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Axions and axion-like particles (ALPs) remain highly motivated extensions to the standard model due to their ability to address open questions such as the relic abundance of dark matter and the strong CP problem. Axions are also capable of undergoing a resonant mixing with photons when the masses of the two fields are roughly equal, producing a wide array of phenomenological consequences. Here, we revisit constraints coming from conversions of the cosmic microwave background (CMB) into axions, which will induce a distortion to the frequency spectrum of the background photons. We introduce a more detailed description for the modeling of the plasma mass of the photon, showcasing how the inclusion of Helium recombination can alter the conversion probability for photons in the Wien tail. Our results include an updated analytic framework, which allows us to define the precise spectral shape of the axion distortion, as well as a numeric component which utilized the code \texttt{CosmoTherm} to fully characterize the distortion, providing a slight increase in the constraining power over the analytics. We also treat for the first time the large-distortion regime for resonant axion-photon conversions. Under the assumption of large-scale primordial magnetic fields near the limit obtained from CMB observations, we find that spectral distortions can probe previously unexplored regions of the axion parameter space., Comment: 18 pages, 14 figures, comments welcome!

We provide a unified framework in which the three emotions at the heart of narrative tension (curiosity, suspense and surprise) are formalized. This framework is built on nonmonotonic reasoning which allows us to compactly represent the default behavior of the world and to simulate the affective evolution of an agent receiving a story. After formalizing the notions of awareness, curiosity, surprise and suspense, we explore the properties induced by our definitions and study the computational complexity of detecting them. We finally propose means to evaluate these emotions' intensity for a given agent listening to a story.

Accurate forest canopy height estimation is essential for evaluating aboveground biomass and carbon stock dynamics, supporting ecosystem monitoring services like timber provisioning, climate change mitigation, and biodiversity conservation. However, despite advancements in spaceborne LiDAR technology, data for northern high latitudes remain limited due to orbital and sampling constraints. This study introduces a methodology for generating spatially continuous, high-resolution canopy height and uncertainty estimates using Deep Learning Regression models. We integrate multi-source, multi-seasonal satellite data from Sentinel-1, Landsat, and ALOS-PALSAR-2, with spaceborne GEDI LiDAR as reference data. Our approach was tested in Ontario, Canada, and validated with airborne LiDAR, demonstrating strong performance. The best results were achieved by incorporating seasonal Sentinel-1 and Landsat features alongside PALSAR data, yielding an R-square of 0.72, RMSE of 3.43 m, and bias of 2.44 m. Using seasonal data instead of summer-only data improved variability by 10%, reduced error by 0.45 m, and decreased bias by 1 m. The deep learning model's weighting strategy notably reduced errors in tall canopy height estimates compared to a recent global model, though it overestimated lower canopy heights. Uncertainty maps highlighted greater uncertainty near forest edges, where GEDI measurements are prone to errors and SAR data may encounter backscatter issues like foreshortening, layover, and shadow. This study enhances canopy height estimation techniques in areas lacking spaceborne LiDAR coverage, providing essential tools for forestry, environmental monitoring, and carbon stock estimation.

A machine-learnable variational scheme using Gaussian radial basis functions (GRBFs) is presented and used to approximate linear problems on bounded and unbounded domains. In contrast to standard mesh-free methods, which use GRBFs to discretize strong-form differential equations, this work exploits the relationship between integrals of GRBFs, their derivatives, and polynomial moments to produce exact quadrature formulae which enable weak-form expressions. Combined with trainable GRBF means and covariances, this leads to a flexible, generalized Galerkin variational framework which is applied in the infinite-domain setting where the scheme is conforming, as well as the bounded-domain setting where it is not. Error rates for the proposed GRBF scheme are derived in each case, and examples are presented demonstrating utility of this approach as a surrogate modeling technique.

