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Ontolearn-A Framework for Large-scale OWL Class Expression Learning in Python
Caglar Demir, Alkid Baci, N'Dah Jean Kouagou, Leonie Nora Sieger, Stefan Heindorf, Simon Bin, Lukas Bl\"ubaum, Alexander Bigerl, Axel-Cyrille Ngonga Ngomo
https://arxiv.org/abs/2510.11561

Ontolearn-A Framework for Large-scale OWL Class Expression Learning in Python
In this paper, we present Ontolearn-a framework for learning OWL class expressions over large knowledge graphs. Ontolearn contains efficient implementations of recent stateof-the-art symbolic and neuro-symbolic class expression learners including EvoLearner and DRILL. A learned OWL class expression can be used to classify instances in the knowledge graph. Furthermore, Ontolearn integrates a verbalization module based on an LLM to translate complex OWL class expressions into natural language sen…

The AURORA Survey: Ionizing Photon Production Efficiency with Minimal Nebular Dust Attenuation Systematics
Anthony J. Pahl, Alice Shapley, Naveen A. Reddy, Ryan Sanders, Michael W. Topping, Danielle A. Berg, Callum T. Donnan, James S. Dunlop, Richard S. Ellis, N. M. F\"orster Schreiber, K. Glazebrook, Derek J. McLeod, Max Pettini, Daniel Schaerer
https://

The AURORA Survey: Ionizing Photon Production Efficiency with Minimal Nebular Dust Attenuation Systematics
We present ionizing photon production efficiencies ($ξ_{\rm ion}$) for 63 z=1.5-6.9 star-forming galaxies using precise nebular dust attenuation corrections from the JWST/AURORA survey. A subset of objects within AURORA have individually-determined nebular dust attenuation curves, which vary significantly in shape and normalization, resulting in reduced systematic uncertainty when constraining the total attenuation of H$α$ luminosity, and thus the intrinsic ionizing output within our sample. …

Deterministic hBN bubbles as a versatile platform for studies on single-photon emitters
Piotr Tatarczak, Tomasz F\k{a}s, Jan Paw{\l}owski, Aleksandra Krystyna D\k{a}browska, Jan Suffczy\'nski, Piotr Wr\'obel, Andrzej Wysmo{\l}ek, Johannes Binder
https://arxiv.org/abs/2510.11610

Deterministic hBN bubbles as a versatile platform for studies on single-photon emitters
Single-photon emitters (SPEs) in two-dimensional materials are highly promising candidates for quantum technologies. SPEs in hexagonal boron nitride (hBN) have been widely investigated, but mostly in exfoliated or powder samples that require an activation process, making it difficult to compare studies and reproduce results. Here, we address this problem and propose a platform based on large-area metaloraganic vapour phase epitaxy (MOVPE)-grown hBN, which combines reproducibility and scalabilit…

pyGinkgo: A Sparse Linear Algebra Operator Framework for Python
Keshvi Tuteja, Gregor Olenik, Roman Mishchuk, Yu-Hsiang Tsai, Markus G\"otz, Achim Streit, Hartwig Anzt, Charlotte Debus
https://arxiv.org/abs/2510.08230

pyGinkgo: A Sparse Linear Algebra Operator Framework for Python
Sparse linear algebra is a cornerstone of many scientific computing and machine learning applications. Python has become a popular choice for these applications due to its simplicity and ease of use. Yet high performance sparse kernels in Python remain limited in functionality, especially on modern CPU and GPU architectures. We present pyGinkgo, a lightweight and Pythonic interface to the Ginkgo library, offering high-performance sparse linear algebra support with platform portability across CU…

Evaluating noises of boson sampling with statistical benchmark methods
Yang Ji, Yongjin Ye, Qiao Wang, Shi Wang, Jie Hou, Yongzheng Wu, Zijian Wang, Bo Jiang
https://arxiv.org/abs/2510.00056

