Tootfinder

Comparative analysis of machine learning techniques for feature selection and classification of Fast Radio Bursts
Ailton J. B. J\'unior, J\'eferson A. S. Fortunato, Leonardo J. Silvestre, Thonimar V. Alencar, Wiliam S. Hip\'olito-Ricaldi
https://arxiv.org/abs/2506.18854

Comparative analysis of machine learning techniques for feature selection and classification of Fast Radio Bursts
Fast Radio Bursts (FRBs) are millisecond-duration radio transients of extragalactic origin, exhibiting a wide range of physical and observational properties. Distinguishing between repeating and non-repeating FRBs remains a key challenge in understanding their nature. In this work, we apply unsupervised machine learning techniques to classify FRBs based on both primary observables from the CHIME catalog and physically motivated derived features. We evaluate three hybrid pipelines combining dime…

This https://arxiv.org/abs/2204.04198 has been replaced.
link: https://scholar.google.com/scholar?q=a

Modern applications of machine learning in quantum sciences
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, gen…

This https://arxiv.org/abs/2503.04672 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_…

Finding White Dwarfs' Hidden Companions using an Unsupervised Machine Learning Technique
White dwarfs (WD) with main-sequence (MS) companions are crucial probes of stellar evolution. However, due to the significant difference in their luminosities, the WD is often outshined by the MS star. The aim of this work is to find hidden companions in Gaia's sample of WD candidates. Our methodology involves applying an unsupervised machine learning algorithm for dimensionality reduction and clustering, known as Self-Organizing Map (SOM), to Gaia BP/RP (XP) spectra. This strategy allows us to…

Covariance Supervised Principal Component Analysis
Theodosios Papazoglou, Guosheng Yin
https://arxiv.org/abs/2506.19247 https://arxiv…

Covariance Supervised Principal Component Analysis
Principal component analysis (PCA) is a widely used unsupervised dimensionality reduction technique in machine learning, applied across various fields such as bioinformatics, computer vision and finance. However, when the response variables are available, PCA does not guarantee that the derived principal components are informative to the response variables. Supervised PCA (SPCA) methods address this limitation by incorporating response variables into the learning process, typically through an o…

Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization
Ana Lawry Aguila, Peirong Liu, Oula Puonti, Juan Eugenio Iglesias
https://arxiv.org/abs/2506.10233

Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization
Supervised machine learning has enabled accurate pathology detection in brain MRI, but requires training data from diseased subjects that may not be readily available in some scenarios, for example, in the case of rare diseases. Reconstruction-based unsupervised anomaly detection, in particular using diffusion models, has gained popularity in the medical field as it allows for training on healthy images alone, eliminating the need for large disease-specific cohorts. These methods assume that a …

Line shapes in time- and angle-resolved photoemission spectroscopy explored by machine learning
Tami C. Meyer, Gesa-R. Siemann, Paulina Majchrzak, Thomas Seyller, Jennifer Rigden, Yu Zhang, Emma Springate, Charlotte Sanders, Philip Hofmann
https://arxiv.org/abs/2506.02137

Line shapes in time- and angle-resolved photoemission spectroscopy explored by machine learning
Time- and angle-resolved photoemission spectroscopy is a powerful technique for investigating the dynamics of excited carriers in quantum materials. Typically, data analysis proceeds via the inspection of time distribution curves (TDCs), which represent the time-dependent photoemission intensity in a region of interest -- often chosen somewhat arbitrarily -- in energy-momentum space. Here, we employ $k$-means, an unsupervised machine learning technique, to systematically investigate trends in T…

This https://arxiv.org/abs/2404.05836 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCY_…

Hiden Topics in Robotic Process Automation -- an Approach based on AI
Robotic Process Automation (RPA) has rapidly evolved into a widely recognized and influential software technology. Its growing relevance has sparked diverse research efforts across various disciplines. This study aims to map the scientific landscape of RPA by identifying key thematic areas, tracking their development over time, and assessing their academic impact. To achieve this, we apply an unsupervised machine learning technique Latent Dirichlet Allocation (LDA) to analyze the abstracts of o…

This https://arxiv.org/abs/2409.14453 has been replaced.
initial toot: https://mastoxiv.page/@arX…

Self-Attention Assistant Classification of Non-Hermitian Phases in Two-Dimensional Lattice
Classification of the non-Hermitian phases in high-dimensional lattice becomes challenging due to interplay of the band topology and non-Hermiticity. The significant increase in data dimensions and the number of categories has rendered traditional supervised learning and unsupervised manifold learning failed. Here, we propose the self-attention assistant machine learning for clustering non-Hermitian phases in two-dimensional lattice. By incorporating the self-attention mechanism, the model can …

COSMOS-Web: Estimating Physical Parameters of Galaxies Using Self-Organizing Maps
Fatemeh Abedini, Ghassem Gozaliasl, Akram Hasani Zonoozi, Atousa Kalantari, Maarit Korpi-Lagg, Olivier Ilbert, Hollis B. Akins, Natalie Allen, Rafael C. Arango-Toro, Caitlin M. Casey, Nicole E. Drakos, Andreas L. Faisst, Carter Flayhart, Maximilien Franco, Santosh Harish, Hossein Hatamnia, Jeyhan S. Kartaltepe, Ali Ahmad Khostovan, Anton M. Koekemoer, Vasily Kokorev, Rebecca L. Larson, Gavin Leroy, Daizho…

COSMOS-Web: Estimating Physical Parameters of Galaxies Using Self-Organizing Maps
The COSMOS-Web survey, with its unparalleled combination of multiband data -- particularly near-infrared imaging from JWST's NIRCam (F115W, F150W, F277W, and F444W), provides a transformative dataset reaching down to approximately 28th magnitude (F444W) for studying galaxy evolution. In this work, we employ Self-Organizing Maps (SOMs), an unsupervised machine learning method, to estimate key physical parameters of galaxies, redshift, stellar mass, star formation rate (SFR), specific SFR (sSFR),…

This https://arxiv.org/abs/2412.03747 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_hepe…

Model-agnostic search for dijet resonances with anomalous jet substructure in proton-proton collisions at $\sqrt{s}$ = 13 TeV
This paper presents a model-agnostic search for narrow resonances in the dijet final state in the mass range 1.8-6 TeV. The signal is assumed to produce jets with substructure atypical of jets initiated by light quarks or gluons, with minimal additional assumptions. Search regions are obtained by utilizing multivariate machine-learning methods to select jets with anomalous substructure. A collection of complementary anomaly detection methods - based on unsupervised, weakly supervised, and semis…

This https://arxiv.org/abs/2505.12494 has been replaced.
initial toot: https://mastoxiv.page/@arX…

SMURF: Scalable method for unsupervised reconstruction of flow in 4D flow MRI
We introduce SMURF, a scalable and unsupervised machine learning method for simultaneously segmenting vascular geometries and reconstructing velocity fields from 4D flow MRI data. SMURF models geometry and velocity fields using multilayer perceptron-based functions incorporating Fourier feature embeddings and random weight factorization to accelerate convergence. A measurement model connects these fields to the observed image magnitude and phase data. Maximum likelihood estimation and subsampli…