FYS4150/projects/project3/ref.bib

@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},
}

@online{roskam_supercritical_2025,
	title = {Supercritical Fluid Chromatography for Chiral Analysis, Part 1: Theoretical Background {\textbar} {LCGC} International},
	url = {https://www.chromatographyonline.com/view/supercritical-fluid-chromatography-for-chiral-analysis-part-1-theoretical-background},
	shorttitle = {Supercritical Fluid Chromatography for Chiral Analysis, Part 1},
	abstract = {With the substantial developments carried out over the past years in instrumentation, columns, and detector hyphenation, the interest in chiral supercritical fluid chromatography ({SFC}) has been steadily growing in various fields. In the first part of this review article, the theoretical advantages, technological developments, and common practices in chiral {SFC} are discussed.},
	author = {Roskam, Gerry and Velde, Bas van de and Gargano, Andrea and Kohler, Isabelle},
	urldate = {2025-10-05},
	date = {2025-10-05},
	langid = {english},
	file = {Snapshot:/home/lars/Zotero/storage/LWSHJMVJ/supercritical-fluid-chromatography-for-chiral-analysis-part-1-theoretical-background.html:text/html},
}

@article{furet_first_2022,
	title = {The First Class of Small Molecules Potently Disrupting the {YAP}‐{TEAD} Interaction by Direct Competition},
	volume = {17},
	doi = {10.1002/cmdc.202200303},
	abstract = {Inhibition of the {YAP}‐{TEAD} protein‐protein interaction is an attractive therapeutic concept under intense investigation with the objective to treat cancers associated with a dysregulation of the Hippo pathway. However, owing to the very extended surface of interaction of the two proteins, the identification of small drug‐like molecules able to efficiently prevent {YAP} from binding to {TEAD} by direct competition has been elusive so far. We disclose here the discovery of the first class of small molecules potently inhibiting the {YAP}‐{TEAD} interaction by binding at one of the main interaction sites of {YAP} at the surface of {TEAD}. These inhibitors, providing a path forward to pharmacological intervention in the Hippo pathway, evolved from a weakly active virtual screening hit advanced to high potency by structure‐based design.},
	journaltitle = {{ChemMedChem}},
	shortjournal = {{ChemMedChem}},
	author = {Furet, Pascal and Bordas, Vincent and Douget, Mickaël and Salem, Bahaa and Mesrouze, Yannick and Imbach‐Weese, Patricia and Sellner, Holger and Vögtle, Markus and Soldermann, Nicolas and Chapeau, Emilie and Wartmann, Markus and Scheufler, Clemens and Fernández, César and Kallen, Joerg and Guagnano, Vito and Chene, Patrick and Schmelzle, Tobias},
	date = {2022-09-02},
	file = {Full Text PDF:/home/lars/Zotero/storage/J399MX84/Furet et al. - 2022 - The First Class of Small Molecules Potently Disrupting the YAP‐TEAD Interaction by Direct Competitio.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 = {antigravity, antihydrogen, antimatter, {CPT}, gravity, 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},
}