An official website of the United States government
Abstract Precise and accurate predictions of the halo mass function for cluster mass scales inwνCDM cosmologies are crucial for extracting robust and unbiased cosmological information from upcoming galaxy cluster surveys.Here, we present a halo mass function emulator for cluster mass scales (≳ 1013M⊙/h) up to redshiftz= 2 with comprehensive support for the parameter space ofwνCDM cosmologies allowed by current data.Based on theAemulusνsuite of simulations, the emulator marks a significant improvement in the precision of halo mass function predictions by incorporating both massive neutrinos and non-standard dark energy equation of state models.This allows for accurate modeling of the cosmology dependence in large-scale structure and galaxy cluster studies.We show that the emulator, designed using Gaussian Process Regression, has negligible theoretical uncertainties compared to dominant sources of error in future cluster abundance studies.Our emulator is publicly available (https://github.com/DelonShen/aemulusnu_hmf), providing the community with a crucial tool for upcoming cosmological surveys such as LSST and Euclid.
more »
« less
No Need to Know: Toward Astrophysics-free Gravitational-wave Cosmology
Abstract Gravitational waves (GWs) from merging compact objects encode direct information about the luminosity distance to the binary. When paired with a redshift measurement, this enables standard-siren cosmology: a Hubble diagram can be constructed to directly probe the Universe’s expansion. This can be done in the absence of electromagnetic measurements, as features in the mass distribution of GW sources provide self-calibrating redshift measurements without the need for a definite or probabilistic host galaxy association. This “spectral siren” technique has thus far only been applied with simple parametric representations of the mass distribution, and theoretical predictions for features in the mass distribution are commonly presumed to be fundamental to the measurement. However, the use of an inaccurate representation leads to biases in the cosmological inference, an acute problem given the current uncertainties in true source population. Furthermore, it is commonly presumed that the form of the mass distribution must be known a priori to obtain unbiased measurements of cosmological parameters in this fashion. Here, we demonstrate that spectral sirens can accurately infer cosmological parameters without such prior assumptions. We apply a flexible, nonparametric model for the mass distribution of compact binaries to a simulated catalog of 1000 GW signals, consistent with expectations for the next LIGO–Virgo–KAGRA observing run. We find that, despite our model’s flexibility, both the source mass model and cosmological parameters are correctly reconstructed. We predict a 11.2%✎measurement ofH0, keeping all other cosmological parameters fixed, and a 6.4%✎measurement ofH(z= 0.9)✎when fitting for multiple cosmological parameters (1σuncertainties). This astrophysically agnostic spectral siren technique will be essential to arrive at precise and unbiased cosmological constraints from GW source populations.
more »
« less
- PAR ID:
- 10565534
- Publisher / Repository:
- DOI PREFIX: 10.3847
- Date Published:
- Journal Name:
- The Astrophysical Journal
- Volume:
- 978
- Issue:
- 2
- ISSN:
- 0004-637X
- Format(s):
- Medium: X Size: Article No. 153
- Size(s):
- Article No. 153
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Galaxies are biased tracers of the underlying cosmic web, which is dominated by dark matter (DM) components that cannot be directly observed. Galaxy formation simulations can be used to study the relationship between DM density fields and galaxy distributions. However, this relationship can be sensitive to assumptions in cosmology and astrophysical processes embedded in galaxy formation models, which remain uncertain in many aspects. In this work, we develop a diffusion generative model to reconstruct DM fields from galaxies. The diffusion model is trained on the CAMELS simulation suite that contains thousands of state-of-the-art galaxy formation simulations with varying cosmological parameters and subgrid astrophysics. We demonstrate that the diffusion model can predict the unbiased posterior distribution of the underlying DM fields from the given stellar density fields while being able to marginalize over uncertainties in cosmological and astrophysical models. Interestingly, the model generalizes to simulation volumes ≈500 times larger than those it was trained on and across different galaxy formation models. The code for reproducing these results can be found athttps://github.com/victoriaono/variational-diffusion-cdm✎.more » « less
-
Abstract New observational facilities are probing astrophysical transients such as stellar explosions and gravitational-wave sources at ever-increasing redshifts, while also revealing new features in source property distributions. To interpret these observations, we need to compare them to predictions from stellar population models. Such models require the metallicity-dependent cosmic star formation history ( ) as an input. Large uncertainties remain in the shape and evolution of this function. In this work, we propose a simple analytical function for . Variations of this function can be easily interpreted because the parameters link to its shape in an intuitive way. We fit our analytical function to the star-forming gas of the cosmological TNG100 simulation and find that it is able to capture the main behavior well. As an example application, we investigate the effect of systematic variations in the parameters on the predicted mass distribution of locally merging binary black holes. Our main findings are that (i) the locations of features are remarkably robust against variations in the metallicity-dependent cosmic star formation history, and (ii) the low-mass end is least affected by these variations. This is promising as it increases our chances of constraining the physics that govern the formation of these objects (https://github.com/LiekeVanSon/SFRD_fit/tree/7348a1ad0d2ed6b78c70d5100fb3cd2515493f02/).more » « less
-
Abstract We generalize the recently proposed Stepped Partially Acoustic Dark Matter (SPartAcous) model by including additional massless degrees of freedom in the dark radiation sector. We fit SPartAcous and its generalization against cosmological precision data from the cosmic microwave background, baryon acoustic oscillations, large-scale structure, supernovae type Ia, and Cepheid variables. We find that SPartAcous significantly reduces theH0tension but does not provide any meaningful improvement of theS8tension, while the generalized model succeeds in addressing both tensions, and provides a better fit than ΛCDM and other dark sector models proposed to address the same tensions. In the generalized model,H0can be raised to 71.4 km/s/Mpc (the 95% upper limit), reducing the tension, if the fitted data does not include the direct measurement from the SH0ES collaboration, and to 73.7 km/s/Mpc (95% upper limit) if it does. A version ofCLASSthat has been modified to analyze this model is publicly available athttps://github.com/ManuelBuenAbad/class_spartacous.more » « less
-
Abstract We present a critical analysis of physics-informed neural operators (PINOs) to solve partial differential equations (PDEs) that are ubiquitous in the study and modeling of physics phenomena using carefully curated datasets. Further, we provide a benchmarking suite which can be used to evaluate PINOs in solving such problems. We first demonstrate that our methods reproduce the accuracy and performance of other neural operators published elsewhere in the literature to learn the 1D wave equation and the 1D Burgers equation. Thereafter, we apply our PINOs to learn new types of equations, including the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we show that our approach is also applicable to learn the physics of the 2D linear and nonlinear shallow water equations, which involve three coupled PDEs. We release our artificial intelligence surrogates and scientific software to produce initial data and boundary conditions to study a broad range of physically motivated scenarios. We provide thesource code, an interactivewebsiteto visualize the predictions of our PINOs, and a tutorial for their use at theData and Learning Hub for Science.more » « less
