Tootfinder

A comprehensive Gaia spectroscopic study of stars in the Sco-Cen complex: star formation history and disk lifetime
Min Fang, Gregory J. Herczeg
https://arxiv.org/abs/2509.19693 …

A comprehensive Gaia spectroscopic study of stars in the Sco-Cen complex: star formation history and disk lifetime
We re-evaluate the star formation history of the nearby Sco-Cen OB Association with a comprehensive analysis of Gaia XP spectra of more than 7,800 potential members. New spectral classifications are obtained by fitting individual XP spectra with templates derived from empirical spectra of young stars. Combining these spectral classifications in this work and in the literature with archival photometry leads to estimates of V-band extinction and stellar luminosities for a total of 8,846 sources. …

#HotTake:
Liberalism is a syncretic religion where the highest deity is the State and all other deities are subject to it. Liberalism asserts this distinction by classifying political systems as deriving from "natural law" and religions as faith systems. Classical liberalism divided the world into "primitive" beliefs and "enlightened" beliefs. Atheistic anarchism aligns with this distinction by opposing the state and capitalism through logic, rather than asserting that the state and capitalism are themselves systems of faith. Atheistic anarchism further reinforces the narrative of the liberal state by aligning with the liberal understanding of religion as a "primitive" institution.
Graeber et al pointed out the connections between property rights as a form of fedishism (magic) and the evolution of currency, debt, taxes, and the state from temples.

Random Forest Classification of MBTA Gravitational-Wave Triggers for Low-Latency Detection
Lorenzo Mobilia, Gianluca Maria Guidi
https://arxiv.org/abs/2509.12882 https://…

Random Forest Classification of MBTA Gravitational-Wave Triggers for Low-Latency Detection
In this work, we explore a possible application of a machine learning classifier for candidate events in a template-based search for gravitational-wave (GW) signals from various compact system sources. We analyze data from the O3a and O3b data acquisition campaign, during which the sensitivity of ground-based detectors is limited by real non-Gaussian noise transient. The state-of-the-art searches for such signals tipically rely on the signal-to-noise ratio (SNR) and a chi-square test to assess …

Applications of the Vendi score in genomic epidemiology
Bjarke Frost Nielsen, Amey P. Pasarkar, Qiqi Yang, Bryan T. Grenfell, Adji Bousso Dieng
https://arxiv.org/abs/2509.22520 …

Applications of the Vendi score in genomic epidemiology
The Vendi score (VS), a diversity metric recently conceived in the context of machine learning, with applications in a wide range of fields, has a few distinct advantages over the metrics commonly used in ecology. It is classification-independent, incorporates abundance information, and has a tunable sensitivity to rare/abundant types. Using rich COVID-19 sequence data as a paradigm, we develop methods for applying the VS to time-resolved sequence data. We show how the VS allows for characteriz…

Wide Area VISTA Extragalactic Survey (WAVES): Selection of targets for the Wide survey using decision-tree classification
G. Kaur, M. Bilicki, S. Bellstedt, E. Tempel, W. A. Hellwing, I. Baldry, B. Bandi, S. Barsanti, S. Driver, N. Guerra-Varas, B. Holwerda, C. Lagos, J. Loveday, A. Robotham
https://arxiv.org/abs/2510.11132

Wide Area VISTA Extragalactic Survey (WAVES): Selection of targets for the Wide survey using decision-tree classification
The Wide-Area VISTA Extragalactic Survey (WAVES) on the 4-metre Multi-Object Spectroscopic Telescope (4MOST) includes two flux-limited subsurveys with very high (95\%) completeness requirements: Wide over $\sim\!1200$ deg$^2$ and Deep over $\sim\!65$ deg$^2$. Both are $Z$-band selected, respectively as $Z<21.1$ and $Z<21.25$ mag, and additionally redshift-limited, while the true redshifts are not known a priori but will be only measured by 4MOST. Here, we present a classification-based method t…