Spectral distortions of the cosmic microwave background (CMB) provide stringent constraints on energy and entropy production in the post-BBN (Big Bang Nucleosynthesis) era. This has been used to constrain dark photon models with COBE/FIRAS and forecast the potential gains with future CMB spectrometers. Here, we revisit these constraints by carefully considering the photon to dark photon conversion process and evolution of the distortion signal. Previous works only included the effect of CMB energy density changes but neglected the change to the photon number density. We clearly define the dark photon distortion signal and show that in contrast to previous analytic estimates the distortion has an opposite sign and a $\simeq 1.5$ times larger amplitude. We furthermore extend the treatment into the large distortion regime to also cover the redshift range $\simeq 2\times 10^6-4\times 10^7$ between the $\mu$-era and the end of BBN using CosmoTherm. This shows that the CMB distortion constraints for dark photon masses in the range $10^{-4}\,{\rm eV}\lesssim m_{\rm dp}\lesssim 10^{-3}\,{\rm eV}$ were significantly underestimated. We demonstrate that in the small distortion regime the distortion caused by photon to dark photon conversion is extremely close to a $\mu$-type distortion independent of the conversion redshift. This opens the possibility to study dark photon models using CMB distortion anisotropies and the correlations with CMB temperature anisotropies as we highlight here., Comment: 13 pages, 10+2 figures, to be submitted to MNRAS, comments welcome

The connection between galaxies and their host dark matter (DM) halos is critical to our understanding of cosmology, galaxy formation, and DM physics. To maximize the return of upcoming cosmological surveys, we need an accurate way to model this complex relationship. Many techniques have been developed to model this connection, from Halo Occupation Distribution (HOD) to empirical and semi-analytic models to hydrodynamic. Hydrodynamic simulations can incorporate more detailed astrophysical processes but are computationally expensive; HODs, on the other hand, are computationally cheap but have limited accuracy. In this work, we present NeHOD, a generative framework based on variational diffusion model and Transformer, for painting galaxies/subhalos on top of DM with an accuracy of hydrodynamic simulations but at a computational cost similar to HOD. By modeling galaxies/subhalos as point clouds, instead of binning or voxelization, we can resolve small spatial scales down to the resolution of the simulations. For each halo, NeHOD predicts the positions, velocities, masses, and concentrations of its central and satellite galaxies. We train NeHOD on the TNG-Warm DM suite of the DREAMS project, which consists of 1024 high-resolution zoom-in hydrodynamic simulations of Milky Way-mass halos with varying warm DM mass and astrophysical parameters. We show that our model captures the complex relationships between subhalo properties as a function of the simulation parameters, including the mass functions, stellar-halo mass relations, concentration-mass relations, and spatial clustering. Our method can be used for a large variety of downstream applications, from galaxy clustering to strong lensing studies., Comment: Submitted to ApJ; 30 + 6 pages; 11 + 4 figures; Comments welcomed

History eXplanation based on Predicates (HXP), studies the behavior of a Reinforcement Learning (RL) agent in a sequence of agent's interactions with the environment (a history), through the prism of an arbitrary predicate. To this end, an action importance score is computed for each action in the history. The explanation consists in displaying the most important actions to the user. As the calculation of an action's importance is #W[1]-hard, it is necessary for long histories to approximate the scores, at the expense of their quality. We therefore propose a new HXP method, called Backward-HXP, to provide explanations for these histories without having to approximate scores. Experiments show the ability of B-HXP to summarise long histories.

