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Convolutional Neural Nets, which is a powerful method of Deep Learning, is applied to classify equation of state of heavy-ion collision event generated within the UrQMD model. Event-by-event transverse momentum and azimuthal angle distributions of protons are used to train a classifier. An overall accuracy of classification of 98% is reached for Au+Au events at √sNN = 11 GeV. Performance of classifiers, trained on events at different colliding energies, is investigated. Obtained results indicate extensive possibilities of application of Deep Learning methods to other problems in physics of heavy-ion collisions.

We develop an optimization-based maximum likelihood approach to analyze the cross-correlation of the Cosmic Microwave Background (CMB) and large-scale structure induced by the kinetic Sunyaev-Zeldovich (kSZ) effect. Our main goal is to reconstruct the radial velocity field of the universe. While the existing quadratic estimator (QE) is statistically optimal for current and near-term experiments, the likelihood can extract more signal-to-noise in the future. Our likelihood formulation has further advantages over the QE, such as the possibility of jointly fitting cosmological and astrophysical parameters and the possibility of unifying several different kSZ analyses. We implement an auto-differentiable likelihood pipeline in JAX, which is computationally tractable for a realistic survey size and resolution, and evaluate it on the Agora simulation. We also implement a machine learning-based estimate of the electron density given an observed galaxy distribution, which can increase the signal-to-noise for both the QE and the likelihood method.

We develop a hybrid GNN-CNN architecture for the reconstruction of 3-dimensional continuous cosmological matter fields from discrete point clouds, provided by observed galaxy catalogs. Using the CAMELS hydrodynamical cosmological simulations we demonstrate that the proposed architecture allows for an accurate reconstruction of both the dark matter and electron density given observed galaxies and their features. Our approach includes a learned grid assignment scheme that improves

We perform kinetic Sunyaev-Zel’dovich (kSZ) velocity reconstruction on data from ACT DR6 and DESILS DR9. To estimate the cross-power between kSZ velocity reconstruction and galaxy density, we make use of a novel quadratic maximum likelihood QML power spectrum estimator implementation in red-shift binned spherical coordinates. We find a detection of the kSZ signal from the cross-correlation between the estimated velocity field and the large-scale galaxy field of 11.7σ. We estimate an amplitude A = 0.39±0.04 of the kSZ signal with respect to a halo model prediction, possibly indicating a high feedback in massive halos, in agreement with previous studies. Our result demonstrates the feasibility of an optimal QML pipeline at the resolution required for this analysis, and will be a powerful tool for kSZ cosmology with upcoming high-resolution surveys.

Large language models (LLMs) have shown strong capabilities in complex reasoning, and test-time scaling techniques can enhance their performance with comparably low cost. Many of these methods have been developed and evaluated on mathematical reasoning benchmarks such as AIME. This paper investigates whether the lessons learned from these benchmarks generalize to the domain of advanced theoretical physics. We evaluate a range of common test-time scaling methods on the TPBench physics dataset and compare their effectiveness with results on AIME. To better leverage the structure of physics problems, we develop a novel, symbolic weak-verifier framework to improve parallel scaling results. Our empirical results demonstrate that this method significantly outperforms existing test-time scaling approaches on TPBench. We also evaluate our method on AIME, confirming its effectiveness in solving advanced mathematical problems. Our findings highlight the power of step-wise symbolic verification for tackling complex scientific problems.

For many analyses in cosmology it is necessary to reconstruct the likely distribution of unobserved fields, such as dark matter or baryons, from observed luminous tracers. The dominant approach in cosmology has been to use the so-called halo model, which assumes radially symmetric profiles centered around luminous tracers such as galaxies. More recently, field-level machine learning methods have been proposed that can learn to estimate the unobserved field after being trained on simulations. However, it is unclear whether machine learning methods indeed significantly improve over linear methods or the halo model. In this paper we make a systematic comparison of different approaches to reconstruct dark matter and baryons from galaxy data using the CAMELS simulations. We find the best results using a combined GNN-CNN approach. We also provide a general analysis and visualization of the relationship of matter, baryons, halos and galaxies in these simulations to interpret our results.

It was recently shown that neural networks can be combined with the analytic method of scaledependent bias to obtain a measurement of local primordial non-Gaussianity, which is optimal in the squeezed limit that dominates the signal-to-noise. The method is robust to non-linear physics, but also inherits the statistical precision offered by neural networks applied to very non-linear scales. In prior work, we assumed that the neural network has access to the full matter distribution. In this work, we apply our method to halos. We first describe a novel two-field formalism that is optimal even when the matter distribution is not observed. We show that any N halo fields can be compressed to two fields without losing information, and obtain optimal loss functions to learn these fields. We then apply the method to high-resolution AbacusSummit and AbacusPNG simulations. In the present work, the two neural networks observe the local population statistics, in particular the halo mass and concentration distribution in a patch of the sky. While the traditional mass-binned halo analysis is optimal in practice without further halo properties on AbacusPNG, our novel formalism easily allows to include additional halo properties such as the halo concentration, which can improve fNL constraints by a factor of a few. We also explore whether shot noise can be lowered with machine learning compared to a traditional reconstruction, finding no improvement for our simulation parameters.

We introduce a benchmark to evaluate the capability of AI to solve problems in theoretical physics (TP), focusing on high-energy theory and cosmology. The first iteration of our benchmark consists of 57 problems of varying difficulty, from undergraduate to research level. These problems are novel in the sense that they do not come from public problem collections. We evaluate our data set on various open and closed language models, including o3-mini, o1, DeepSeek-R1, GPT-4o and versions of Llama and Qwen. While we find impressive progress in model performance with the most recent models, our research-level difficulty problems are mostly unsolved. We address challenges of auto-verifiability and grading, and discuss common failure modes. While currently state-of-the art models are still of limited use for researchers, our results show that AI assisted TP research may become possible in the near future. We discuss the main obstacles towards this goal and possible strategies to overcome them. The public problems and solutions, results for various models, and updates to the data set and score distribution, are available on the website of the dataset tpbench.org.

We present a novel implementation for the quadratic maximum likelihood (QML) power spectrum estimator for multiple correlated scalar fields on the sphere. Our estimator supports arbitrary binning in redshift and multipoles ℓ and includes cross-correlations of redshift bins. It implements a fully optimal analysis with a pixel-wise covariance model. We implement a number of optimizations which make the estimator and associated covariance matrix computationally tractable for a low-ℓ analysis, suitable for example for kSZ velocity reconstruction or primordial non-Gaussianity from scale-dependent bias analyses. We validate our estimator extensively on simulations and compare its features and precision with the common pseudo-Cℓ method, showing significant gains at large scales. We make our code publicly available. In a companion paper, we apply the estimator to kSZ velocity reconstruction using data from ACT and DESI Legacy Survey and construct full set of QML estimators on 40 correlated fields up to N_side= 32 in timescale of an hour on a single 24-core CPU requiring <256 Gb RAM, demonstrating the performance of the code.