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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Far-Infrared Line Emission from the Outer Galaxy Cluster Gy 3–7 With SOFIA/FIFI-LS: Physical Conditions and UV Fields

Context. Far-infrared (FIR) line emission provides key information about the gas cooling and heating due to shocks and UV radiation associated with the early stages of star formation. Gas cooling via FIR lines might, however, depend on metallicity. Aims. We aim to quantify the FIR line emission and determine the spatial distribution of the CO rotational temperature, ultraviolet (UV) radiation field, and H2 number density toward the embedded cluster Gy 3–7 in the CMa–l224 star-forming region, whose metallicity is expected to be intermediate between that of the Large Magellanic Cloud and the Solar neighborhood. By comparing the total luminosities of CO and [O I] toward Gy 3–7 with values found for low- and high-mass protostars extending over a broad range of metallicities, we also aim to identify the possible effects of metallicity on the FIR line cooling within our Galaxy. Methods. We studied SOFIA/FIFI-LS spectra of Gy 3–7, covering several CO transitions from J = 14–13 to 31-30, the OH doublet at 79 µm, the [O I] 63.2 and 145.5 µm, and the [C II] 158 µm lines. The field of view covers a 2 0 × 1 0 region with a resolution of ∼7 00–1800 . Results. The spatial extent of CO high-J (Jup ≥14) emission resembles that of the elongated 160 µm continuum emission detected with Herschel, but its peaks are offset from the positions of the dense cores. The [O I] lines at 63.2 µm and 145.5 µm follow a similar pattern, but their peaks are found closer to the positions of the cores. The CO transitions from J = 14–13 to J = 16–15 are detected throughout the cluster and show a median rotational temperature of 170 ± 30 K on Boltzmann diagrams. Comparisons to other protostars observed with Herschel show a good agreement with intermediate-mass sources in the inner Galaxy. Assuming an origin of the [O I] and high-J CO emission in UV-irradiated C−shocks, we obtained pre-shock H2 number densities of 104–105 cm−3 and UV radiation field strengths of 0.1–10 Habing fields (G0). Conclusions. Far-IR line observations reveal ongoing star formation in Gy 3–7, dominated by intermediate-mass Class 0/I young stellar objects. The ratio of molecular-to-atomic far-IR line emission shows a decreasing trend with bolometric luminosities of the protostars. However, it does not indicate that the low-metallicity has an impact on the line cooling in Gy 3–7.

stars: formation↗
DOE OSTI2022

Parameter inference with non-linear galaxy clustering: accounting for theoretical uncertainties

ABSTRACT We implement euclidemulator (version 1), an emulator for the non-linear correction of the matter power spectrum, into the Markov chain Monte Carlo forecasting code montepython. We compare the performance of halofit, hmcode, and euclidemulator1, both at the level of power spectrum prediction and at the level of posterior probability distributions of the cosmological parameters, for different cosmological models and different galaxy power spectrum wavenumber cut-offs. We confirm that the choice of the power spectrum predictor has a non-negligible effect on the computed sensitivities when doing cosmological parameter forecasting, even for a conservative wavenumber cut-off of $0.2\, h\, {\rm Mpc}^{-1}$. We find that euclidemulator1 is on average up to 17 per cent more sensitive to the cosmological parameters than the other two codes, with the most significant improvements being for the Hubble parameter of up to 42 per cent and the equation of state of dark energy of up to 26 per cent, depending on the case. In addition, we point out that the choice of the power spectrum predictor contributes to the risk of computing a significantly biased mean cosmology when doing parameter estimations. For the four tested scenarios we find biases, averaged over the cosmological parameters, of between 0.5σ and 2σ (from below 1σ up to 6σ for individual parameters). This paper provides a proof of concept that this risk can be mitigated by taking a well-tailored theoretical uncertainty into account as this allows to reduce the bias by a factor of 2 to 5, depending on the case under consideration, while keeping posterior credibility contours small: the standard deviations are amplified by a factor of ≤1.4 in all cases.

