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Weak gravitational lensing is an important tool to estimate the masses of galaxy clusters, as it allows us to directly access their projected surface mass density along the line of sight in a manner largely independent of their dynamical state. Moreover, we can extract information on the projected shape of the cluster mass distribution. In this work, we generate mock catalogs of lensed background galaxies to measure the individual lensing properties of galaxy clusters from the simulation project The Three Hundred. By repeating the analysis for different projections of the same cluster, we find that the use of shear multipoles provides constraints on the ellipticity of the cluster projected mass density but does not have a significant impact on the cluster mass reconstruction compared to the standard approach.

The Milky Way's dwarf spheroidal (dSph) galaxies are among the best targets for the indirect detection of dark matter (DM) with γ-rays. The expected gamma-ray flux depends on the so-called 'J-factor', the integral of the squared DM density along the line-of-sight. Using a large number of simulated dSphs, we have defined an optimized Jeans analysis setup for the reconstruction of the DM density with stellar-kinematic data. Employing this setup, we provide here estimates of astrophysical J-factors for twenty-two Galactic dSphs, including the newly discovered Reticulum II. We finally identify several criteria that may indicate a contamination of a kinematic dataset by interlopers, leading to unreliable J-factors. We find that the kinematic sample of Segue I, one of the closest dSph, might be affected by this issue.

We present a new method for estimating galaxy cluster masses using weak-lensing magnification. The effect of weak-lensing magnification introduces a correlation between the position of foreground galaxy clusters and the density of background sources. Therefore, cluster masses can be inferred through observations of these correlations. In this work, we introduce a method that allows us to considerably reduce noise correlations between different radial bins of the cluster magnification signal via a Wiener filtering of our observed magnification field on large scales. This method can reduce the uncertainty on the estimated galaxy cluster mass and it can also be applied to cluster mass estimation for weak-lensing shear. The method was applied to Hyper-Suprime Cam galaxies and CAMIRA clusters detected within the Hyper-Suprime Cam survey (HSC). With HSC data, we find that our filtering method significantly reduces the correlation of noise between radial magnification bins. The estimated cluster mass is consistent between the filtered and unfiltered methods, with similar errors between the two methods as our current measurement errors contain significant contributions from the irreducible shot-noise. For deeper surveys, the effects of shot noise will be less important and this method will lead to greater improvements on the estimated cluster mass.

During star formation, both infall and outflows are present around protostellar cores. Here we show solutions of a self-similar model that study the two flows with only one set of equations. We focus here on the effects of magnetic field and dust on solutions. Unmagnetized solutions have also been found. This shows that magnetic field is not the main driving mechanism of the circulation process. We have found that a reduction of magnetic field produces denser, slower and narrower outflows. When the opacity is less dominated by dust, density increases in the equatorial region, allowing larger accretion rates to occur. The comprehension of massive star formation could be related to this latter effect.

It is commonly accepted that stars form in molecular clouds by the gravitational collapse of dense gas. However, it is precisely not the infalling but the outflowing material that is primarily observed. Outflow motions prevail around both low and high mass young stellar objects. We present here results from a family of self-similar models that could possibly help to understand this paradox. The models take into account the heating of the central protostar for the deflection and acceleration of the gas. The models make room for all the ingredients observed around the central objects, i.e. molecular outflows, fast jets, accretion disks and infalling envelopes. We suggest that radiative heating and magnetic field may ultimately be the main energy sources driving outflows for both low and high mass stars. The models show that the ambient medium surrounding the jet is unhomogeneous in density, velocity, magnetic field. Consequently, we suggest that jets and outflows have a prehistory that is inprinted in their environment, and that this should have direct consequences on the setting of jet numerical simulations.

The first survey data release by the Euclid mission covers approximately \\(63\\,\\mathrm{deg^2}\\) in the Euclid Deep Fields to the same depth as the Euclid Wide Survey. This paper showcases, for the first time, the performance of cluster finders on Euclid data and presents examples of validated clusters in the Quick Release 1 (Q1) imaging data. We identify clusters using two algorithms (AMICO and PZWav) implemented in the Euclid cluster-detection pipeline. We explore the internal consistency of detections from the two codes, and cross-match detections with known clusters from other surveys using external multi-wavelength and spectroscopic data sets. This enables assessment of the Euclid photometric redshift accuracy and also of systematics such as mis-centring between the optical cluster centre and centres based on X-ray and/or Sunyaev--Zeldovich observations. We report 426 joint PZWav and AMICO-detected clusters with high signal-to-noise ratios over the full Q1 area in the redshift range \\(0.2 \\leq z \\leq 1.5\\). The chosen redshift and signal-to-noise thresholds are motivated by the photometric quality of the early Euclid data. We provide richness estimates for each of the Euclid-detected clusters and show its correlation with various external cluster mass proxies. Out of the full sample, 77 systems are potentially new to the literature. Overall, the Q1 cluster catalogue demonstrates a successful validation of the workflow ahead of the Euclid Data Release 1, based on the consistency of internal and external properties of Euclid-detected clusters.

