
@article{harris_array_2020,
	title = {Array programming with {NumPy}},
	volume = {585},
	url = {https://doi.org/10.1038/s41586-020-2649-2},
	doi = {10.1038/s41586-020-2649-2},
	pages = {357--362},
	number = {7825},
	journaltitle = {Nature},
	author = {Harris, Charles R. and Millman, K. Jarrod and Walt, Stéfan J. van der and Gommers, Ralf and Virtanen, Pauli and Cournapeau, David and Wieser, Eric and Taylor, Julian and Berg, Sebastian and Smith, Nathaniel J. and Kern, Robert and Picus, Matti and Hoyer, Stephan and Kerkwijk, Marten H. van and Brett, Matthew and Haldane, Allan and Río, Jaime Fernández del and Wiebe, Mark and Peterson, Pearu and Gérard-Marchant, Pierre and Sheppard, Kevin and Reddy, Tyler and Weckesser, Warren and Abbasi, Hameer and Gohlke, Christoph and Oliphant, Travis E.},
	date = {2020-09},
	note = {Publisher: Springer Science and Business Media {LLC}},
}

@article{hunter_matplotlib_2007,
	title = {Matplotlib: A 2D graphics environment},
	volume = {9},
	doi = {10.1109/MCSE.2007.55},
	abstract = {Matplotlib is a 2D graphics package used for Python for application development, interactive scripting, and publication-quality image generation across user interfaces and operating systems.},
	pages = {90--95},
	number = {3},
	journaltitle = {Computing in Science \& Engineering},
	author = {Hunter, J. D.},
	date = {2007},
	note = {Publisher: {IEEE} {COMPUTER} {SOC}},
}

@article{pedregosa_scikit-learn_2011,
	title = {Scikit-learn: Machine Learning in Python},
	volume = {12},
	pages = {2825--2830},
	journaltitle = {Journal of Machine Learning Research},
	author = {Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V. and Thirion, B. and Grisel, O. and Blondel, M. and Prettenhofer, P. and Weiss, R. and Dubourg, V. and Vanderplas, J. and Passos, A. and Cournapeau, D. and Brucher, M. and Perrot, M. and Duchesnay, E.},
	date = {2011},
}

@inreference{noauthor_stochastic_2025,
	title = {Stochastic gradient descent},
	rights = {Creative Commons Attribution-{ShareAlike} License},
	url = {https://en.wikipedia.org/w/index.php?title=Stochastic_gradient_descent&oldid=1309164477},
	abstract = {Stochastic gradient descent (often abbreviated {SGD}) is an iterative method for optimizing an objective function with suitable smoothness properties (e.g. differentiable or subdifferentiable). It can be regarded as a stochastic approximation of gradient descent optimization, since it replaces the actual gradient (calculated from the entire data set) by an estimate thereof (calculated from a randomly selected subset of the data). Especially in high-dimensional optimization problems this reduces the very high computational burden, achieving faster iterations in exchange for a lower convergence rate.
The basic idea behind stochastic approximation can be traced back to the Robbins–Monro algorithm of the 1950s. Today, stochastic gradient descent has become an important optimization method in machine learning.},
	booktitle = {Wikipedia},
	urldate = {2025-09-22},
	date = {2025-09-02},
	langid = {english},
	note = {Page Version {ID}: 1309164477},
	file = {Snapshot:/home/lars/Zotero/storage/B6CVR59B/index.html:text/html},
}

@unpublished{elstner_lecture_2025,
	location = {Karlsruhe Institute for Technology, Karlsruhe},
	title = {Lecture: Machine Learning for Chemistry},
	type = {Lecture},
	howpublished = {Lecture},
	author = {Elstner, Marcus and Kubar, Tomas},
	date = {2025-05-20},
	langid = {german},
	file = {PDF:/home/lars/Zotero/storage/5LSLJMK8/Elstner and Kubar - 2025 - Lecture Machine Learning for Chemistry.pdf:application/pdf},
}