Astronomy & Astrophysics↗
NASA NTRS2012

Cold Fronts in Clusters of Galaxies: Observations and Modeling

Mergers of galaxy clusters -- some of the most energetic events in the Universe -- produce disturbances in hot intracluster medium, such as shocks and cold fronts, that can be used as tools to study the physics of galaxy clusters. Cold fronts may constrain viscosity and the structure and strength of the cluster magnetic fields. Combined with radio data, these observations also shed light on the production of ultrarelativistic particles that are known to coexist with the cluster thermal plasma. This talk will summarize the current X-ray observations of cluster mergers, as well as some recent radio data and high resolution hydrodynamic simulations.

Markevitch, Maxim↗
NASA NTRS1983

Numerical experiments on the clustering of galaxies

Consistent and robust growth rates for disturbances which lead to galaxy clustering are obtainable with a precision of 1-2 percent, in numerical experiments that encompass such conditions as expansion, nonexpansion, and parameter variations. The experiments have given attention to the dominant physical processes of gravitational clustering in an expanding universe of conventional matter, and are based on n-body integrations for 100,000 particles responding self-consistently to forces of self-gravitation with periodic boundary conditions. Observed structures of the scale of galaxy clusters and superclusters are most easily described in terms of matter swept away from growing empty regions. The result of this process has a cellular appearance which resembles clustering of the scale of large voids and superclusters.

Miller, R. H.↗
DOE OSTI2022

Deprojection of X-ray data in galaxy clusters: confronting simulations with observations

ABSTRACT Numerical simulations with varying realism indicate an emergent principle−multiphase condensation and large cavity power occur when the ratio of the cooling time to the free-fall time (tcool/tff) falls below a threshold value close to 10. Observations indeed show cool-core signatures when this ratio falls below 20–30, but the prevalence of cores with tcool/tff ratio below 10 is rare as compared to simulations. In X-ray observations, we obtain projected spectra from which we have to infer radial gas density and temperature profiles. Using idealized models of X-ray cavities and multiphase gas in the core and 3D hydro jet-ICM simulations, we quantify the biases introduced by deprojection based on the assumption of spherical symmetry in determining tcool/tff. We show that while the used methods are able to recover the tcool/tff ratio for relaxed clusters, they have an uncertainty of a factor of 2−3 in systems containing large cavities (≳ 20 kpc). We also show that the mass estimates from these methods, in the absence of X-ray spectra close to the virial radius, suffer from a degeneracy between the virial mass (M200) and the concentration parameter (c) in the form of M200c2 ≈ constant. Additionally, the lack of soft-X-ray (≲ 0.5 keV) coverage and poor spatial resolution makes us overestimate min(tcool/tff) by a factor of few in clusters with min(tcool/tff) ≲ 5. This bias can largely explain the lack of cool-core clusters with min(tcool/tff) ≲ 5.

Sarkar, Kartick C. (ORCID:0000000277678472)↗
DOE OSTI2022

Accelerating cosmological inference with Gaussian processes and neural networks – an application to LSST Y1 weak lensing and galaxy clustering

ABSTRACT Studying the impact of systematic effects, optimizing survey strategies, assessing tensions between different probes and exploring synergies of different data sets require a large number of simulated likelihood analyses, each of which cost thousands of CPU hours. In this paper, we present a method to accelerate cosmological inference using emulators based on Gaussian process regression and neural networks. We iteratively acquire training samples in regions of high posterior probability which enables accurate emulation of data vectors even in high dimensional parameter spaces. We showcase the performance of our emulator with a simulated 3×2 point analysis of LSST-Y1 with realistic theoretical and systematics modelling. We show that our emulator leads to high-fidelity posterior contours, with an order of magnitude speed-up. Most importantly, the trained emulator can be re-used for extremely fast impact and optimization studies. We demonstrate this feature by studying baryonic physics effects in LSST-Y1 3×2 point analyses where each one of our MCMC runs takes approximately 5 min. This technique enables future cosmological analyses to map out the science return as a function of analysis choices and survey strategy.

Astronomy & Astrophysics↗