Evaluating noises of boson sampling with statistical benchmark methods
The lack of self-correcting codes hiders the development of boson sampling to be large-scale and robust. Therefore, it is important to know the noise levels in order to cautiously demonstrate the quantum computational advantage or realize certain tasks. Based on those statistical benchmark methods such as the correlators and the clouds, which are initially proposed to discriminate boson sampling and other mockups, we quantificationally evaluate noises of photon partial distinguishability and ph…

pyspect: An Extensible Toolbox for Automatic Construction of Temporal Logic Trees via Reachability Analysis
Kaj Munhoz Arfvidsson, Loizos Hadjiloizou, Frank J. Jiang, Karl H. Johansson, Jonas M{\aa}rtensson
https://arxiv.org/abs/2510.11316

pyspect: An Extensible Toolbox for Automatic Construction of Temporal Logic Trees via Reachability Analysis
In this paper, we present pyspect, a Python toolbox that simplifies the use of reachability analysis for temporal logic problems. Currently, satisfying complex requirements in cyber-physical systems requires significant manual effort and domain expertise to develop the underlying reachability programs. This high development effort limits the broader adoption of reachability analysis for complex verification problems. To address this, pyspect provides a method-agnostic approach to performing rea…

Incoherent repumping scheme in the $^{88}$Sr$^{ }$ five-level manifold
Valentin Martimort, Sacha Guesne, Derwell Drapier, Vincent Tugaye, Lilay Gros-Desormeaux, Valentin Cambier, Albane Douillet, Luca Guidoni, Jean-Pierre Likforman
https://arxiv.org/abs/2512.08710 https://arxiv.org/pdf/2512.08710 https://arxiv.org/html/2512.08710
arXiv:2512.08710v1 Announce Type: new
Abstract: Laser-cooled trapped ions are at the heart of modern quantum technologies and their cooling dynamics often deviate from the simplified two-level atom model. Doppler cooling of the $^{88}$Sr$^{ }$ ion involves several electronic levels and repumping channels that strongly influence fluorescence.In this work, we study a repumping scheme for the $^{88}$Sr$^{ }$ ion by combining precision single-ion spectroscopy with comprehensive numerical modeling based on optical Bloch equations including 18 Zeeman sublevels. We show that, although the observed fluorescence spectra retain a Lorentzian lineshape, their width and amplitude cannot be explained by a two-level atom description. Moreover, we find the optimal repumping conditions for maximizing the photon scattering rate.
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Argus: JAX state-space filtering for gravitational wave detection with a pulsar timing array
Tom Kimpson, Nicholas J. O'Neill, Patrick M. Meyers, Andrew Melatos
https://arxiv.org/abs/2510.11077

Argus: JAX state-space filtering for gravitational wave detection with a pulsar timing array
Argus is a high-performance Python package for detecting and characterising nanohertz gravitational waves in pulsar timing array data. The package provides a complete Bayesian inference framework based on state-space models, using Kalman filtering for efficient likelihood evaluation. Argus leverages JAX for just-in-time compilation, GPU acceleration, and automatic differentiation, facilitating rapid Bayesian inference with gradient-based samplers. The state-space approach provides a computation…

ELENA: a software for fast and precise computation of first order phase transitions and gravitational waves production in particle physics models
Francesco Costa, Jaime Hoefken Zink, Michele Lucente, Silvia Pascoli, Salvador Rosauro-Alcaraz
https://arxiv.org/abs/2510.00289

ELENA: a software for fast and precise computation of first order phase transitions and gravitational waves production in particle physics models
We present ELENA (EvaLuator of tunnElliNg Actions), an open-source Python package designed to compute the full evolution of first-order phase transitions in the early Universe generated by particle physics models, taking into account several refinements that go beyond commonly assumed simplifications. The core of ELENA is based on a vectorized implementation of the tunnelling potential formalism, which allows for a fast computation of the finite-temperature tunnelling action. This, in turn, ena…

Colibri: A new tool for fast-flying PDF fits
Mark N. Costantini, Luca Mantani, James M. Moore, Valentina Schutze Sanchez, Maria Ubiali
https://arxiv.org/abs/2510.03391 https://

Colibri: A new tool for fast-flying PDF fits
We present Colibri, an open-source Python code that provides a general and flexible tool for PDF fits. The code is built so that users can implement their own PDF model, and use the built-in functionalities of Colibri for a fast computation of observables. It grants easy access to experimental data, several error propagation methodologies, including the Hessian method, the Monte Carlo replica method, and an efficient numerical Bayesian sampling algorithm. To demonstrate the capabilities of Coli…