The segmentation of ultra-high resolution images poses challenges such as loss of spatial information or computational inefficiency. In this work, a novel approach that combines encoder-decoder architectures with domain decomposition strategies to address these challenges is proposed. Specifically, a domain decomposition-based U-Net (DDU-Net) architecture is introduced, which partitions input images into non-overlapping patches that can be processed independently on separate devices. A communication network is added to facilitate inter-patch information exchange to enhance the understanding of spatial context. Experimental validation is performed on a synthetic dataset that is designed to measure the effectiveness of the communication network. Then, the performance is tested on the DeepGlobe land cover classification dataset as a real-world benchmark data set. The results demonstrate that the approach, which includes inter-patch communication for images divided into $16\times16$ non-overlapping subimages, achieves a $2-3\,\%$ higher intersection over union (IoU) score compared to the same network without inter-patch communication. The performance of the network which includes communication is equivalent to that of a baseline U-Net trained on the full image, showing that our model provides an effective solution for segmenting ultra-high-resolution images while preserving spatial context. The code is available at https://github.com/corne00/HiRes-Seg-CNN.

This is the report from the 2023 Particle Physics Project Prioritization Panel (P5) approved by High Energy Physics Advisory Panel (HEPAP) on December 8, 2023. The final version was made public on May 8, 2024 and submitted to DOE SC and NSF MPS., Comment: 2-page spread version. The online version is available at https://www.usparticlephysics.org/2023-p5-report/ and the graphics at https://usparticlephysics.org/media-assets-library

Reasoning about unpredicted change consists in explaining observations by events; we propose here an approach for explaining time-stamped observations by surprises, which are simple events consisting in the change of the truth value of a fluent. A framework for dealing with surprises is defined. Minimal sets of surprises are provided together with time intervals where each surprise has occurred, and they are characterized from a model-based diagnosis point of view. Then, a probabilistic approach of surprise minimisation is proposed.

The purpose of this book is to provide an overview of AI research, ranging from basic work to interfaces and applications, with as much emphasis on results as on current issues. It is aimed at an audience of master students and Ph.D. students, and can be of interest as well for researchers and engineers who want to know more about AI. The book is split into three volumes.

The frequency spectrum of the cosmic microwave background (CMB) is a relatively untapped source of data which can allow us to peer beyond the surface of last scattering. Small deviations away from a perfect blackbody shape will encode valuable information about the state of the primordial Universe which may not be accessible by other means. Here, we briefly review some key science goals of CMB spectral distortions, with an emphasis on how future generations of experiments can be used in tandem with complementary observational probes to perform model discrimination of exotic physics scenarios. We focus here on synergies between spectral distortions, gravitational waves, and 21cm cosmology., Comment: 8 pages, contribution to the 2024 Cosmology session of the 58th Rencontres de Moriond

We develop multigrid-in-time preconditioners for Karush-Kuhn-Tucker (KKT) systems that arise in the solution of time-dependent optimization problems. We focus on a specific instance of KKT systems, known as augmented systems, which underpin the composite-step sequential quadratic programming framework [1]. To enable time-domain decomposition, our approach introduces virtual state variables and continuity constraints at each discrete time interval. The virtual state variables not only facilitate a decoupling in time but also give rise to fixed-point iterations that aid the solution of KKT systems. These fixed-point schemes can be used either as preconditioners for Krylov subspace methods or as smoothers for multigrid-in-time schemes. For the latter, we develop a block-Jacobi scheme that parallelizes trivially in the time domain. To complete the multigrid construction, we use simple prolongation and restriction operators based on geometric multigrid ideas, and a coarse-grid solver based on a GMRES iteration preconditioned with the symmetric block Gauss-Seidel scheme. We present two optimal control examples, involving the viscous Burgers' and van der Pol oscillator equations, respectively, and demonstrate algorithmic scalability.

Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. Powered by deep reinforcement learning, LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small molecules that inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing constraints on synthesizability and drug-likeliness. LambdaZero provides an exponential speedup in terms of the number of calls to the expensive molecular docking oracle, and LambdaZero de novo designed molecules reach docking scores that would otherwise require the virtual screening of a hundred billion molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH. In in vitro experimental validation, a series of ligands from a generated quinazoline-based scaffold were synthesized, and the lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide (UM0152893) displayed sub-micromolar enzyme inhibition of sEH.

