SciPost Physics CodebasesPub Date : 2023-09-14DOI: 10.21468/SciPostPhysCodeb.20 10.21468/SciPostPhysCodeb.20-r1.3
Atis Yosprakob
{"title":"GrassmannTN: A Python package for Grassmann tensor network computations","authors":"Atis Yosprakob","doi":"10.21468/SciPostPhysCodeb.20 10.21468/SciPostPhysCodeb.20-r1.3","DOIUrl":"https://doi.org/10.21468/SciPostPhysCodeb.20 10.21468/SciPostPhysCodeb.20-r1.3","url":null,"abstract":"We present GrassmannTN, a Python package for the computation of the Grassmann tensor network. The package is built to assist in the numerical computation without the need to input the fermionic sign factor manually. It prioritizes coding readability by designing every tensor manipulating function around the tensor subscripts. The computation of the Grassmann tensor renormalization group and Grassmann isometries using GrassmannTN are given as the use case examples.","PeriodicalId":430271,"journal":{"name":"SciPost Physics Codebases","volume":"574 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2023-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139340107","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Codebase release 1.1 for LinReTraCe","authors":"Matthias Pickem, Emanuele Maggio, J. M. Tomczak","doi":"10.21468/scipostphyscodeb.16-r1.1","DOIUrl":"https://doi.org/10.21468/scipostphyscodeb.16-r1.1","url":null,"abstract":"We describe the “Linear Response Transport Centre” (LinReTraCe), a package for the simulation of transport properties of solids. LinReTraCe captures quantum (in)coherence effects beyond semi-classical Boltzmann techniques, while incurring similar numerical costs. The enabling algorithmic innovation is a semi-analytical evaluation of Kubo formulae for resistivities and the coefficients of Hall, Seebeck and Nernst. We detail the program’s architecture, its interface and usage with electronic-structure packages such as WIEN2k, VASP, and Wannier90, as well as versatile tight-binding settings.","PeriodicalId":430271,"journal":{"name":"SciPost Physics Codebases","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134335611","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Codebase release 0.4 for pyBumpHunter","authors":"L. Vaslin, S. Calvet, Vincent Barra, J. Donini","doi":"10.21468/scipostphyscodeb.15-r0.4","DOIUrl":"https://doi.org/10.21468/scipostphyscodeb.15-r0.4","url":null,"abstract":"The BumpHunter algorithm is widely used in the search for new particles in High Energy Physics analysis. This algorithm offers the advantage of evaluating the local and global p-values of a localized deviation in the observed data without making any hypothesis on the supposed signal. The increasing popularity of the Python programming language motivated the development of a new public implementation of this algorithm in Python, called pyBumpHunter, together with several improvements and additional features. It is the first public implementation of the BumpHunter algorithm to be added to Scikit-HEP. This paper presents in detail the BumpHunter algorithm as well as all the features proposed in this implementation. All these features have been tested in order to demonstrate their behaviour and performance.","PeriodicalId":430271,"journal":{"name":"SciPost Physics Codebases","volume":"608 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131588231","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Kong, Konstantin T. Matchev, S. Mrenna, Prasanth Shyamsundar
{"title":"Codebase release 0.1 for infstat","authors":"K. Kong, Konstantin T. Matchev, S. Mrenna, Prasanth Shyamsundar","doi":"10.21468/scipostphyscodeb.14-r0.1","DOIUrl":"https://doi.org/10.21468/scipostphyscodeb.14-r0.1","url":null,"abstract":"We propose an intuitive, machine-learning approach to multiparameter inference, dubbed the InferoStatic Networks (ISN) method, to model the score and likelihood ratio estimators in cases when the probability density can be sampled but not computed directly. The ISN uses a backend neural network that models a scalar function called the inferostatic potential varphiφ. In addition, we introduce new strategies, respectively called Kernel Score Estimation (KSE) and Kernel Likelihood Ratio Estimation (KLRE), to learn the score and the likelihood ratio functions from simulated data. We illustrate the new techniques with some toy examples and compare to existing approaches in the literature. We mention en passant some new loss functions that optimally incorporate latent information from simulations into the training procedure.","PeriodicalId":430271,"journal":{"name":"SciPost Physics Codebases","volume":"59 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127580021","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Codebase release 2.0 for UFOManager","authors":"M. Neubauer, Avik Roy, Zijun