arXiv subjects

Julian Meunier

Publications and source records attributed to Julian Meunier.

3 recordsLinked to original sources

arXiv2026

Probable Detection of a Cooler Gas Component in the Perseus Cluster with XRISM

We present an analysis of the temperature structure of the Perseus cluster atmosphere using XRISM Resolve observations. The average temperature rises from 3.3 keV near the nucleus of NGC 1275 to 8 keV at 10 arcmin (210 kpc), which is consistent with Chandra and XMM measurements. The velocity and velocity dispersion profiles are broadly consistent with those in arXiv:2509.04421. While the gas at altitudes beyond $\sim60$ kpc can be modeled as a single temperature plasma, we find evidence for more than one gas phase in the inner $\sim60$ kpc. The hotter gas component, traced primarily by the Fe He$\alpha$ line, has a velocity dispersion of $\lesssim140$ km s$^{-1}$. We detect a cooler, $\sim1.87-2.43$ keV, gas component with a velocity dispersion of $\sim300-400$ km s$^{-1}$ and a bulk velocity of $\sim 21-213$ km s$^{-1}$ with respect to the central galaxy. These ranges reflect large systematic uncertainties associated with modeling spatial-spectral mixing and the bright central point source. Potential low energy gain variations may add additional uncertainties. The cooler component is identified by broad wings in prominent emission lines, most notably S Ly$\alpha$ and Fe He$\alpha$. This cooler component's Mach number $\sim0.73-0.96$ and non-thermal pressure fraction of $\sim22.9-33.7\%$ are much higher than found for the hotter gas. The cooler gas may be associated with merging halos along the line of sight which formed the cool, sloshing spiral and/or cooling gas being disturbed by the radio jets and lobes.

astro-ph.HE↗

arXiv2025

A XRISM Observation of the Archetypal Radio-Mode Feedback System Hydra-A: Measurements of Atmospheric Motion and Constraints on Turbulent Dissipation

We present XRISM Resolve observations centered on Hydra-A, a redshift z = 0.054 brightest cluster galaxy which hosts one of the largest and most powerful FR-I radio sources in the nearby Universe. We examine the effects of its high jet power on the velocity structure of the cluster's hot atmosphere. Hydra-A's central radio jets have inflated X-ray cavities with energies upward of $10^{61}$ erg. They reach altitudes of 225 kpc from the cluster center, well beyond the atmosphere's central cooling region. Resolve's $3\times3$ arcmin field-of-view covers $190\times190$ kpc, which encompasses most of the cooling volume. We find a one dimensional atmospheric velocity dispersion across the volume of $164\pm10$ km/s. The fraction in isotropic turbulence or unresolved bulk velocity is unknown. Assuming pure isotropic turbulence, the turbulent kinetic energy is $2.5 \%$ of the thermal energy radiated away over the cooling timescale, implying that kinetic energy must be supplied continually to offset cooling. While Hydra-A's radio jets are powerful enough to supply kinetic energy to the atmosphere at the observed level, turbulent dissipation alone would struggle to offset cooling throughout the cooling volume. The central galaxy's radial velocity is similar to the atmospheric velocity, with an offset of $-37 \pm 23$ km/s.

astro-ph.GA↗

arXiv2025

Towards characterizing dark matter subhalo perturbations in stellar streams with graph neural networks

The phase space of stellar streams is proposed to detect dark substructure in the Milky Way through the perturbations created by passing subhalos - and thus is a powerful test of the cold dark matter paradigm and its alternatives. Using graph convolutional neural network (GCNN) data compression and simulation-based inference (SBI) on a simulated GD-1-like stream, we improve the constraint on the mass of a [$10^8$, $10^7$, $10^6$] $M_\odot$ perturbing subhalo by factors of [11, 7, 3] with respect to the current state-of-the-art density power spectrum analysis. We find that the GCNN produces posteriors that are more accurate (better calibrated) than the power spectrum. We simulate the positions and velocities of stars in a GD-1-like stream and perturb the stream with subhalos of varying mass and velocity. Leveraging the feature encoding of the GCNN to compress the input phase space data, we then use SBI to estimate the joint posterior of the subhalo mass and velocity. We investigate how our results scale with the size of the GCNN, the coordinate system of the input and the effect of incomplete observations. Our results suggest that a survey with $10 \times$ fewer stars (300 stars) with complete 6-D phase space data performs about as well as a deeper survey (3000 stars) with only 3-D data (photometry, spectroscopy). The stronger constraining power and more accurate posterior estimation motivate further development of GCNNs in combining future photometric, spectroscopic and astrometric stream observations.

astro-ph.GA↗