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๐Ÿ” avoidance learning ๐Ÿ“‚ Physics
Showing 28154 results for "avoidance learning" in Physics
Physics Preprint PDF DOI

Design and performance of a large-area scintillator-based chamber for the MID subsystem of ALICE 3

Ruben Alfaro Molina, Juan Carlos Cabanillas Noris, Edmundo Garcia Solis, Laura Helena Gonzalez Trueba, Varlen Grabski, Gerardo Herrera Corral, Jesus Eduardo Munoz Mendez, Ildefonso Leon Monzon, Antonio Ortiz, Antonio Paz, Ian Perez Garcia, Ricardo Rodriguez Pineda, Solangel Rojas Torres, Guillermo Tejeda Munoz, Paola Vargas Torres, Victor Vazquez Campos, Yael Antonio Vasquez Beltran ยท 2026

This paper reports on the design and construction of a chamber for the muon identifier detector (MID) of the ALICE 3 upgrade project. The chamber consists of two sensitive layers separated by a 1 cm aโ€ฆ

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Physics Preprint PDF DOI

Traces of Helium Detected in Type Ic Supernova 2014L

Jing Lu, Wolfgang E. Kerzendorf, John T. O'Brien, Maryam Modjaz, Jared A. Goldberg, Nutan Chen, Erin Visser, Joshua V. Shields, Andrew G. Fullard ยท 2026

The absence of helium features in optical spectra is one of the classification criteria for Type Ic supernovae (SNe Ic). However, it is highly debated whether helium is truly absent in ejecta or spectโ€ฆ

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Physics Preprint PDF DOI

Learning the Exact Flux: Neural Riemann Solvers with Hard Constraints

Yucheng Zhang, Chayanon Wichitrnithed, Shukai Cai, Sourav Dutta, Kyle Mandli, Clint Dawson ยท 2026

Godunov-type methods, which obtain numerical fluxes through local Riemann problems at cell interfaces, are among the most fundamental and widely used numerical methods in computational fluid dynamics.โ€ฆ

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Physics Preprint PDF DOI

Four Generations of Quantum Biomedical Sensors

Xin Jin, Priyam Srivastava, Ronghe Wang, Yuqing Li, Jonathan Beaumariage, Tom Purdy, M. V. Gurudev Dutt, Kang Kim, Kaushik Seshadreesan, Junyu Liu ยท 2026

Quantum sensing technologies offer transformative potential for ultra-sensitive biomedical sensing, yet their clinical translation remains constrained by classical noise limits and a reliance on macroโ€ฆ

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Physics Preprint PDF DOI

CROCS Data Release I: Constraints on the Hubble Constant

Luke Weisenbach, Sophie L. Newman, Kieran Graham, Sai S. Dhavala, Benjamin Floyd, Neel Shah, Gemini 3 Flash, The CROCS Collaboration ยท 2026

Recent cosmological surveys and datasets have highlighted a variety of tensions to the concordance model of our universe, $\Lambda$CDM. Of particular interest is the Hubble tension, the $5.5\sigma$ diโ€ฆ

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Physics Preprint PDF DOI

Certifying and learning local quantum Hamiltonians

Andreas Bluhm, Matthias C. Caro, Francisco Escudero Gutierrez, Junseo Lee, Aadil Oufkir, Cambyse Rouze, Myeongjin Shin ยท 2026

In this work, we study the problems of certifying and learning quantum $k$-local Hamiltonians, for a constant $k$. Our main contributions are as follows: - Certification of Hamiltonians. We show thaโ€ฆ

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Physics Preprint PDF DOI

QTAM: QTransform Amplitude Modulation

Lorenzo Asprea, Francesco Sarandrea, Alessio Romano, Jacob Lange, Federica Legger, Sara Vallero ยท 2026

We present Q-Transform Amplitude Modulation (QTAM), a novel, fully invertible implementation of the Constant-Q Transform algorithm, designed to enable robust signal denoising and the disentanglement oโ€ฆ

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Data-Driven Optimisation of Superconducting Magnets at CEA Paris-Saclay

Damien F. G. Minenna, Guillaume Dilasser, Robin Penavaire, Valerio Calvelli, Thibault de Chabannes, Thibault Lecrevisse, Thomas Achard, Jason Le Coz, Christophe Berriaud, Benoit Bolzon, Antomne Caunes, Phillipe Fazilleau, Helene Felice, Clement Genot, Antoine Guinet, Nikola Jerance, Francois-Paul Juster, Thibaut Lemercier, Gilles Lenoir, Clement Lorin, Yann Perron, Camille Pucheu-Plante, Etienne Rochepault, Damien Simon, Francesco Stacchi, Michel Segreti, Vincent Trauchessec, Olivier Tuske, Hajar Zgour ยท 2026