@online{lekhansh_lasso_2024,
	title = {Lasso vs. Ridge Regression: A Detailed Comparison},
	url = {https://medium.com/@tyagi.lekhansh/lasso-vs-ridge-regression-a-detailed-comparison-140f7832c624},
	shorttitle = {Lasso vs. Ridge Regression},
	abstract = {In the realm of regression analysis, Lasso (Least Absolute Shrinkage and Selection Operator) and Ridge Regression are two popular…},
	titleaddon = {Medium},
	author = {Lekhansh},
	urldate = {2025-09-22},
	date = {2024-09-04},
	langid = {english},
}

@book{hastie_elements_2009,
	location = {New York, {NY}},
	title = {The Elements of Statistical Learning},
	rights = {http://www.springer.com/tdm},
	isbn = {978-0-387-84857-0 978-0-387-84858-7},
	url = {http://link.springer.com/10.1007/978-0-387-84858-7},
	series = {Springer Series in Statistics},
	publisher = {Springer},
	author = {Hastie, Trevor and Tibshirani, Robert and Friedman, Jerome},
	urldate = {2025-09-22},
	date = {2009},
	doi = {10.1007/978-0-387-84858-7},
	keywords = {Averaging, Boosting, classification, clustering, data mining, machine learning, Projection pursuit, Random Forest, supervised learning, Support Vector Machine, unsupervised learning},
	file = {Full Text PDF:/home/lars/Zotero/storage/D3N4DVY9/Hastie et al. - 2009 - The Elements of Statistical Learning.pdf:application/pdf},
}

@book{goodfellow_deep_2016,
	title = {Deep Learning},
	publisher = {{MIT} Press},
	author = {Goodfellow, Ian and Bengio, Yoshua and Courville, Aaron},
	date = {2016},
}

@book{bishop_pattern_2006,
	location = {New York},
	title = {Pattern recognition and machine learning},
	isbn = {978-0-387-31073-2},
	series = {Information science and statistics},
	publisher = {Springer},
	author = {Bishop, Christopher M.},
	date = {2006},
	langid = {english},
	file = {PDF:/home/lars/Zotero/storage/9H5W9BGC/Bishop - 2006 - Pattern recognition and machine learning.pdf:application/pdf},
}

@book{vogel_particle_2024,
	location = {Cham},
	title = {Particle Confinement in Penning Traps: An Introduction},
	volume = {126},
	rights = {https://www.springernature.com/gp/researchers/text-and-data-mining},
	isbn = {978-3-031-55419-3 978-3-031-55420-9},
	url = {https://link.springer.com/10.1007/978-3-031-55420-9},
	series = {Springer Series on Atomic, Optical, and Plasma Physics},
	shorttitle = {Particle Confinement in Penning Traps},
	publisher = {Springer International Publishing},
	author = {Vogel, Manuel},
	urldate = {2025-10-09},
	date = {2024},
	langid = {english},
	doi = {10.1007/978-3-031-55420-9},
	keywords = {Confined ions and plasmas, Highly charged ions, Ion trapping, Laser Cooling, Magnetic moments, Particle confinement, Penning traps, Precision spectroscopy, Resistive Cooling, Stored ions, Trapped charged particles, Unified notation Penning traps},
	file = {Full Text PDF:/home/lars/Zotero/storage/SI8SF7RF/Vogel - 2024 - Particle Confinement in Penning Traps An Introduction.pdf:application/pdf},
}