We introduce the DREAMS project, an innovative approach to understanding the astrophysical implications of alternative dark matter models and their effects on galaxy formation and evolution. The DREAMS project will ultimately comprise thousands of cosmological hydrodynamic simulations that simultaneously vary over dark matter physics, astrophysics, and cosmology in modeling a range of systems -- from galaxy clusters to ultra-faint satellites. Such extensive simulation suites can provide adequate training sets for machine-learning-based analyses. This paper introduces two new cosmological hydrodynamical suites of Warm Dark Matter, each comprised of 1024 simulations generated using the Arepo code. One suite consists of uniform-box simulations covering a $(25~h^{-1}~{\rm M}_\odot)^3$ volume, while the other consists of Milky Way zoom-ins with sufficient resolution to capture the properties of classical satellites. For each simulation, the Warm Dark Matter particle mass is varied along with the initial density field and several parameters controlling the strength of baryonic feedback within the IllustrisTNG model. We provide two examples, separately utilizing emulators and Convolutional Neural Networks, to demonstrate how such simulation suites can be used to disentangle the effects of dark matter and baryonic physics on galactic properties. The DREAMS project can be extended further to include different dark matter models, galaxy formation physics, and astrophysical targets. In this way, it will provide an unparalleled opportunity to characterize uncertainties on predictions for small-scale observables, leading to robust predictions for testing the particle physics nature of dark matter on these scales., Comment: 28 pages, 8 figures, DREAMS website: https://www.dreams-project.org

The formation redshift and abundance of the first stars and galaxies is highly sensitive to the build up of low mass dark matter halos as well as astrophysical feedback effects which modulate star formation in these low mass halos. The 21-cm signal at cosmic dawn will depend strongly on the formation of these first luminous sources and thus can be used to constrain unknown astrophysical and dark matter properties in the early universe. In this paper, we explore how well we could measure properties of dark matter using the 21-cm power spectrum at $z>10$, given unconstrained astrophysical parameters. We create a generalizable form of the dark matter halo mass function for models with damped and/or oscillatory linear power spectra, finding a single "smooth-k" window function which describes a broad range of models including CDM. We use this to make forecasts for structure formation using the Effective Theory of Structure Formation (ETHOS) framework to explore a broad parameter space of dark matter models. We make predictions for the 21-cm power spectrum observed by HERA varying both cosmological ETHOS parameters as well as astrophysical parameters. Using a Markov Chain Monte Carlo forecast we find that the ETHOS dark matter parameters are degenerate with astrophysical parameters linked to star formation in low mass dark matter halos but not with X-ray heating produced by the first generation of stars. After marginalizing over uncertainties in astrophysical parameters we demonstrate that with just 540 days of HERA observations it should be possible to distinguish between CDM and a broad range of dark matter models with suppression at wavenumbers $k\lesssim 200\,h$Mpc$^{-1}$ assuming a moderate noise level. These results demonstrate the potential of 21-cm observations to constrain the matter power spectrum on scales smaller than current probes., Comment: 13 pages, 9 figures, submitted to PRD

Motivated by Furstenberg's Theorem on sets in the circle invariant under multiplication by a non-lacunary semigroup, we define a general class of dynamical systems possessing similar topological dynamical properties. We call such systems chaotic almost minimal, reflecting that these systems are chaotic, but in some sense are close to minimal. We study properties of the acting group needed to admit such an action, and show the existence of a chaotic almost minimal $\mathbb{Z}$-action. We show there exists chaotic almost minimal $\mathbb{Z}^{d}$-actions which support multiple distinct nonatomic ergodic probability measures., Comment: 37 pages, new version correcting some wording and including new references