Superconducting magnets for particle accelerators are particularly challenging to design because they involve a large number of coupled physical phenomena and the management of complex datasets. Artifโ€ฆ

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Physics Preprint PDF DOI

STRADAViT: Towards a Foundational Model for Radio Astronomy through Self-Supervised Transfer

Andrea DeMarco, Ian Fenech Conti, Hayley Camilleri, Ardiana Bushi, Simone Riggi ยท 2026

Next-generation radio astronomy surveys are delivering millions of resolved sources, but robust and scalable morphology analysis remains difficult across heterogeneous telescopes and imaging pipelinesโ€ฆ

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Sampling at intermediate temperatures is optimal for training large language models in protein structure prediction

L. Ghiringhelli, A. Zambon, G. Tiana ยท 2026

We investigate the parameter space of transformer models trained on protein sequence data using a statistical mechanics framework, sampling the loss landscape at varying temperatures by Langevin dynamโ€ฆ

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Top-Yukawa contributions to $pp\to b\bar{b}H$: two-loop leading-colour amplitudes

Heribertus Bayu Hartanto, Rene Poncelet ยท 2026

We derive two-loop scattering amplitudes for bottom-quark pair production in association with a Higgs boson at the LHC, focusing on terms proportional to the top-quark Yukawa coupling. We treat the boโ€ฆ

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Continuous three-dimensional imaging of nanoscale dynamics by in situ electron tomography

Timothy M. Craig, Adrien Moncomble, Ajinkya A. Kadu, Gail A. Vinnacombe-Willson, Luis M. Liz-Marzan, Robin Girod, Sara Bals ยท 2026

Direct observation of nanoscale transformations in three dimensions (3D) is essential for understanding materials evolution under operating conditions, yet dynamic electron tomography remains limited โ€ฆ

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Physics Preprint PDF DOI

Decoding Dopant-Induced Electronic Modulation in Graphene via Region-Resolved Machine Learning of XANES

Yinan Wang, Arpita Varadwaj, Teruyasu Mizoguchi, Masato Kotsugi ยท 2026

Revealing how heteroatom doping alters the local electronic structure of graphene is crucial for understanding and controlling its functional properties. In this study, we combine density functional tโ€ฆ

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Machine Learning Assisted Reconstruction of Local Electronic Structure of Non-Uniformly Strained MoS2

Soumyadip Hazra, Sraboni Dey, Arijit Kayal, Narendra Shah, Renjith Nadarajan, Joy Mitra ยท 2026

Wrinkles and nanobubbles are an integral and often unavoidable part of integrating 2D van der Waals semiconductors into actual device architectures. Despite their ubiquitous nature, quantitative correโ€ฆ

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Determining the NJL Coupling and AMM in Magnetized QCD Matter via Machine Learning

Zigeng Ding, Fan Lin, Xinyang Wang ยท 2026

In this study, we investigate the phase structure of magnetized QCD matter by determining the field-dependent parameters of the Nambu-Jona-Lasinio (NJL) model through a physics-informed machine learniโ€ฆ

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Long-range interaction effects on the phase transition, mechanical effect, and electric field response of BaTiO3 by machine learning potentials

Po-Yen Chen, Teruyasu Mizoguchi ยท 2026

Bulk materials are governed by both short-range and long-range interactions, both of which are naturally captured in conventional density functional theory (DFT) calculations through Ewald summation oโ€ฆ

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Learning noisy phase transition dynamics from stochastic partial differential equations

Luning Sun, Van Hai Nguyen, Shusen Liu, John Klepeis, Fei Zhou ยท 2026

The non-equilibrium dynamics of mesoscale phase transitions are fundamentally shaped by thermal fluctuations, which not only seed instabilities but actively control kinetic pathways, including rare baโ€ฆ

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What U Can Do: New Solutions and New Challenges Beyond Leading Order

Yi Pang, Robert J. Saskowski ยท 2026

String theories naturally exhibit dualities that lead to hidden symmetries in the low-energy effective description, which have been used to great effect to generate supergravity solutions. We review rโ€ฆ

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Large-scale nonlinear optical computing with incoherent light via linear diffractive systems

Alexander Chen, Yuntian Wang, Md Sadman Sakib Rahman, Yuhang Li, Aydogan Ozcan ยท 2026

Nonlinear computation is essential for various information processing tasks. Optical implementations are attractive because passive light propagation can manipulate high-dimensional signals with extreโ€ฆ

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Schr\"odinger's Seed: Purr-fect Initialization for an Impurr-fect Universe

Mi chen, Renhao Ye ยท 2026

Context. Random seed selection in deep learning is often arbitrary -- conventionally fixed to values such as 42, a number with no known feline endorsement. Aims. We propose that cats, as liminal beingโ€ฆ

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