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We study inflation in a recently proposed gravitational effective field theory describing the trace anomaly. The theory requires an additional scalar which is massless in the early universe. This scalar—referenced as an anomalyon—couples to the familiar matter and radiation through the gauge field trace anomaly. We derive a class of cosmological solutions that deviate from the standard inflationary ones only slightly, in spite of the fact that the anomalyon has a sizable time dependent background. On the other hand, the scalar cosmological perturbations in this theory are different from the conventional inflationary perturbations. The inflaton and anomalyon perturbations mix, and one of the diagonal combinations gives the standard nearly scale-invariant adiabatic spectrum, while the other combination has a blue power spectrum at short distance scales. We argue that this blue spectrum can lead to the formation of primordial black holes (PBHs) at distance scales much shorter than the ones tested in cosmic microwave background observations. The resulting PBHs can be heavy enough to survive to the present day Universe. For natural values of the parameters involved the PBHs would constitute only a tiny fraction of the dark matter, but with fine-tunings perhaps all of dark matter could be accounted by them. We also show that the theory predicts primordial gravitational waves which are almost identical to the standard inflationary ones. Published by the American Physical Society2025 

Abstract We present a study on the inference of cosmological and astrophysical parameters using stacked galaxy cluster profiles. Utilizing the CAMELS-zoomGZ simulations, we explore how various cluster properties—such as X-ray surface brightness, gas density, temperature, metallicity, and Compton-y profiles—can be used to predict parameters within the 28-dimensional parameter space of the IllustrisTNG model. Through neural networks, we achieve a high correlation coefficient of 0.97 or above for all cosmological parameters, including Ω_m,H₀, andσ₈, and over 0.90 for the remaining astrophysical parameters, showcasing the effectiveness of these profiles for parameter inference. We investigate the impact of different radial cuts, with bins ranging from 0.1R_200cto 0.7R_200c, to simulate current observational constraints. Additionally, we perform a noise sensitivity analysis, adding up to 40% Gaussian noise (corresponding to signal-to-noise ratios as low as 2.5), revealing that key parameters such as Ω_m,H₀, and the initial mass function slope remain robust even under extreme noise conditions. We also compare the performance of full radial profiles against integrated quantities, finding that profiles generally lead to more accurate parameter inferences. Our results demonstrate that stacked galaxy cluster profiles contain crucial information on both astrophysical processes within groups and clusters and the underlying cosmology of the Universe. This underscores their significance for interpreting the complex data expected from next-generation surveys and reveals, for the first time, their potential as a powerful tool for parameter inference. 

Abstract We introduce the DaRk mattEr and Astrophysics with Machine learning and Simulations (DREAMS) project, an innovative approach to understanding the astrophysical implications of alternative dark matter (DM) models and their effects on galaxy formation and evolution. The DREAMS project will ultimately comprise thousands of cosmological hydrodynamic simulations that simultaneously vary over DM 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 (WDM), each comprising 1024 simulations generated using thearepocode. One suite consists of uniform-box simulations covering a ${\left(25h^{-1}\mathrm{Mpc}\right)}^3$volume, while the other consists of Milky Way zoom-ins with sufficient resolution to capture the properties of classical satellites. For each simulation, the WDM 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 DM and baryonic physics on galactic properties. The DREAMS project can be extended further to include different DM 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 DM on these scales. 

Abstract Pulsar distances are notoriously difficult to measure, and play an important role in many fundamental physics experiments, such as pulsar timing arrays. Here, we perform a cross-match between International PTA pulsars (IPTA) and Gaia's Data Release 2 (DR2) and Data Release 3 (DR3). We then combine the IPTA pulsar’s parallax with its binary companion’s parallax, found in Gaia, to improve the distance measurement to the binary. We find seven cross-matched IPTA pulsars in Gaia DR2, and when using Gaia DR3 we find six IPTA pulsar cross-matches but with seven Gaia objects. Moving from Gaia DR2 to Gaia DR3, we find that the Gaia parallaxes for the successfully cross-matched pulsars improved by 53%, and pulsar distances improved by 29%. Finally, we find that binary companions with a <3.0σdetection are unreliable associations, setting a high bar for successful cross-matches. 

Complex astrophysical systems often exhibit low-scatter relations between observable properties (e.g., luminosity, velocity dispersion, oscillation period). These scaling relations illuminate the underlying physics, and can provide observational tools for estimating masses and distances. Machine learning can provide a fast and systematic way to search for new scaling relations (or for simple extensions to existing relations) in abstract high-dimensional parameter spaces. We use a machine learning tool called symbolic regression (SR), which models patterns in a dataset in the form of analytic equations. We focus on the Sunyaev-Zeldovich flux−cluster mass relation ( Y SZ − M ), the scatter in which affects inference of cosmological parameters from cluster abundance data. Using SR on the data from the IllustrisTNG hydrodynamical simulation, we find a new proxy for cluster mass which combines Y SZ and concentration of ionized gas ( c gas ): M ∝ Y conc 3/5 ≡ Y SZ 3/5 (1 − A c gas ). Y conc reduces the scatter in the predicted M by ∼20 − 30% for large clusters ( M ≳ 10 14 h −1 M ⊙ ), as compared to using just Y SZ . We show that the dependence on c gas is linked to cores of clusters exhibiting larger scatter than their outskirts. Finally, we test Y conc on clusters from CAMELS simulations and show that Y conc is robust against variations in cosmology, subgrid physics, and cosmic variance. Our results and methodology can be useful for accurate multiwavelength cluster mass estimation from upcoming CMB and X-ray surveys like ACT, SO, eROSITA and CMB-S4. 

