- Published on 14 November 2023
Over the past few years, machine learning has been revolutionizing particle physics. Modern machine learning techniques, including deep learning, are rapidly being applied, adapted, and developed for high energy physics. This EPJ ST special issue will feature some articles which will review some aspects of the natural synergy between machine learning and theoretical and experimental particle physics. The editors of this special issue attempt to bring in contributions from enthusiastic and highly active researchers. Several expert theorists and experimentalists have already agreed to contribute to this collection. Should you wish to contribute, please write to the Guest Editors Biplob Bhattacherjee and Swagata Mukherjee to see how your contribution may fit into the collection.
- Published on 10 September 2023
Guest Editors: Poulose Poulose and Ritesh Kumar Singh
An electron-positron (or a muon) collider is crucial for particle physics with its clean collision environment, reduced background interactions, precise energy, and polarization control. Complementing hadronic colliders, electron-positron colliders offer a controlled setting for high-accuracy investigations of fundamental physics phenomena. In fact, beyond the LHC, such a collider is vital for studying the Higgs boson and Electroweak Symmetry Breaking mechanism, conducting precision tests of the Standard Model, as well as exploring new dynamics. Leptonic colliders, especially electron-positron colliders, have been under consideration for decades, and a large amount of literature is available in the accelerator and detector sector, as well as physics possibilities. However, with the LHC very successfully delivering detailed information on many aspects of the fundamental interactions, it is desirable to reassess the physics capabilities of a high energy lepton collider. While such attempts are available in the literature, this Volume of EPJST would endeavor to make a comprehensive compilation of the advantages of lepton colliders in the light of the LHC results.
EPJ ST Special Issue: Discrete neural networks: Firing patterns and synchronization strategies-basics for new AI technologies
- Published on 01 September 2023
The progress made in artificial intelligence has greatly influenced the everyday lives of humans. One of the key drivers behind AI development is the study of neural networks. Neural networks are capable of interpreting numerical patterns in the form of vectors and their primary function is to classify and categorize data based on similarities. Modern technologies such as self-driving cars, facial recognition, and language translation heavily rely on neural networks. These networks enable computers to make intelligent decisions with minimal human intervention because they can learn and model complex and nonlinear relationships between input and output data.
Analyzing neural networks and their dynamics involves studying equilibrium states, periodic solutions, stability, chaos, and optimization. Gaining insight into firing patterns is a crucial factor in understanding the dynamic features of neural networks. Firing patterns reflect the salient features of the stimuli that generate them. Therefore, investigating firing patterns is essential for uncovering hidden dynamics within network models. Additionally, the influence of time delays in neural networks, particularly in the context of mimicking the functioning of the brain, has attracted attention. Mathematical models illustrate numerous biological phenomena related to neuronal activities, including multi-stability, synchronization, spiking, and bursting patterns. Therefore, studying the dynamics of neural networks plays a significant role in understanding biological phenomena and designing practical procedures for information processing and signal propagation.
EPJ ST Special Issue: Nuclear Astrophysics: Recent Progress in Understanding Element Formation in the Universe
- Published on 01 September 2023
Guest Editors: Rajdeep Chatterjee and Gautam Gangopadhyay
Understanding elemental abundances from the big bang to hydrostatic nucleosynthesis in stars, and finally, explosive nucleosynthesis in neutron star - neutron star collisions, and core-collapse supernovae, has been a long-standing problem. Recent ideas, like using non-extensive statistics as a probable solution for the cosmological lithium problem, have also established connections with non-equilibrium statistical physics. Another exciting development is the so-called i-process (in carbon-enhanced metal-poor stars), as something intermediate between the s- and the r-process while producing neutron-rich nuclei. In turn, this opens an entire field on how the structure of these exotics, as found from experiments in various rare-isotope beam laboratories around the world, influences estimates of abundances in all nucleosynthesis processes. Hence, this EPJ ST special issue attempts to bring together several theoretical, experimental, and observational aspects of the field, thereby highlighting the interdisciplinary aspects of element formation in the universe.
