical generative modeling approach, energy-based models have the natural advantage of flexibility in the form of the energy function. Recently, energy-based models have achieved great success in modeling high-dimensional data in…
I give my perspective on promising directions for high energy physics in coming years.
Machine learning is becoming a new paradigm for scientific research in various research fields due to its exciting and powerful capability of modeling tools used for big-data processing task. In this mini-review, we first briefly introduce…
The physics case for the operation of high-luminosity proton-nucleus ($pA$) collisions during Run 3 and 4 at the LHC is reviewed. The collection of $\mathcal{O}$(1-10 pb$^{-1}$) of proton-lead ($p$Pb) collisions at the LHC will provide…
The HL-LHC and the corresponding detector upgrades for the CMS experiment will present extreme challenges for the full simulation. In particular, increased precision in models of physics processes may be required for accurate reproduction…
Reproducibility is an important feature of science; experiments are retested, and analyses are repeated. Trust in the findings increases when consistent results are achieved. Despite the importance of reproducibility, significant work is…
Quantum computers use quantum mechanical phenomena to perform conventionally intractable calculations for specific problems. Despite being universal machines, quantum computers are not expected to replace classical computers, but rather, to…
In this whitepaper, we argue that nurturing HEP Lab-Engineering cooperation through established collaboration and support mechanisms will advance the scientific mission of the labs significantly, while at the same time giving the…
Advances in machine learning methods provide tools that have broad applicability in scientific research. These techniques are being applied across the diversity of nuclear physics research topics, leading to advances that will facilitate…
We apply a complex network-based method of visibility graph to explore the degree of complexity and fractal nature in the multiparticle production process at the LHC energy regime. For the investigation, we have used proton-proton (pp)…
Artificial Intelligence (AI) is rapidly transforming scientific research and has become central to many data-intensive disciplines. High Energy Physics (HEP), with its vast data volumes, complex theoretical structures, and precision-driven…
Researchers may describe different aspects of past scientific publications in their publications and the descriptions may keep changing in the evolution of science. The diverse and changing descriptions (i.e., citation context) on a…
Many domains of high energy physics analysis are starting to explore machine learning techniques. Powerful methods can be used to identify and measure rare processes from previously insurmountable backgrounds. One of the most profound…
The future goals of particle physics are classified from a theorist's point of view. The prospects of mass and mixing angle determination and of the top quark and Higgs boson discovery are discussed. It is shown that the most important…
In this paper we study research trends in condensed matter physics. Trends are analyzed by means of the the number of publications in the different sub-fields as function of the years. We found that many research topics have a similar…
This is a critical discussion of physical relevance of some space-time characteristics which are in current use in high energy physics.
We discuss the interpretation of the LHC Higgs data and the test of the Higgs mechanism. This is done in a more model-independent approach relying on an effective Lagrangian, as well as in specific models like composite Higgs models and…
The European School of High-Energy Physics is intended to give young physicists an introduction to the theoretical aspects of recent advances in elementary particle physics. These proceedings contain lecture notes on quantum field theory…
In this chapter of the High Energy Physics Software Foundation Community Whitepaper, we discuss the current state of infrastructure, best practices, and ongoing developments in the area of data and software preservation in high energy…
To account for strong aging characteristics of citation networks, we modify Google's PageRank algorithm by initially distributing random surfers exponentially with age, in favor of more recent publications. The output of this algorithm,…