Atomic Physics
Ambiguity-Free Inertial Measurement with Multi-Wavelength Atom Interferometry
Wei-Chen Jia, Yue Xin, Ke Shen et al. (+1 more)
White-light interferometry enables ambiguity-free localization by synthesizing interference envelopes from multiple optical wavelengths, but no analogous capability has been realized for coherent matter waves. Here we report the first experimental demonstration of multi-wavelength atom interferometry, establishing the matter-wave counterpart of white-light interferometry. By exploiting counter-propagating atomic beams as multi-wavelength matter wave sources and synthesizing interference envelopes from their spectral components, we realize inertial measurements based on envelope localization rather than conventional fringe-phase estimation. The resulting multi-scale interferometric response provides ambiguity-free operation, a well-defined rotational scale factor, and reduced sensitivity to initial phase bias. As a proof of principle, we demonstrate simultaneous dual-axis rotation and acceleration sensing and directly resolve the phase ambiguity that fundamentally limits conventional open-loop atom interferometers. We further measure the Earth's rotation with a relative error of 4.3% and a long-term stability of 93 ppm at an averaging time of 15,000 s. Our results establish multi-wavelength atom interferometry as a new paradigm for coherent matter-wave sensing, extending the principles of white-light interferometry to atom optics and opening new opportunities for inertial sensing, geodesy, precision metrology, and inertial
navigation.
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All authors Wei-Chen Jia, Yue Xin, Ke Shen, Yan-Ying Feng
Optics
Tailoring soft cavities for robust molecular strong coupling
Siddharaj M. Gadge and Adarsh B. Vasista
How should one design efficient chemically open optical cavities for molecular strong coupling? Addressing this question is important for the development of soft-cavity platforms for dynamically tunable light–matter interactions, where direct access to confined electromagnetic modes is essential. Conventional cavity figures of merit such as $Q/\sqrt{V}$ and cooperativity successfully describe spectral confinement and dissipation but do not fully capture the role of linewidth asymmetry between cavity and molecular degrees of freedom. Here, we systematically investigate strong coupling between TDBC dye molecules and whispering gallery modes of polystyrene microspheres by varying the microsphere radius over a broad range. To quantify the robustness of strong coupling, we define the parameter $\chi = \frac{g}{\max(\kappa,\gamma)}$, where $g$ is the coupling strength, while $\kappa$ and $\gamma$ denote the cavity and molecular linewidths, respectively. Although the coupling strength decreases monotonically with increasing cavity size due to mode-volume scaling, we find that $\chi$ exhibits a pronounced maximum near the condition $\kappa \approx \gamma$. This observation suggests that linewidth matching is not merely a criterion for improved spectral visibility, but reflects a dissipation-matching condition that optimizes the robustness of coherent light–matter exchange in soft-cavities. Our results provide an alternative framework for designing morphology-dependent cavities for molecular strong coupling.
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Applied Physics · Cross-listed from cond-mat.mtrl-sci
Integrated magnonic neural circuits based on nonlinear wave neurons
Mengying Guo, Xudong Jing, Kristýna Davidkova et al. (+11 more)
Artificial intelligence is driving intense interest in alternative computing hardware capable of neural information processing beyond conventional charge-based electronics. Among emerging approaches, wave-based computing promises highly parallel and energy-efficient operation, but scalable physical neural hardware has remained elusive because wave systems generally lack cascadable nonlinear
neurons with signal regeneration and phase-robust operation. Here we demonstrate integrated magnonic neural
circuits based on nonlinear threshold
neurons realized in nanoscale yttrium iron garnet waveguides. The
neurons perform weighted summation of multiple
spin-wave inputs, while a pump-controlled nonlinear activation defines continuously tunable firing thresholds. Owing to deeply nonlinear
spin-wave dynamics, the activated
neurons emit self-normalized outputs whose intensities are largely independent of the input amplitudes, while nonlinear phase self-adjustment suppresses sensitivity to the relative input phases, enabling deterministic
neuron-to-
neuron cascading without external signal restoration. We experimentally realize programmable threshold
neurons, reconfigurable weighted classification and deterministic cascading between sequential neuronal stages, and further demonstrate reconfigurable physical pattern recognition in a seven-
neuron integrated magnonic
circuit through experimental classification of the binary letter patterns 'HUST'. These results establish nonlinear
magnons as a scalable platform for integrated neural hardware and position nonlinear wave dynamics as a general paradigm for physical neuromorphic computing.