- Published on 05 July 2023
Guest Editor: Tripta Bhatia
Cells are confined and compartmentalized by membranes that are typically composed of hundreds of different types of lipids. Although the concept of synthetic membrane processes was introduced several years ago, recent studies have revealed new developments and exciting results with studies of biomimetic models that are used as reference systems for the cell. Structural complexity has been shown to build up in model membrane systems based on fundamental physical principles that could potentially validate the relationship between membrane phase, pattern formation, protein activity, and phase transition. Understanding the coupling between physical properties and functions opens rational methods for manipulating cellular function and dysfunction. Studies in synthetic and cell biology have generated insight into the nature of cell assembly from molecular building blocks. Hence, this issue of EPJ ST in the area of biomimetic and cellular membranes is devoted to the current state of the art in the field and an outlook on the soft matter of life to point out the directions for further studies.
- Published on 07 June 2023
Guest Editors: Sajad Jafari, Fatemeh Parastesh, Eckehard Schӧll
Real networks are composed of many interconnected dynamical systems. Network science has provided efficient tools and platforms for studying these networks. However, recent developments have shown that classical networks are not beneficial enough for investigating complex dynamics and behaviors in special cases. It has been revealed that the links are not limited to connecting two nodes but more nodes. Therefore, the interactions can go beyond pairwise connections, constructing group interactions or higher-order interactions whose introduction to the network helps to reach further developments. The higher-order interactions have been found in diverse fields, including neurology, social science, and ecology, and significantly influence their emergent behaviors. Consequently, the study of higher-order interactions has recently been the focus of many scientists.
- Published on 26 May 2023
Guest Editors: Rusa Mandal (lead editor), B. Ananthanarayan, Daniel Wyler
The proposed collection of articles is to bring together in-depth discussions of the current status of b-quark physics as well as of the prospects for the future.
The mass of the b-quark and its small decay rate have made it eminently suitable for studying the strong and the weak interactions. On one hand, the mass is high enough to allow reliable perturbative calculations of the strong interaction, due to the onset of asymptotic freedom, to have led to successful effective theories (HQET, SCET) and also to allow decays with a large number of final states. On the other hand, it is low enough to have led to the further development of various methods to handle the strong interactions at low energies, such as aspects of chiral perturbation theory or lattice methods. The small rate, due to the CKM suppression factors, makes it ideal for studies of the weak interactions, in particular the rare decays and CP violating effects. Most importantly, this has led to a solid basis for understanding CP violation, and the entire CKM mixing matrix. Before the advent of the higher energies available at LHC, B-physics indeed dominated phenomenological activities in particle physics. Today, there is intensive work at the LHCb-detector, at CERN, and at the Belle-II-detector, Tsukuba Japan.
EPJ ST Special Issue: Frontier 23: Elementary particle physics, dark matter and astroparticle physics
- Published on 26 May 2023
Including a review by the convener of the India-CMS collaboration as well as experimental and theoretical results in b-flavour physics and several dark matter and neutrino physics articles, this topical collection aims to present a broad and timely set of perspectives to the elementary particle and astroparticle physics communities - a selection of reviews and research articles at the centre of the storm.
The editors of this special issue attempt to bring in contributions from young and highly active researchers. Should you wish to contribute, please write to the lead editor (Sudhir K. Vempati) to see how your contribution may fit into the collection.
Manuscripts should be prepared following the Submission Guidelines.
Open Access: EPJ ST is a hybrid journal offering Open Access publication via the Open Choice programme and a growing number of Transformative Agreements which enable authors to publish OA at no direct cost (all costs are paid centrally).
- Published on 03 October 2022
Editors: Dechang Li and Baohua Ji
Molecular and cellular mechanics is a fundamental field for understanding the physiology and pathology of biological systems at nano- and microscale. It is widely recognized that cell behaviors highly depend on its microenvironment, including extracellular matrix and neighboring cells. One can regulate cell behaviors such as migration, differentiation, proliferation and immunity, etc., through modulating the cell-cell and cell-matrix interactions. A quantitative study of the biophysical mechanisms underlying the cellular behaviors will contribute to the understanding of the pathogenesis and development of diseases, such as tumors, cardiovascular disease, neurodegenerative disease, etc.
- Published on 22 August 2022
Editors: Roberta Citro and Silvia Scarpetta
This special topic will cover different aspects of machine learning for the description of quantum many-body and physics systems, from both solid state, statistical mechanics and computer science. Recently many successfull applications of machine learning has given novel insights in several domains in physics.