The presence of an abundant population of low frequency photons at high redshifts (such as a radio background) can source leading order effects on the evolution of the matter and spin temperatures through rapid free-free absorptions. This effect, known as soft photon heating, can have a dramatic impact on the differential brightness temperature, $\Delta T_{\rm b}$, a central observable in $21$cm cosmology. Here, we introduce a semi-analytic framework to describe the dynamics of soft photon heating, providing a simplified set of evolution equations and a useful numerical scheme which can be used to study this generic effect. We also perform quasi-instantaneous and continuous soft photon injections to elucidate the different regimes in which soft photon heating is expected to impart a significant contribution to the global $21$cm signal and its fluctuations. We find that soft photon backgrounds produced after recombination with spectral index $\gamma > 3.0$ undergo significant free-free absorption, and therefore this heating effect cannot be neglected. The effect becomes stronger with steeper spectral index, and in some cases the injection of a synchrotron-like spectrum ($\gamma = 3.6$) can suppress the amplitude of $\Delta T_{\rm b}$ relative to the standard model prediction, making the global $21$cm signal even more difficult to detect in these scenarios., Comment: 19 pages, 10 figures, comments welcome! The accompanying soft photon heating code can be found at https://github.com/Bryce-Cyr/Soft_Photon_Heating

Cosmological observables are particularly sensitive to key ratios of energy densities and rates, both today and at earlier epochs of the Universe. Well-known examples include the photon-to-baryon and the matter-to-radiation ratios. Equally important, though less publicized, are the ratios of pressure-supported to pressureless matter and the Thomson scattering rate to the Hubble rate around recombination, both of which observations tightly constrain. Preserving these key ratios in theories beyond the $\Lambda$ Cold-Dark-Matter ($\Lambda$CDM) model ensures broad concordance with a large swath of datasets when addressing cosmological tensions. We demonstrate that a mirror dark sector, reflecting a partial $\mathbb{Z}_2$ symmetry with the Standard Model, in conjunction with percent level changes to the visible fine-structure constant and electron mass which represent a \textit{phenomenological} change to the Thomson scattering rate, maintains essential cosmological ratios. Incorporating this ratio preserving approach into a cosmological framework significantly improves agreement to observational data ($\Delta\chi^2=-35.72$) and completely eliminates the Hubble tension with a cosmologically inferred $H_0 = 73.80 \pm 1.02$ km/s/Mpc when including the S$H_0$ES calibration in our analysis. While our approach is certainly nonminimal, it emphasizes the importance of keeping key ratios constant when exploring models beyond $\Lambda$CDM., Comment: 12 pages, 6 figures, comments welcome! Published in PR.D

Some cosmic microwave background (CMB) data allow a cosmological scenario in which the free streaming of neutrinos is delayed until close to matter-radiation equality. Interestingly, recent analyses have revealed that large-scale structure (LSS) data also align with this scenario, discarding the possibility of an accidental feature in the CMB sky and calling for further investigation into the free-streaming nature of neutrinos. By assuming a simple representation of self-interacting neutrinos, we investigate whether this nonstandard scenario can accommodate a consistent cosmology for both the CMB power spectra and the large-scale distribution of galaxies simultaneously. Employing three different approaches - a profile likelihood exploration, a nested sampling method, and a heuristic Metropolis-Hasting approximation - we exhaustively explore the parameter space and demonstrate that galaxy data exacerbates the challenge already posed by the Planck polarization data for this nonstandard scenario. We find that the Bayes factor disfavor strong interactions among neutrinos over the $\Lambda$CDM and $\Lambda$CDM + $N_\mathrm{eff}$ + $\sum m_\nu$ models with odds of $7:10000$ and $23:1000$, respectively, providing large evidence against the simple self-interacting neutrino model. Our analysis emphasizes the need to consider a broader range of phenomenologies in the early Universe. We also highlight significant numerical and theoretical challenges ahead in uncovering the exact nature of the feature observed in the data or, ultimately, confirming the standard chronological evolution of the Universe., Comment: 17 pages + references, 9 figures, 5 tables, v2 reflects version accepted for publication in PRD. arXiv admin note: text overlap with arXiv:2309.03941