As network performance has outpaced computational power and storage capacity, a new paradigm has evolved to enable the sharing of geographically distributed resources. This paradigm is known as Grid computing and aims to offer access to distributed resource irrespective of their physical location. Many national, European and international projects have been launched during the last years trying to explore the Grid and to change the way we are doing our everyday work. In Ireland, we have started the CosmoGrid project that is a collaborative project aimed to provide high performance super-computing environments. This will help to address complex problems such as magnetohydrodynamic outflows and jets in order to model and numerically simulate them. Indeed, the numerical modeling of plasma jets requires massive computations, due to the wide range of spatial-temporal scales involved. We present here the first jet simulations and their corresponding models that could help to understand results from laboratory experiments.

We present the v1.0 release of CLMM, an open source Python library for the estimation of the weak lensing masses of clusters of galaxies. CLMM is designed as a standalone toolkit of building blocks to enable end-to-end analysis pipeline validation for upcoming cluster cosmology analyses such as the ones that will be performed by the LSST-DESC. Its purpose is to serve as a flexible, easy-to-install and easy-to-use interface for both weak lensing simulators and observers and can be applied to real and mock data to study the systematics affecting weak lensing mass reconstruction. At the core of CLMM are routines to model the weak lensing shear signal given the underlying mass distribution of galaxy clusters and a set of data operations to prepare the corresponding data vectors. The theoretical predictions rely on existing software, used as backends in the code, that have been thoroughly tested and cross-checked. Combined, theoretical predictions and data can be used to constrain the mass distribution of galaxy clusters as demonstrated in a suite of example Jupyter Notebooks shipped with the software and also available in the extensive online documentation.

We present the first systematic analysis of photometric redshifts (photo-z) estimated from the Rubin Observatory Data Preview 1 (DP1) data taken with the Legacy Survey of Space and Time (LSST) Commissioning Camera. Employing the Redshift Assessment Infrastructure Layers (RAIL) framework, we apply eight photo-z algorithms to the DP1 photometry, using deep ugrizy coverage in the Extended Chandra Deep Field South (ECDFS) field and griz data in the Rubin_SV_38_7 field. In the ECDFS field, we construct a reference catalog from spectroscopic redshift (spec-z), grism redshift (grism-z), and multiband photo-z for training and validating photo-z. Performance metrics of the photo-z are evaluated using spec-zs from ECDFS and Dark Energy Spectroscopic Instrument Data Release 1 samples. Across the algorithms, we achieve per-galaxy photo-z scatter of \\(_ NMAD 0.03\\) and outlier fractions around 10% in the 6-band data, with performance degrading at faint magnitudes and z>1.2. The overall bias and scatter of our machine-learning based photo-zs satisfy the LSST Y1 requirement. We also use our photo-z to infer the ensemble redshift distribution n(z). We study the photo-z improvement by including near-infrared photometry from the Euclid mission, and find that Euclid photometry improves photo-z at z>1.2. Our results validate the RAIL pipeline for Rubin photo-z production and demonstrate promising initial performance.

We present cosmological parameter results from the final full-mission Planck measurements of the CMB anisotropies. We find good consistency with the standard spatially-flat 6-parameter \\(\\Lambda\\)CDM cosmology having a power-law spectrum of adiabatic scalar perturbations (denoted \"base \\(\\Lambda\\)CDM\" in this paper), from polarization, temperature, and lensing, separately and in combination. A combined analysis gives dark matter density \\(\\Omega_c h^2 = 0.120\\pm 0.001\\), baryon density \\(\\Omega_b h^2 = 0.0224\\pm 0.0001\\), scalar spectral index \\(n_s = 0.965\\pm 0.004\\), and optical depth \\(\\tau = 0.054\\pm 0.007\\) (in this abstract we quote \\(68\\,\\%\\) confidence regions on measured parameters and \\(95\\,\\%\\) on upper limits). The angular acoustic scale is measured to \\(0.03\\,\\%\\) precision, with \\(100\\theta_*=1.0411\\pm 0.0003\\). These results are only weakly dependent on the cosmological model and remain stable, with somewhat increased errors, in many commonly considered extensions. Assuming the base-\\(\\Lambda\\)CDM cosmology, the inferred late-Universe parameters are: Hubble constant \\(H_0 = (67.4\\pm 0.5)\\)km/s/Mpc; matter density parameter \\(\\Omega_m = 0.315\\pm 0.007\\); and matter fluctuation amplitude \\(\\sigma_8 = 0.811\\pm 0.006\\). We find no compelling evidence for extensions to the base-\\(\\Lambda\\)CDM model. Combining with BAO we constrain the effective extra relativistic degrees of freedom to be \\(N_{\\rm eff} = 2.99\\pm 0.17\\), and the neutrino mass is tightly constrained to \\(\\sum m_\\nu< 0.12\\)eV. The CMB spectra continue to prefer higher lensing amplitudes than predicted in base -\\(\\Lambda\\)CDM at over \\(2\\,\\sigma\\), which pulls some parameters that affect the lensing amplitude away from the base-\\(\\Lambda\\)CDM model; however, this is not supported by the lensing reconstruction or (in models that also change the background geometry) BAO data. (Abridged)