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All authors Mengying Guo, Xudong Jing, Kristýna Davidkova, Roman Verba, Zhenyu Zhou, Xueyu Guo, Carsten Dubs, Chuan Gao, Yiheng Rao, Kaiming Cai, Jing Li, Philipp Pirro, Andrii V. Chumak, Qi Wang
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Observation of coherently modulated thermal phonon band and lifetime in superlattice
Yuxuan Liao, Hiroshi Uchiyama, Naomi Nagai et al. (+12 more)
Manipulating
phonon band structures through wave interference in artificial periodic nanostructures is of fundamental interest and forms the foundation of
phonon band engineering. Yet, direct observation of thermal
phonon wave interference has remained elusive despite half a century of extensive research. Here, utilizing inelastic x-ray scattering, we directly observed momentum-resolved coherently modulated thermal
phonon bands and lifetimes in a short-period GaAs/AlAs superlattice at 300 K. The modulation persists at even 500 K, demonstrating robust coherent thermal
phonon transport at high temperatures. Our measurements, combined with ab initio
lattice-dynamics calculations, further show that thermal
phonon interference expands the three-
phonon scattering phase space and strengthens anharmonic effects, highlighting the interplay between
phonon band modulation and anharmonicity. Our observations establish nanostructuring as a versatile route for engineering thermal
phonon bands and lifetimes at high temperatures across diverse applications.
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All authors Yuxuan Liao, Hiroshi Uchiyama, Naomi Nagai, Natalia Morais, Taiushun Manjo, Rulei Guo, Harsh Chandra, Ryohei Nagahiro, Bin Xu, Hiroshi Fukui, Daisuke Ishikawa, Alfred Q.R. Baron, Yasuhiko Arakawa, Kazuhiko Hirakawa, Junichiro Shiomi
Fluid Dynamics · Cross-listed from cs.LG
Spectrally Regularized Latent Flow Matching for Turbulence Generation
Khalid Rafiq and Aditya G. Nair
Latent
diffusion and flow matching have emerged as leading approaches for synthetic
turbulence generation, yet they systematically under-represent dissipation-range amplitudes. We introduce a latent flow matching framework with a spectrally regularized compression stage that directly targets this failure mode. On a 256^2 DNS
dataset at Re_f \approx 2250, replacing an MSE-trained VAE with a zone-weighted log-spectral objective raises deep-dissipation retained spectral power from 25% to 94% in reconstruction and from 20% to 79% in unconditional generation. The improved latent representation also yields a substantially better sampling cost-fidelity tradeoff: the MSE-trained latent space imposes a fundamental quality ceiling near DD bias -0.70 that no integrator or step-count can overcome, while the spectrally regularized latent space reaches DD bias -0.117 at just 20 function evaluations. Mechanistically, encoder-decoder swap experiments show that the improvement is driven primarily by encoder-induced latent reorganization rather than decoder capacity, while a support-amplitude decomposition reveals that MSE-trained models behave as conservative suppression models, minimizing pointwise error by attenuating intermittent high-wavenumber structure. Both pipelines recover the second-order structure function and the correct sign of S_3, indicating the correct cascade direction without explicit supervision. A small residual gap in the magnitude of S_3 suggests that phase-coherent triadic organization remains a complementary axis to amplitude fidelity for future generative
turbulence models.
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Computational Physics · Cross-listed from cs.CE
Neural-Parameterized Cellular Automata for Wildfire Spread
Maksym Zhenirovskyy, Ion Matei, Rohit Vuppala et al. (+2 more)
Traditional wildfire models rely on rigid, low-dimensional parameters and static fuel maps, frequently underpredicting fire spread. To address this weakness, we introduce a hybrid deep-learning parameterized Probabilistic Cellular Automata (CA) framework implemented in JAX. Our approach employs a Multi-Scale Convolutional Neural Network to dynamically generate spatially varying parameters that govern fire-spread probability, wind
alignment, and slope influence. This hybrid design captures complex, nonlinear environmental interactions while preserving the physical interpretability of the underlying three-state CA. The JAX implementation enables hardware acceleration and gradient-based parameter calibration. Evaluated on six large-scale wildfires in the western United States, the model maintains IoU > 0.6 over 72-hour forecast horizons after a 10-day data assimilation window during which the model is fitted incrementally to observed perimeters; the resulting forecast is a conditional projection of fire growth under the suppression regime already ncoded in those observations.
