Related papers: A Review on Machine Learning for Neutrino Experime…
The treatment of nuclear effects in neutrino-nucleus interactions is one of the main sources of systematic uncertainty for the analysis and interpretation of data of neutrino oscillation experiments. Neutrinos interact with nuclei via…
Neutrinos produced by nuclear reactors have played a major role in advancing our knowledge of the properties of neutrinos. The first direct detection of the neutrino, confirming its existence, was performed using reactor neutrinos. More…
Neutrino scattering at low energies is essential for a variety of timely applications potentially having fundamental implications, e.g. unraveling unknown neutrino properties, such as the third neutrino mixing angle, the detection of the…
Machine learning plays a role in many aspects of modern IR systems, and deep learning is applied in all of them. The fast pace of modern-day research has given rise to many different approaches for many different IR problems. The amount of…
After a brief overview of the present knowledge of neutrino masses and mixing, we summarize what can be learned about physics beyond the standard model from the various proposed neutrino experiments. We also comment on the impact of the…
Recent developments in our understanding of neutrino masses and their implications for physics beyond the standard model are reviewed.
Machine learning has played an important role in the analysis of high-energy physics data for decades. The emergence of deep learning in 2012 allowed for machine learning tools which could adeptly handle higher-dimensional and more complex…
The weak interactions of neutrinos with other Standard Model particles are well described within the Standard Model of particle physics. However, modern accelerator-based neutrino experiments employ nuclei as targets, where neutrinos…
Scientific progress is tightly coupled to the emergence of new research tools. Today, machine learning (ML)-especially deep learning (DL)-has become a transformative instrument for quantum science and technology. Owing to the intrinsic…
Recent discoveries in the field of neutrino oscillations have provided a unique window into physics beyond the standard model. In this lecture, I summarize how well we understand the various observations, what they tell us about the nature…
First-principle simulations are at the heart of the high-energy physics research program. They link the vast data output of multi-purpose detectors with fundamental theory predictions and interpretation. This review illustrates a wide range…
Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Since quantum systems produce counter-intuitive patterns believed not to be efficiently…
With neutrino astronomy just beginning to burgeon, and the prospects of detecting the cosmic neutrino background closer than ever, we live in an era with the unique opportunity not only to investigate the universe with this novel probe, but…
Various issues in neutrino phenomenology are reviewed, including: the possibility of large mixing angles in various models for neutrino masses, difficulties for degenerate neutrinos as candidates for hot dark matter, strategies for…
The role of simple quantum mechanics in understanding neutrino oscillation experiments is pointed out by comparison with two-slit and Bragg scattering experiments. The importance of considering the beam and the detector as a correlated…
Sequential or chained models are increasingly prevalent in machine learning for scientific applications, due to their flexibility and ease of development. Chained models are particularly useful when a task is separable into distinct steps…
Nearly 70 years since the neutrino was discovered, and 25 years since discovery of neutrino oscillations established its non-zero mass, the absolute neutrino-mass scale remains unknown. Due to its unique characteristics, determining this…
In recent years, machine learning has demonstrated impressive results in various fields, including software vulnerability detection. Nonetheless, using machine learning to identify software vulnerabilities presents new challenges,…
Machine Learning is a powerful tool for astrophysicists, which has already had significant uptake in the community. But there remain some barriers to entry, relating to proper understanding, the difficulty of interpretability, and the lack…
In this contribution we discuss the future of the global long-baseline neutrino oscillation program. The case is made that our current lack of understanding of neutrino-nucleus interactions is a serious challenge which will need to be met…