ABSTRACT Feedback from active galactic nuclei (AGNs) and supernovae can affect measurements of integrated Sunyaev–Zeldovich (SZ) flux of haloes (YSZ) from cosmic microwave background (CMB) surveys, and cause its relation with the halo mass (YSZ–M) to deviate from the self-similar power-law prediction of the virial theorem. We perform a comprehensive study of such deviations using CAMELS, a suite of hydrodynamic simulations with extensive variations in feedback prescriptions. We use a combination of two machine learning tools (random forest and symbolic regression) to search for analogues of the Y–M relation which are more robust to feedback processes for low masses ($$M\lesssim 10^{14}\, \mathrm{ h}^{-1} \, \mathrm{ M}_\odot$$); we find that simply replacing Y → Y(1 + M*/Mgas) in the relation makes it remarkably self-similar. This could serve as a robust multiwavelength mass proxy for low-mass clusters and galaxy groups. Our methodology can also be generally useful to improve the domain of validity of other astrophysical scaling relations. We also forecast that measurements of the Y–M relation could provide per cent level constraints on certain combinations of feedback parameters and/or rule out a major part of the parameter space of supernova and AGN feedback models used in current state-of-the-art hydrodynamic simulations. Our results can be useful for using upcoming SZ surveys (e.g. SO, CMB-S4) and galaxy surveys (e.g. DESI and Rubin) to constrain the nature of baryonic feedback. Finally, we find that the alternative relation, Y–M*, provides complementary information on feedback than Y–M. 

We present Weak Gravitational Lensing measurements of a sample of 157 clusters within the Kilo Degree Survey (KiDS), detected with a > 5σthermal Sunyaev-Zel’dovich (SZ) signal by the Atacama Cosmology Telescope (ACT). Using a halo-model approach, we constrained the average total cluster mass,M_WL, accounting for the ACT cluster selection function of the full sample. We find that the SZ cluster mass estimateM_SZ, which was calibrated using X-ray observations, is biased withM_SZ/M_WL = (1 − b_SZ) = 0.65 ± 0.05. Separating the sample into six mass bins, we find no evidence of a strong mass dependency for the mass bias, (1 − b_SZ). Adopting this ACT-KiDS SZ mass calibration would bring thePlanckSZ cluster count into agreement with the counts expected from thePlanckcosmic microwave background ΛCDM cosmological model, although it should be noted that the cluster sample considered in this work has a lower average massM_SZ, uncor = 3.64 × 10¹⁴ M_⊙compared to thePlanckcluster sample which has an average mass in the rangeM_SZ, uncor = (5.5 − 8.5)×10¹⁴ M_⊙, depending on the sub-sample used. 

Abstract Understanding the halo–galaxy connection is fundamental in order to improve our knowledge on the nature and properties of dark matter. In this work, we build a model that infers the mass of a halo given the positions, velocities, stellar masses, and radii of the galaxies it hosts. In order to capture information from correlations among galaxy properties and their phase space, we use Graph Neural Networks (GNNs), which are designed to work with irregular and sparse data. We train our models on galaxies from more than 2000 state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations project. Our model, which accounts for cosmological and astrophysical uncertainties, is able to constrain the masses of the halos with a ∼0.2 dex accuracy. Furthermore, a GNN trained on a suite of simulations is able to preserve part of its accuracy when tested on simulations run with a different code that utilizes a distinct subgrid physics model, showing the robustness of our method. The PyTorch Geometric implementation of the GNN is publicly available on GitHub ( https://github.com/PabloVD/HaloGraphNet ). 

Abstract Traditional large-scale models of reionization usually employ simple deterministic relations between halo mass and luminosity to predict how reionization proceeds. We here examine the impact on modeling reionization of using more detailed models for the ionizing sources as identified within the 100 h −1 Mpc cosmological hydrodynamic simulation S imba , coupled with postprocessed radiative transfer. Comparing with simple (one-to-one) models, the main difference with using S imba sources is the scatter in the relation between dark matter halos and star formation, and hence ionizing emissivity. We find that, at the power spectrum level, the ionization morphology remains mostly unchanged, regardless of the variability in the number of sources or escape fraction. In particular, the power spectrum shape remains unaffected and its amplitude changes slightly by less than 5%–10%, throughout reionization, depending on the scale and neutral fraction. Our results show that simplified models of ionizing sources remain viable to efficiently model the structure of reionization on cosmological scales, although the precise progress of reionization requires accounting for the scatter induced by astrophysical effects.