Related papers: Comment on "Reproducibility and Replication of Exp…
The kaon physics programme, long heralded as a cutting-edge frontier by the European Strategy for Particle Physics, continues to stand at the intersection of discovery and innovation in high-energy physics (HEP). With its unparalleled…
Searches for new physics in the first year of running of LEP2, at energies of 161 and 172 GeV, are summarized. After a short review of WW results and their implications on new physics, searches for the Higgs boson and SUSY particles,…
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…
Aiming to produce reinforcement learning (RL) policies that are human-interpretable and can generalize better to novel scenarios, Trivedi et al. (2021) present a method (LEAPS) that first learns a program embedding space to continuously…
The recent results on direct photons and dileptons in high energy heavy ion collisions, obtained particularly at RHIC and LHC are reviewed. The results are new not only in terms of the probes, but also in terms of the precision. We will…
We review what can (and cannot) be learned if dark matter is detected in one or more experiments, emphasizing the importance of combining LHC data with direct, astrophysical and cosmological probes of dark matter. We briefly review the…
I present a concise review of the Higgs problem which plays a central role in particle physics today. The Higgs of the minimal Standard Model is so far just a conjecture that needs to be verified or discarded at the LHC. Probably the…
Reinforcement learning from human feedback (RLHF) has emerged as a powerful technique to make large language models (LLMs) easier to use and more effective. A core piece of the RLHF process is the training and utilization of a model of…
Modern searches for physics beyond the Standard Model produce rapidly expanding literature containing heterogeneous information, including textual analyses, numerical datasets, and graphical exclusion limits. Integrating these distributed…
In several large-scale replication projects, statistically non-significant results in both the original and the replication study have been interpreted as a "replication success". Here we discuss the logical problems with this approach:…
Loop-suppressed penguin $b\to s$ transitions are sensitive to heavy New Physics particles propagating inside the loops. Thanks to the large sample sizes from the LHC, we are able to perform multidimensional angular analyses that are…
If you want to tell people the truth, make them laugh, otherwise they'll kill you. (source unclear) Machine learning and deep learning are the technologies of the day for developing intelligent automatic systems. However, a key hurdle for…
Since the discovery of the Higgs boson, testing the many possible extensions to the Standard Model has become a key challenge in particle physics. This paper discusses a new method for predicting the compatibility of new physics theories…
Many research fields are currently reckoning with issues of poor levels of reproducibility. Some label it a "crisis", and research employing or building Machine Learning (ML) models is no exception. Issues including lack of transparency,…
LEP offers an excellent opportunity to measure two photon processes over a large kinematical range and thus study the complex nature of the photon. This article reviews the experimental status of ``Two Photon Physics'' at LEP. The recent…
The H1 and ZEUS collaborations have searched for signals of physics beyond the Standard Model in ep collisions at a center-of-mass energy of 301-319 GeV. During the HERA I phase each experiment accumulated an integrated luminosity of about…
Experimental data bases are typically very large and high dimensional. To learn from them requires to recognize important features (a pattern), often present at scales different to that of the recorded data. Following the experience…
Reproducibility of modeling is a problem that exists for any machine learning practitioner, whether in industry or academia. The consequences of an irreproducible model can include significant financial costs, lost time, and even loss of…
The purpose of the present paper is to clarify, as far as it is possible, the overall picture of experimental results in the field of non-conventional phenomena in nuclear matter published in scientific literature, accumulated in the last…
In reinforcement learning, Reverse Experience Replay (RER) is a recently proposed algorithm that attains better sample complexity than the classic experience replay method. RER requires the learning algorithm to update the parameters…