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All authors Maksym Zhenirovskyy, Ion Matei, Rohit Vuppala, Takuya Kurihana, Hon Yung Wonga
Fluid Dynamics
Modelling Wave Attenuation by Mangroves: Effects of Root Morphology and Sea-Level Rise
Khang Ee Pang and Zhi Yung Tay
Mangroves are increasingly promoted as nature-based solutions for coastal protection, yet many existing models neglect the vertical variation of vegetation biomass, leading to oversimplified representations of root-flow interactions. In this study, we introduce a generalised parametrisation of the mangrove vegetation profile and derive a wave attenuation model that explicitly accounts for the mangrove root characteristics. The model reveals that wave attenuation is species specific and exhibits a non-monotonic dependence on wave frequency. We further quantify the sensitivity of wave attenuation efficiency to sea-level rise and show that rising sea levels do not necessarily reduce attenuation capacity. In particular, when the water depth is less than approximately 1.6 times the mean root height, wave attenuation may instead be enhanced under sea-level rise. Future changes in attenuation efficiency under different
climate scenarios are also quantified. OpenFOAM simulations are further used to calibrate drag coefficients as a function of the root submergence ratio. These results highlight the critical role of vertical root structure in governing wave attenuation and provide a framework for assessing the long-term resilience of mangrove-based coastal protection under
climate change.
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Computational Physics · Cross-listed from cs.LG
Structure-Preserving Neural Surrogates with Tractable Uncertainty Quantification
Handi Zhang, Adrienne M. Propp, Brooks Kinch et al. (+2 more)
Recent advances in scientific machine learning provide a means of near-real-time solution to partial differential equations (PDEs), but lack the theoretical underpinnings of conventional simulators that support contemporary verification and validation. In this work, we construct data-driven reduced-order models that serve as structure-preserving, real-time surrogates. Remarkably, the exterior calculus that imposes physical conservation structure also exposes topological structure that we use to build a Gaussian process (GP) representation of uncertainty in state-flux relationships, ultimately yielding a Dirichlet-to-Neumann map for quantities of interest with closed-form expressions for posterior uncertainty. We specifically propose structure-preserving $H(\mathrm{div})$–$L^2$ subspaces of conventional Raviart–Thomas and $dgP_0$ elements prescribed by a lightweight
transformer. Reduced-order dynamics consistent with this subspace are learned by posing a conservation law in which a GP describes the fluxes between volumes. This work hinges on a novel interface between mixed FEM spaces and GP regression; when training is posed as the optimal recovery problem (ORP), the resulting GP regression can be written as an optimization problem with equality constraints that impose a conservation structure, amenable to a fast Schur-complement training strategy. The trained model can then be solved in real time with closed-form
estimators for boundary fluxes driven by prescribed Dirichlet data. The paper includes RKHS posterior error bounds for linear functionals to support uncertainty quantification, as well as numerical experiments demonstrating the accuracy of the posterior distribution as a surrogate for error estimation.
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All authors Handi Zhang, Adrienne M. Propp, Brooks Kinch, Houman Owhadi, Nathaniel Trask
Optics
Above-bulk DC Kerr electro-optics at the water/ITO interface, resolved with the Pockels effect
Soichiro Ashikaga, Kazuaki Nakata, Akihiro Okada et al. (+4 more)
On a charged interface, broken inversion
symmetry permits a large field-linear Pockels response through $\chi^{(2)}(\omega;\omega,0)$; at the water/ITO interface $|r_{13}|$ has been reported to reach the $10^{2}\,\mathrm{pm/V}$ order. The coexisting third-order DC Kerr term $\chi^{(3)}(\omega;\omega,0,0)$ – small in bulk water ($|\chi^{(3)}_{\mathrm{bulk}}|\sim5.5\times10^{-21}\,\mathrm{m^2/V^2}$) – had not been jointly parameterized with the Pockels term along the fundamental-frequency ($\omega$) electro-optic path. Superimposing an AC modulation and a DC bias in 0.1\,M NaCl mixes the $\chi^{(3)}$ contribution with the 1f response through the cross-term $2 s_{1133}\,E_{\mathrm{DC}}$, so that the water refractive-index modulation $\Delta n_{\mathrm{water}}$ varies linearly with $V_{\mathrm{WE}}$; a model-assisted linear fit then determines both terms from a single AC$+$DC sweep. At $V_{\mathrm{WE}}=0\,\mathrm{V}$ vs Ag/AgCl, $|r_{13}|=(1.18 \pm 0.06_{\mathrm{PZC}})\times10^{2}\,\mathrm{pm/V}$ and, under $\eta_{\mathrm{DC}}=1$, the thickness-normalized DC Kerr coefficient $|s_{1133}/d_{\mathrm{EDL}}|=33.0 \pm 5.6\,\mathrm{pm/V^{2}}$. Across physically reasonable $d_{\mathrm{EDL}}$ ($0.6$–$1.6\,\mathrm{nm}$), the interfacial DC Kerr susceptibility reaches $|\chi^{(3),\mathrm{int}}_{1133}| \approx (2\text{--}5.5)\times10^{-20}\,\mathrm{m^2/V^2}$, several-fold above the visible-range bulk-water value. This response is a property of the specific interface, tunable through the choice of electrode, electrolyte, and solvent rather than intrinsic to bulk water. Amid renewed interest in the Kerr response of water (including recent THz-band optical Kerr studies), the method directly probes this DC Kerr term along the $\omega$ path and complements SHG/SFG (the $2\omega$ path).