@article{scampoli_aegis_2014,
	title = {The {AEgIS} experiment at {CERN} for the measurement of antihydrogen gravity acceleration},
	volume = {29},
	issn = {0217-7323},
	url = {https://www.worldscientific.com/doi/abs/10.1142/S0217732314300171},
	doi = {10.1142/S0217732314300171},
	abstract = {The Antihydrogen Experiment: Gravity, Interferometry, Spectroscopy ({AEgIS}) experiment is conducted by an international collaboration based at {CERN} whose aim is to perform the first direct measurement of the gravitational acceleration of antihydrogen in the local field of the Earth, with Δg/g = 1\% precision as a first achievement. The idea is to produce cold (100 {mK}) antihydrogen  through a pulsed charge exchange reaction by overlapping clouds of antiprotons, from the Antiproton Decelerator ({AD}) and positronium atoms inside a Penning trap. The antihydrogen has to be produced in an excited Rydberg state to be subsequently accelerated to form a beam. The deflection of the antihydrogen beam can then be measured by using a moiré deflectometer coupled to a position sensitive detector to register the impact point of the anti-atoms through the vertex reconstruction of their annihilation products. After being approved in late 2008, {AEgIS} started taking data in a commissioning phase in 2012. This paper presents an outline of the experiment with a brief overview of its physics motivation and of the state-of-the-art of the g measurement on antimatter. Particular attention is given to the current status of the emulsion-based position detector needed to measure the  sag in {AEgIS}.},
	pages = {1430017},
	number = {17},
	journaltitle = {Modern Physics Letters A},
	shortjournal = {Mod. Phys. Lett. A},
	author = {Scampoli, Paola and Storey, James},
	urldate = {2025-10-09},
	date = {2014-06-07},
	note = {Publisher: World Scientific Publishing Co.},
	keywords = {Antihydrogen, gravity, high resolution tracking detector},
}

@article{bertsche_prospects_2018,
	title = {Prospects for comparison of matter and antimatter gravitation with {ALPHA}-g},
	volume = {376},
	url = {https://royalsocietypublishing.org/doi/full/10.1098/rsta.2017.0265},
	doi = {10.1098/rsta.2017.0265},
	abstract = {The {ALPHA} experiment has recently entered an expansion phase of its experimental programme, driven in part by the expected benefits of conducting experiments in the framework of the new {AD} + {ELENA} antiproton facility at {CERN}. With antihydrogen trapping now a routine operation in the {ALPHA} experiment, the collaboration is leading progress towards precision atomic measurements on trapped antihydrogen atoms, with the first excitation of the 1S–2S transition and the first measurement of the antihydrogen hyperfine spectrum (Ahmadi et al. 2017 Nature 541, 506–510 (doi:10.1038/nature21040); Nature 548, 66–69 (doi:10.1038/nature23446)). We are building on these successes to extend our physics programme to include a measurement of antimatter gravitation. We plan to expand a proof-of-principle method (Amole et al. 2013 Nat. Commun. 4, 1785 (doi:10.1038/ncomms2787)), first demonstrated in the original {ALPHA} apparatus, and perform a precise measurement of antimatter gravitational acceleration with the aim of achieving a test of the weak equivalence principle at the 1\% level. The design of this apparatus has drawn from a growing body of experience on the simulation and verification of antihydrogen orbits confined within magnetic-minimum atom traps. The new experiment, {ALPHA}-g, will be an additional atom-trapping apparatus located at the {ALPHA} experiment with the intention of measuring antihydrogen gravitation.

This article is part of the Theo Murphy meeting issue ‘Antiproton physics in the {ELENA} era’.},
	pages = {20170265},
	number = {2116},
	journaltitle = {Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences},
	author = {Bertsche, W. A.},
	urldate = {2025-10-09},
	date = {2018-02-19},
	note = {Publisher: Royal Society},
	keywords = {gravity, antigravity, antihydrogen, antimatter, {CPT}, Lorentz invariance},
	file = {Full Text PDF:/home/lars/Zotero/storage/7YM83XXD/Bertsche - 2018 - Prospects for comparison of matter and antimatter gravitation with ALPHA-g.pdf:application/pdf},
}

@incollection{dambrosio_runge-kutta_2023,
	location = {Cham},
	title = {Runge-Kutta Methods},
	isbn = {978-3-031-31343-1},
	url = {https://doi.org/10.1007/978-3-031-31343-1_4},
	abstract = {Order barriers of linear multistep methods are rather severe. In this chapter, we move to a different family of methods, i.e., Runge-Kutta methods, enabling better order and stability barriers. The strategy is novel with respect to that beyond linear multistep methods: indeed, it is no longer of multistep type, but we move to a multistage strategy relying on the information in some additional points, located inside each subinterval of the domain discretization. Order analysis is here presented according to the theory of rooted trees and B-series.},
	pages = {109--150},
	booktitle = {Numerical Approximation of Ordinary Differential Problems : From Deterministic to Stochastic Numerical Methods},
	publisher = {Springer Nature Switzerland},
	author = {D’Ambrosio, Raffaele},
	editor = {D'Ambrosio, Raffaele},
	urldate = {2025-10-10},
	date = {2023},
	langid = {english},
	doi = {10.1007/978-3-031-31343-1_4},
	file = {Full Text PDF:/home/lars/Zotero/storage/2AHLRIZU/D’Ambrosio - 2023 - Runge-Kutta Methods.pdf:application/pdf},
}