Wang","doi":"10.21468/scipostphyscodeb.13-r2.0","DOIUrl":"https://doi.org/10.21468/scipostphyscodeb.13-r2.0","url":null,"abstract":"Research in the data-intensive discipline of high energy physics (HEP) often relies on domain-specific digital contents. Reproducibility of research relies on proper preservation of these digital objects. This paper reflects on the interpretation of principles of Findability, Accessibility, Interoperability, and Reusability (FAIR) in such context and demonstrates its implementation by describing the development of an end-to-end support infrastructure for preserving and accessing Universal FeynRules Output (UFO) models guided by the FAIR principles. UFO models are custom-made python libraries used by the HEP community for Monte Carlo simulation of collider physics events. Our framework provides simple but robust tools to preserve and access the UFO models and corresponding metadata in accordance with the FAIR principles.","PeriodicalId":430271,"journal":{"name":"SciPost Physics Codebases","volume":"73 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127070449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Codebase release 1.0 for FermiFCI","authors":"Lukas Rammelmüller, D. Huber, A. Volosniev","doi":"10.21468/scipostphyscodeb.12-r1.0","DOIUrl":"https://doi.org/10.21468/scipostphyscodeb.12-r1.0","url":null,"abstract":"We introduce a generic and accessible implementation of an exact diagonalization method for studying few-fermion models. Our aim is to provide a testbed for the newcomers to the field as well as a stepping stone for trying out novel optimizations and approximations. This userguide consists of a description of the algorithm, and several examples in varying orders of sophistication. In particular, we exemplify our routine using an effective-interaction approach that fixes the low-energy physics. We benchmark this approach against the existing data, and show that it is able to deliver state-of-the-art numerical results at a significantly reduced computational cost.","PeriodicalId":430271,"journal":{"name":"SciPost Physics Codebases","volume":"197 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114201707","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Waszkiewicz, Maciej Bartczak, Kamil Kolasa, M. Lisicki
{"title":"Codebase release 0.2 for Pychastic","authors":"R. Waszkiewicz, Maciej Bartczak, Kamil Kolasa, M. Lisicki","doi":"10.21468/scipostphyscodeb.11-r0.2","DOIUrl":"https://doi.org/10.21468/scipostphyscodeb.11-r0.2","url":null,"abstract":"In the last decade, Python-powered physics simulations ecosystem has been growing steadily, allowing greater interoperability, and becoming an important tool in numerical exploration of physical phenomena, particularly in soft matter systems. Driven by the need for fast and precise numerical integration in colloidal dynamics, here we formulate the problem of Brownian Dynamics (BD) in a mathematically consistent formalism of the Itō calculus, and develop a Python package to assist numerical computations. We show that, thanks to the automatic differentiation packages, the classical truncated Taylor-Itō integrators can be implemented without the burden of computing the derivatives of the coefficient functions beforehand. Furthermore, we show how to circumvent the difficulties of BD simulations such as calculations of the divergence of the mobility tensor in the diffusion equation and discontinuous trajectories encountered when working with dynamics on S^2S2 and SO(3)SO(3). The resulting Python package, Pychastic, is capable of performing BD simulations including hydrodynamic interactions at speeds comparable to dedicated implementations in lower-level programming languages, but with a much simpler end-user interface.","PeriodicalId":430271,"journal":{"name":"SciPost Physics Codebases","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134501442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Codebase release 1.3 for SymTensor","authors":"Yang Gao, P. Helms, G. Chan, Edgar Solomonik","doi":"10.21468/scipostphyscodeb.10-r1.3","DOIUrl":"https://doi.org/10.21468/scipostphyscodeb.10-r1.3","url":null,"abstract":"Tensor contractions are ubiquitous in computational chemistry and\u0000physics, where tensors generally represent states or operators and\u0000contractions express the algebra of these quantities. In this context,\u0000the states and operators often preserve physical conservation laws,\u0000which are manifested as group symmetries in the tensors. These group\u0000symmetries imply that each tensor has block sparsity and can be stored\u0000in a reduced form. For nontrivial contractions, the memory footprint and\u0000cost are lowered, respectively, by a linear and a quadratic factor in\u0000the number of symmetry sectors. State-of-the-art tensor contraction\u0000software libraries exploit this opportunity by iterating over blocks or\u0000using general block-sparse tensor representations. Both approaches\u0000entail overhead in performance and code complexity. With intuition aided\u0000by tensor diagrams, we present a technique, irreducible representation\u0000alignment, which enables efficient handling of Abelian group symmetries\u0000via only dense tensors, by using contraction-specific reduced forms.