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All authors Soichiro Ashikaga, Kazuaki Nakata, Akihiro Okada, Takumi Takayanagi, Kyohei Yamashita, Takayoshi Kobayashi, Eiji Tokunaga
Optics
Impact of mode completeness on the accuracy of the coupling theory of quasinormal modes: a strict numerical demonstration
Can Tao, Junda Zhu and Haitao Liu
The coupling theory of quasinormal modes (QNMs) for a coupled system of generally lossy and dispersive optical nanoresonators has been established in a rigorous manner based on the first principle of Maxwell's equations [Phys. Rev. B 102, 045430 (2020)], and can achieve superior computational efficiency and physical intuitiveness compared with full-wave numerical methods if a small set of modes can achieve a high accuracy. The QNMs suffer from an exponential divergence of far field and can form a complete basis inside but not outside the resonator. In the QNM coupling theory (QCT), it is required that the QNMs of each resonator form a complete basis in expanding the scattered field both inside and outside the resonator, which can be achieved by using regularized QNMs (RQNMs). However, a strict numerical demonstration of the impact of the mode completeness of RQNMs on the accuracy of QCT by using a virtually complete basis of RQNMs is still absent. In this paper, we will provide such a numerical demonstration along with an improvement of the QCT and some theoretical demonstrations on a rigorous incorporation of RQNMs into the QCT. The RQNMs are obtained by introducing an equivalent surface current (ESC) encircling the resonator (called ESC-RQNMs) or the perfectly matched layer (PML) surrounding the computational domain (called PML-RQNMs). The numerical example is selected as two one-dimensional resonators of slabs in the extreme coupling case of direct contact, for which a virtually complete basis of RQNMs can be solved either analytically (for ESC-RQNMs) or numerically (for PML-RQNMs). The results show that by using a virtually complete basis of RQNMs, the QCT can achieve a high accuracy in predicting both the source-free eigenmodes and the source-excited scattered field of the coupled system, which is not true if using the incomplete basis of not-regularized QNMs (i.e., physical QNMs).
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Optics
From Rings to Top-Hat beams
M. A. Jácome-Silva, I. Julían-Macías, I. Ramos-Prieto et al. (+4 more)
We present the exact analytical paraxial propagation of structured light beams that transition from Ring annular profiles to top-hat intensity distributions. The initial field is defined as a superposition of a Gaussian-weighted power-law core and a singular inverse-quadratic modulation term, both carrying an azimuthal phase factor. By solving the Fresnel diffraction integral in cylindrical coordinates, we obtain exact closed-form expressions for the propagated field at arbitrary planes. The paraxial
evolution is shown to be governed by a Cauchy-Riemann beam term and an infinite series of modified Bessel functions of the second kind weighted by an azimuthal phase factor. This analytical framework demonstrates how tuning the source parameters enables a continuous transition from ring-dominated annular profiles to uniform top-hat beams. For the fundamental mode ($l=0$), the singular component fills the central intensity null, producing a flat transverse plateau.