@incollection{dambrosio_linear_2023,
	location = {Cham},
	title = {Linear Multistep Methods},
	isbn = {978-3-031-31343-1},
	url = {https://doi.org/10.1007/978-3-031-31343-1_3},
	abstract = {Even if one-step methods provide the simplest and maybe most intuitive family of step-by-step numerical schemes, enlarging this class with more complex methods could be useful in order to achieve better accuracy and stability properties. For this reason, we present a more general family of methods relying on a multistep structure and provide the analysis of accuracy and stability properties of linear multistep methods.},
	pages = {73--107},
	booktitle = {Numerical Approximation of Ordinary Differential Problems : From Deterministic to Stochastic Numerical Methods},
	publisher = {Springer Nature Switzerland},
	author = {D’Ambrosio, Raffaele},
	editor = {D'Ambrosio, Raffaele},
	urldate = {2025-10-10},
	date = {2023},
	langid = {english},
	doi = {10.1007/978-3-031-31343-1_3},
	file = {Full Text PDF:/home/lars/Zotero/storage/262EDLAD/D’Ambrosio - 2023 - Linear Multistep Methods.pdf:application/pdf},
}

@software{beer_nukesorpueue_2025,
	title = {Nukesor/pueue},
	rights = {Apache-2.0},
	url = {https://github.com/Nukesor/pueue},
	abstract = {:stars: Manage your shell commands.},
	author = {Beer, Arne Christian},
	urldate = {2025-10-16},
	date = {2025-10-15},
	note = {original-date: 2015-09-04T16:24:23Z},
	keywords = {command-line, command-line-tool, daemon, hacktoberfest, queue-manager, queue-tasks, rust, shell-queue},
}

@software{team_pandas-devpandas_2025,
	title = {pandas-dev/pandas: Pandas},
	url = {https://zenodo.org/records/17229934},
	shorttitle = {pandas-dev/pandas},
	abstract = {Pandas is a powerful data structures for data analysis, time series, and statistics.},
	publisher = {Zenodo},
	author = {team, The pandas development},
	urldate = {2025-10-16},
	date = {2025-09-30},
	doi = {10.5281/zenodo.17229934},
	keywords = {data science, python},
	file = {Snapshot:/home/lars/Zotero/storage/QV289HHN/17229934.html:text/html},
}

@inproceedings{sanderson_armadillo_2025,
	title = {Armadillo: An Efficient Framework for Numerical Linear Algebra},
	url = {http://arxiv.org/abs/2502.03000},
	doi = {10.1109/ICCAE64891.2025.10980539},
	shorttitle = {Armadillo},
	abstract = {A major challenge in the deployment of scientific software solutions is the adaptation of research prototypes to production-grade code. While high-level languages like {MATLAB} are useful for rapid prototyping, they lack the resource efficiency required for scalable production applications, necessitating translation into lower level languages like C++. Further, for machine learning and signal processing applications, the underlying linear algebra primitives, generally provided by the standard {BLAS} and {LAPACK} libraries, are unwieldy and difficult to use, requiring manual memory management and other tedium. To address this challenge, the Armadillo C++ linear algebra library provides an intuitive interface for writing linear algebra expressions that are easily compiled into efficient production-grade implementations. We describe the expression optimisations we have implemented in Armadillo, exploiting template metaprogramming. We demonstrate that these optimisations result in considerable efficiency gains on a variety of benchmark linear algebra expressions.},
	pages = {303--307},
	booktitle = {2025 17th International Conference on Computer and Automation Engineering ({ICCAE})},
	author = {Sanderson, Conrad and Curtin, Ryan},
	urldate = {2025-10-16},
	date = {2025-03-20},
	eprinttype = {arxiv},
	eprint = {2502.03000 [cs]},
	keywords = {Computer Science - Mathematical Software},
	file = {Preprint PDF:/home/lars/Zotero/storage/UNJD6AR5/Sanderson and Curtin - 2025 - Armadillo An Efficient Framework for Numerical Linear Algebra.pdf:application/pdf;Snapshot:/home/lars/Zotero/storage/RHNN68A2/2502.html:text/html},
}