Large Language Model-Based Uncertainty-Adjusted Label Extraction for Artificial Intelligence Model Development in Upper Extremity Radiography
Hanna Kreutzer, Anne-Sophie Caselitz, Thomas Dratsch, Daniel Pinto dos Santos, Christiane Kuhl, Daniel Truhn, Sven Nebelung
https://arxiv.org/abs/2510.05664 …

Large Language Model-Based Uncertainty-Adjusted Label Extraction for Artificial Intelligence Model Development in Upper Extremity Radiography
Objectives: To evaluate GPT-4o's ability to extract diagnostic labels (with uncertainty) from free-text radiology reports and to test how these labels affect multi-label image classification of musculoskeletal radiographs. Methods: This retrospective study included radiography series of the clavicle (n=1,170), elbow (n=3,755), and thumb (n=1,978). After anonymization, GPT-4o filled out structured templates by indicating imaging findings as present ("true"), absent ("false"), or "uncertain." To …

A Novel Tunable Controller for Grid Forming Converters towards Critical Services Application
Yangyadatta Tripathy, Barjeev Tyagi
https://arxiv.org/abs/2509.01748 https://…

A Novel Tunable Controller for Grid Forming Converters towards Critical Services Application
This paper demonstrates the key features of a control system applicable to inverter-based resources (IBR), which is based on grid-forming technology. Such resources are classified as grid-forming or grid-following converters based on the type of output with or without grid connection. With rapid growth in the energy sector to adopt carbon-free generation, Grid Forming Converter (GFC) seems suitable for power provision to remote or islanded operation of converters. A fully-fledged bulk power gri…

Cosmic string gravitational wave backgrounds at LISA: I. Signal survey, template reconstruction, and model comparison
Androniki Dimitriou, Daniel G. Figueroa, Peera Simakachorn, Bryan Zaldivar
https://arxiv.org/abs/2508.05395

Cosmic string gravitational wave backgrounds at LISA: I. Signal survey, template reconstruction, and model comparison
We present a catalog of gravitational wave background (GWB) signal templates from cosmic-string networks, based on relevant models proposed in the literature. We classify templates as conventional, based on standard cosmology and Nambu-Goto results (VOS and BOS), and beyond conventional, based on modifications of a) the loop number density (LRS, super, metastable, current-carrying strings), b) the expansion history (non-standard cosmologies, extra degrees of freedom, either thermal or secluded)…

Revisiting the systematics of Brevipalpus flat mites (Tenuipalpidae): phylogeny, species groups and cryptic diversity
Renata Santos de Mendon\c{c}a (PNPD/CAPES, UnB, CENARGEN), Francisco Ferragut (UPV, IAM), Isis Carolina S. de Oliveira (UnB, CENARGEN), Aline Daniele Tassi (ESALQ, UF), Felipe Fileni (UMR CBGP), Ronald Ochoa (UMR CBGP), Denise Navia (UMR CBGP)
h…

Revisiting the systematics of Brevipalpus flat mites (Tenuipalpidae): phylogeny, species groups and cryptic diversity
Phytophagous mites in the genus Brevipalpus (Tenuipalpidae) can be major pests, causing direct damage to their host plants or vectoring viruses. However, a phylogeny-based classification to understand their evolution and predict their bioecological aspects is lacking. Accurate species identification is crucial for studying pathosystem interactions, implementing quarantine measures, and developing control strategies. This study revisited the classification of Brevipalpus based on phylogenetic re…

Short-Duration Gravitational Wave Burst Detection using Convolutional Neural Network
Matteo Pracchia, Sacha Peters, Maxime Fays
https://arxiv.org/abs/2509.13241 https://

Short-Duration Gravitational Wave Burst Detection using Convolutional Neural Network
Detecting unmodeled gravitational wave (GW) bursts presents significant challenges due to the lack of accurate waveform templates required for matched-filtering techniques. A primary difficulty lies in distinguishing genuine signals from transient noise. Machine learning approaches, particularly convolutional neural networks (CNNs), offer promising alternatives for this classification problem. This paper presents a CNN-based pipeline for detecting short GW bursts (duration $< 10~\mathrm{s}$), a…