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All authors M. A. Jácome-Silva, I. Julían-Macías, I. Ramos-Prieto, U. Ruíz-Corona, F. Soto-Eguibar, D. Sánchez-de-la-Llave, H. M. Moya-Cessa
Optics
Time-Reversal Characteristic Modes of Lossy Reciprocal Structures
Chenbo Shi, Jin Pan, Xin Gu et al. (+2 more)
A time-reversal characteristic-mode decomposition is developed for reciprocal lossy electromagnetic structures. The formulation is built on a transmit–receive interpretation of reciprocity: the far-field pattern radiated by a mode determines the time-reversed incident field that is optimally matched to couple energy back into that same mode. This physical picture leads to an antilinear characteristic-mode equation whose solutions remain radiation-power orthogonal even in the presence of material loss, lossy loading, or matched absorption. As a result, the modal expansion coefficients directly represent the radiated-power contributions of the corresponding modes and avoid the singular biorthogonal normalization that may arise in nonnormal classical characteristic-mode expansions. Equivalent formulations are derived in the scattering-operator, T-
matrix, and method-of-moments (MoM) frameworks, thereby connecting external wave-channel descriptions with current-space and port-excitation descriptions. The proposed modes reduce to classical characteristic modes in the lossless limit. Numerical examples involving a lossy two-sphere system and a loaded folded
antenna demonstrate the radiation-power orthogonality, modal-expansion stability, and power interpretability of the proposed decomposition near exceptional points, where classical characteristic-mode expansions become singular or lose their radiated-power meaning.
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All authors Chenbo Shi, Jin Pan, Xin Gu, Shichen Liang, Le Zuo
Atomic and Molecular Clusters · Cross-listed from quant-ph
Tensor-Network-Based Distributed Quantum Dynamics on Independent Quantum Computers
Anurag Dwivedi, Melissa C. Revelle, Daniel S. Lobser et al. (+6 more)
We present an approach based on tensor networks for distributed
quantum computing simulation of chemical wavepacket dynamics in a continuous variable representation. The central idea is that the tensor-network representation of the multidimensional time-
evolution operator naturally induces an elevated
Hilbert space where the dynamics decomposes into a set of independent lower-dimensional propagations. This transformation converts an entangled
quantum evolution into a set of parallel computational tasks that can be executed asynchronously across heterogeneous
quantum and classical computing architectures. The resulting formalism establishes a direct connection between tensor-network decompositions, uniformly controlled
quantum circuits, and asynchronous distributed
quantum computing. The approach is developed with a goal towards hybrid
quantum/classical implementation, and is appropriate for a general heterogeneous mixture of
quantum hardware systems. The experimental realization of the asynchronously distributed
quantum processes that arise from the tensor-network decomposition are carried out on the Sandia National Laboratories' trapped-ion
quantum computer, where the
circuits are compiled using native partial-entangling $XX(\theta)$ gates, reducing the expected two-
qubit gate infidelity by more than 30% relative to conventional fully entangling decompositions. We demonstrate the methodology by
quantum computing the vibrational spectra of a small protonated water cluster that shows critical
quantum nuclear behavior. Such water cluster systems have been found to be challenging for experimental action spectroscopy and for theory, and here, for the first time, we provide results for vibrational spectroscopy that are in agreement with the respective classical results to within 4cm$^{-1}$, thus allowing for the potential for spectroscopic accuracy from
quantum computations.
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All authors Anurag Dwivedi, Melissa C. Revelle, Daniel S. Lobser, Brian K. McFarland, Edward C. Tortorici, Christopher G. Yale, Susan M. Clark, Philip Richerme, Srinivasan S. Iyengar
Chemical Physics · Cross-listed from cs.LG
Range-Aware Bayesian Optimization for Discovering Diverse Designs within Target Property Windows
Shengli Jiang, Jason Wu, Charles M. Schroeder et al. (+1 more)
In many materials and product design problems, desirable candidates exhibit properties that fall within an acceptable range rather than achieve a single optimum. Recovering multiple, distinct solutions that satisfy such specifications is also practically valuable, as some candidates may be preferred for reasons of cost, processability, or robustness that are difficult to encode directly in an objective function. Here, we develop a range-aware
Bayesian optimization (BO) framework in which the acquisition function directly scores the posterior probability that a candidate satisfies a target range. The framework naturally extends to parallel pursuit of multiple distinct specifications over a shared candidate space. Across
benchmark tasks, range-aware acquisition consistently recovers larger and more diverse sets of valid designs than standard BO baselines and recent goal-seeking methods. Its utility is further demonstrated in two practically motivated design case studies involving optimizing reaction conditions for
polymer synthesis and sequence-defined oligomer discovery for prescribed optical absorption bands, supported by
quantum chemical calculations. These results suggest that range-aware BO can provide a practical and sample-efficient foundation for specification-driven design, particularly when design flexibility and solution diversity are important considerations.