@article{sanderson_practical_2019,
	title = {Practical Sparse Matrices in C++ with Hybrid Storage and Template-Based Expression Optimisation},
	volume = {24},
	issn = {2297-8747},
	url = {http://arxiv.org/abs/1811.08768},
	doi = {10.3390/mca24030070},
	abstract = {Despite the importance of sparse matrices in numerous fields of science, software implementations remain difficult to use for non-expert users, generally requiring the understanding of underlying details of the chosen sparse matrix storage format. In addition, to achieve good performance, several formats may need to be used in one program, requiring explicit selection and conversion between the formats. This can be both tedious and error-prone, especially for non-expert users. Motivated by these issues, we present a user-friendly and open-source sparse matrix class for the C++ language, with a high-level application programming interface deliberately similar to the widely used {MATLAB} language. This facilitates prototyping directly in C++ and aids the conversion of research code into production environments. The class internally uses two main approaches to achieve efficient execution: (i) a hybrid storage framework, which automatically and seamlessly switches between three underlying storage formats (compressed sparse column, Red-Black tree, coordinate list) depending on which format is best suited and/or available for specific operations, and (ii) a template-based meta-programming framework to automatically detect and optimise execution of common expression patterns. Empirical evaluations on large sparse matrices with various densities of non-zero elements demonstrate the advantages of the hybrid storage framework and the expression optimisation mechanism.},
	pages = {70},
	number = {3},
	journaltitle = {Mathematical and Computational Applications},
	shortjournal = {{MCA}},
	author = {Sanderson, Conrad and Curtin, Ryan},
	urldate = {2025-10-16},
	date = {2019-07-19},
	eprinttype = {arxiv},
	eprint = {1811.08768 [cs]},
	keywords = {Computer Science - Mathematical Software},
	file = {Preprint PDF:/home/lars/Zotero/storage/PZ5ZIXJU/Sanderson and Curtin - 2019 - Practical Sparse Matrices in C++ with Hybrid Storage and Template-Based Expression Optimisation.pdf:application/pdf;Snapshot:/home/lars/Zotero/storage/CQNT3AKH/1811.html:text/html},
}

@software{pranav_p-ranavargparse_2025,
	title = {p-ranav/argparse},
	rights = {{MIT}},
	url = {https://github.com/p-ranav/argparse},
	abstract = {Argument Parser for Modern C++},
	author = {Pranav},
	urldate = {2025-10-16},
	date = {2025-10-15},
	note = {original-date: 2019-03-30T22:28:48Z},
	keywords = {argument-parser, cpp17, cross-platform, header-only, library, mit-license},
}

@software{ramirez_typer_nodate,
	title = {Typer},
	url = {https://github.com/fastapi/typer},
	author = {Ramírez, Sebastián},
}

@online{noauthor_github_2025,
	title = {{GitHub} Copilot · Your {AI} pair programmer},
	url = {https://github.com/features/copilot},
	abstract = {{GitHub} Copilot works alongside you directly in your editor, suggesting whole lines or entire functions for you.},
	titleaddon = {{GitHub}},
	urldate = {2025-10-16},
	date = {2025},
	langid = {english},
	file = {Snapshot:/home/lars/Zotero/storage/CZLEGSHZ/copilot.html:text/html},
}