\u0000This technique yields a general algorithm for arbitrary group symmetric\u0000contractions, which we implement in Python and apply to a variety of\u0000representative contractions from quantum chemistry and tensor network\u0000methods. As a consequence of relying on only dense tensor contractions,\u0000we can easily make use of efficient batched matrix multiplication via\u0000Intel’s MKL and distributed tensor contraction via the Cyclops library,\u0000achieving good efficiency and parallel scalability on up to 4096 Knights\u0000Landing cores of a supercomputer.","PeriodicalId":430271,"journal":{"name":"SciPost Physics Codebases","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123631748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Codebase release 1.0 Worm","authors":"Nicolas Sadoune, L. Pollet","doi":"10.21468/scipostphyscodeb.9-r1.0","DOIUrl":"https://doi.org/10.21468/scipostphyscodeb.9-r1.0","url":null,"abstract":"We present a novel and open-source implementation of the worm\u0000algorithm, which is an algorithm to simulate Bose-Hubbard and\u0000sign-positive spin models using a path-integral representation of the\u0000partition function. The code can deal with arbitrary lattice structures\u0000and assumes spin-exchange terms, or bosonic hopping amplitudes, between\u0000nearest-neighbor sites, and local or nearest-neighbor interactions of\u0000the density-density type. We explicitly demonstrate the near-linear\u0000scaling of the algorithm with respect to the system volume and the\u0000inverse temperature and analyze the autocorrelation times in the\u0000vicinity of a U(1)U(1)\u0000second order phase transition. The code is written in such a way that\u0000extensions to other lattice models as well as closely-related\u0000sign-positive models can be done straightforwardly on top of the\u0000provided framework.","PeriodicalId":430271,"journal":{"name":"SciPost Physics Codebases","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122812990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
C. Bierlich, Smita Chakraborty, N. Desai, L. Gellersen, I. Helenius, P. Ilten, L. Lönnblad, S. Mrenna, S. Prestel, C. Preuss, T. Sjöstrand, P. Skands, M. Utheim, R. Verheyen
{"title":"Codebase release 8.3 for PYTHIA","authors":"C. Bierlich, Smita Chakraborty, N. Desai, L. Gellersen, I. Helenius, P. Ilten, L. Lönnblad, S. Mrenna, S. Prestel, C. Preuss, T. Sjöstrand, P. Skands, M. Utheim, R. Verheyen","doi":"10.21468/scipostphyscodeb.8-r8.3","DOIUrl":"https://doi.org/10.21468/scipostphyscodeb.8-r8.3","url":null,"abstract":"This manual describes the Pythia event generator, the most recent\u0000version of an evolving physics tool used to answer fundamental questions\u0000in particle physics. The program is most often used to generate\u0000high-energy-physics collision “events”, i.e. sets of particles produced\u0000in association with the collision of two incoming high-energy particles,\u0000but has several uses beyond that. The guiding philosophy is to produce\u0000and re-produce properties of experimentally obtained collisions as\u0000accurately as possible. The program includes a wide ranges of reactions\u0000within and beyond the Standard Model, and extending to heavy ion\u0000physics. Emphasis is put on phenomena where strong interactions play a\u0000major role. The manual contains both pedagogical and practical\u0000components. All included physics models are described in enough detail\u0000to allow the user to obtain a cursory overview of used assumptions and\u0000approximations, enabling an informed evaluation of the program output. A\u0000number of the most central algorithms are described in enough detail\u0000that the main results of the program can be reproduced independently,\u0000allowing further development of existing models or the addition of new\u0000ones. Finally, a chapter dedicated fully to the user is included towards\u0000the end, providing pedagogical examples of standard use cases, and a\u0000detailed description of a number of external interfaces. The program\u0000code, the online manual, and the latest version of this print manual can\u0000be found on the Pythia web page: https://www.pythia.org/.","PeriodicalId":430271,"journal":{"name":"SciPost Physics Codebases","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124111461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号