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All authors Shengli Jiang, Jason Wu, Charles M. Schroeder, Michael A. Webb
Optics
Nonlinearity Reversal in Epsilon-Near-Zero Indium Tin Oxide Driven by Few-Cycle Light Pulse
Mustafa Goksu Ozlu, Colton Fruhling, Ian Hoffman et al. (+4 more)
Recent breakthrough studies of nonlinearities at extreme pump intensities ($\sim$1 $\text{TW/cm}^2$) in transparent conducting
oxides (TCOs) have rewritten our understanding of the dynamics in these materials. However, exploring TCO dynamics beyond these intensities is prohibited by the damage threshold of the material. In this work, we overcome this problem by using a few-cycle pump
laser pulse (sub-8\,fs) to maximize the intensity while keeping the optical fluence below the damage threshold. We observe a reversal in the optical response trend starting at optical pump
laser intensities of $\sim$5 $\text{TW/cm}^2$ similar to Segal et al. At the highest pump pulse intensities, we obtain a complete change in the sign of the modulation for both transmission and reflection, producing a full-cycle oscillation of the refractive index modulation within 300\,fs. The amplitude of the sign reversal scales quadratically with the intensity. We therefore propose a simple two-
photon absorption (TPA) model to explain the observed behaviour. The TPA, which is normally forbidden by the Pauli blocking, is enabled here by intraband excitations from the lower to the upper non-
equilibrium states of the conduction band (CB). Such excitations vacate the states at the bottom of the CB, lifting up the blocking and thus making interband TPA possible. The model is in good agreement with experimental results, capturing the essential trends in the observed data and revealing the dynamics of competing channels caused by the interplay between interband and intraband transitions. This intensity-controlled mechanism could be the key to unlocking new applications of TCOs for time-varying photonics such as photonic time
crystals.
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All authors Mustafa Goksu Ozlu, Colton Fruhling, Ian Hoffman, Jae Ik Choi, Marcello Ferrera, Alexandra Boltasseva, Vladimir M. Shalaev
Optics · Cross-listed from cond-mat.mes-hall
Polarization-Resolved Photon Statistics of Cavity Quantum Materials
Benjamin Kass, Spenser Talkington and Martin Claassen
By forming hybrid light-matter states, optical cavities offer a route for engineering material properties, however, unambiguously probing the effects of light-matter coupling remains difficult. Here, we show that the polarization-resolved statistics of
photons transmitted through a cavity, measurable via $g^{(2)}$, provide one such diagnostic. By relating $g^{(2)}$ to matter correlation functions such as the Raman structure factor, we link
photon bunching and antibunching to material properties. By applying this method to the stripy-to-
antiferromagnetic transition in the Kitaev-Heisenberg
spin model, we find that polarization-dependent patterns of bunching and antibunching encode the
magnetic point-group
symmetries of each phase and characterize the behavior at the phase boundary. Finally, we predict measuring $g^{(2)}$ for output
photon pairs polarized orthogonal to the input field will isolate higher-order light-matter scattering processes that probe higher-order material correlations.
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Atmospheric and Oceanic Physics
Urban Heat MiniCubes: An AI-Ready dataset for urban heat research
Jonathan Starfeldt, Maria J. Molina, Alexander Kerr et al. (+3 more)
Urban heat is amplified by impermeable surfaces and heterogeneous built environments, yet street-level variability remains difficult to quantify because multi-sensor observations are rarely available in consistent, analysis-ready form at the necessary spatiotemporal scales. We present "Urban Heat MiniCubes," a publicly available, FAIR-oriented
dataset designed for machine learning applications in urban heat research. The
dataset provides harmonized 90 x 90 km gridded data cubes for 48 cities in the Western Hemisphere spanning 2022-2023, with variables reprojected and collocated to a common grid to reduce preprocessing (e.g., reprojection, resampling, and spatiotemporal
alignment). Urban Heat MiniCubes includes two complementary modalities: (i) higher-spatial-resolution, lower-frequency observations from Landsat 8/9 (e.g., surface reflectances) and Sentinel-1 (e.g., synthetic aperture radar backscatter), and (ii) higher-temporal-frequency, coarser observations from GOES-R (e.g., longwave infrared brightness temperatures) and a microwave land surface temperature product. We document variables and metadata and provide technical assessment using inter-variable analyses and autoencoder-based reconstruction-error summaries across pixel classes (e.g., water and cloud). Potential use cases and limitations are also discussed.
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All authors Jonathan Starfeldt, Maria J. Molina, Alexander Kerr, Adam Yang, Thomas R.H. Holmes, Christopher R. Hain