@unpublished{kubar_lecture_2025,
	location = {Karlsruhe Institute for Technology, Karlsruhe},
	title = {Lecture: Molecular Dynamic Simulations Summer 2025},
	type = {Lecture},
	howpublished = {Lecture},
	author = {Kubar, Tomas and Elstner, Marcus and Kozlowska, Mariana},
	date = {2025-05-20},
	langid = {german},
}

@article{fjordholm_numerical_nodate,
	title = {Numerical methods for {ODEs}},
	url = {https://www.uio.no/studier/emner/matnat/math/MAT3110/h24/pensumliste/numerical_methods_tufte.pdf},
	author = {Fjordholm, Ulrik Skre},
	langid = {english},
	file = {PDF:/home/lars/Zotero/storage/P27ZBG42/Fjordholm - Numerical methods for ODEs.pdf:application/pdf},
}

@online{cheever_fourth_2022,
	title = {Fourth Order Runge-Kutta},
	url = {https://lpsa.swarthmore.edu/NumInt/NumIntFourth.html},
	author = {Cheever, Erik},
	urldate = {2025-10-16},
	date = {2022},
	file = {Fourth Order Runge-Kutta:/home/lars/Zotero/storage/UT45E36A/NumIntFourth.html:text/html},
}

@article{noauthor_pdf_2025,
	title = {({PDF}) Why is Boris algorithm so good?},
	url = {https://www.researchgate.net/publication/258081219_Why_is_Boris_algorithm_so_good},
	doi = {10.1063/1.4818428},
	abstract = {{PDF} {\textbar} Due to its excellent long term accuracy, the Boris algorithm is the de facto standard for advancing a charged particle. Despite its popularity, up... {\textbar} Find, read and cite all the research you need on {ResearchGate}},
	journaltitle = {{ResearchGate}},
	urldate = {2025-10-16},
	date = {2025-08-06},
	langid = {english},
	file = {Snapshot:/home/lars/Zotero/storage/RGHADD4H/258081219_Why_is_Boris_algorithm_so_good.html:text/html;Submitted Version:/home/lars/Zotero/storage/IHRBN7XU/2025 - (PDF) Why is Boris algorithm so good.pdf:application/pdf},
}

@article{webb_symplectic_2014,
	title = {Symplectic integration of magnetic systems},
	volume = {270},
	issn = {0021-9991},
	url = {https://www.sciencedirect.com/science/article/pii/S0021999114002368},
	doi = {10.1016/j.jcp.2014.03.049},
	abstract = {Simulation of the long time behavior of systems requires more than just numerical stability to return dependable results – it must preserve the underlying geometric structure of the continuous equations. Symplectic integrators are the most common form of geometric integrator, and are therefore of interest in simulating plasmas for many plasma periods, for example. We present here results on generating symplectic integrators for magnetic systems, and in particular show that the algorithms due to Boris and Vay are symplectic.},
	pages = {570--576},
	journaltitle = {Journal of Computational Physics},
	shortjournal = {Journal of Computational Physics},
	author = {Webb, Stephen D.},
	urldate = {2025-10-16},
	date = {2014-08-01},
	keywords = {Boris integrator, Magnetic systems, Symplectic integration},
	file = {ScienceDirect Full Text PDF:/home/lars/Zotero/storage/G4JCWPPB/Webb - 2014 - Symplectic integration of magnetic systems.pdf:application/pdf;ScienceDirect Snapshot:/home/lars/Zotero/storage/H8YLEJ8C/S0021999114002368.html:text/html},
}

@software{hoppock_iwhoppockboris-algorithm_2025,
	title = {iwhoppock/boris-algorithm},
	url = {https://github.com/iwhoppock/boris-algorithm},
	abstract = {The Boris algorithm for numerically tracing non-relativistic charged particles in electromagnetic fields, written in C, matlab, and python},
	author = {Hoppock, Ian},
	urldate = {2025-10-16},
	date = {2025-09-29},
	note = {original-date: 2018-11-15T15:21:28Z},
	keywords = {boris, boris-algorithm, particle-tracing, particle-tracking, physics, plasma, plasma-physics, plasma-simulation, plasma-turbulence, single-particle-motion},
}
