High resolution radio study of the Pulsar Wind Nebula within the Supernova Remnant G0.9+0.1

We have conducted a radio study at 3.6, 6 and 20 cm using ATCA and VLA and reprocessed XMM-Newton and Chandra data of the pulsar wind nebula (PWN) in the supernova remnant (SNR) G0.9+0.1. The new observations revealed that the morphology and symmetry suggested by Chandra observations (torus and jet-like features) are basically preserved in the radio range in spite of the rich structure observed in the radio emission of this PWN, including several arcs, bright knots, extensions and filaments. The reprocessed X-ray images show for the first time that the X-ray plasma fills almost the same volume as the radio PWN. Notably the X-ray maximum does not coincide with the radio maximum and the neutron star candidate CXOU J174722.8-280915 lies within a small depression in the radio emission. From the new radio data we have refined the flux density estimates, obtaining S(PWN) ~ 1.57 Jy, almost constant between 3.6 and 20 cm. For the whole SNR (compact core and shell), a flux density S(at 20 cm)= 11.5 Jy was estimated. Based on the new and the existing 90 cm flux density estimates, we derived alpha(PWN)=-0.18+/-0.04 and alpha(shell)=-0.68+/- 0.07. From the combination of the radio data with X-ray data, a spectral break is found near nu ~ 2.4 x 10^(12) Hz. The total radio PWN luminosity is L(radio)=1.2 x 10^(35) erg s^(-1) when a distance of 8.5 kpc is adopted. By assuming equipartition between particle and magnetic energies, we estimate a nebular magnetic field B = 56 muG. The associated particle energy turns out to be U(part)=5 x 10^(47) erg and the magnetic energy U(mag)=2 x 10^(47) erg. Based on an empirical relation between X-ray luminosity and pulsar energy loss rate, and the comparison with the calculated total energy, a lower limit of 1100 yr is derived for the age of this PWN.Comment: 10 pages,8 figures, accepted for publication in A&A, June 13 200

Modes of Multiple Star Formation

This paper argues that star forming environments should be classified into finer divisions than the traditional isolated and clustered modes. Using the observed set of galactic open clusters and theoretical considerations regarding cluster formation, we estimate the fraction of star formation that takes place within clusters. We find that less than 10% of the stellar population originates from star forming regions destined to become open clusters, confirming earlier estimates. The smallest clusters included in the observational surveys (having at least N=100 members) roughly coincide with the smallest stellar systems that are expected to evolve as clusters in a dynamical sense. We show that stellar systems with too few members N < N_\star have dynamical relaxation times that are shorter than their formation times (1-2 Myr), where the critical number of stars N_\star \approx 100. Our results suggest that star formation can be characterized by (at least) three principal modes: I. isolated singles and binaries, II. groups (N<N_\star), and III. clusters (N>N_\star). Many -- if not most -- stars form through the intermediate mode in stellar groups with 10<N<100. Such groups evolve and disperse much more rapidly than open clusters; groups also have a low probability of containing massive stars and are unaffected by supernovae and intense ultraviolet radiation fields. Because of their short lifetimes and small stellar membership, groups have relatively little effect on the star formation process (on average) compared to larger open clusters.Comment: accepted to The Astrophysical Journa

Spatial transcriptomic characterization of COVID-19 pneumonitis identifies immune circuits related to tissue injury

Severe lung damage resulting from COVID-19 involves complex interactions between diverse populations of immune and stromal cells. In this study, we used a spatial transcriptomics approach to delineate the cells, pathways, and genes present across the spectrum of histopathological damage in COVID-19–affected lung tissue. We applied correlation network–based approaches to deconvolve gene expression data from 46 areas of interest covering more than 62,000 cells within well-preserved lung samples from 3 patients. Despite substantial interpatient heterogeneity, we discovered evidence for a common immune-cell signaling circuit in areas of severe tissue that involves crosstalk between cytotoxic lymphocytes and pro-inflammatory macrophages. Expression of IFNG by cytotoxic lymphocytes was associated with induction of chemokines, including CXCL9, CXCL10, and CXCL11, which are known to promote the recruitment of CXCR3+ immune cells. The TNF superfamily members BAFF (TNFSF13B) and TRAIL (TNFSF10) were consistently upregulated in the areas with severe tissue damage. We used published spatial and single-cell SARS-CoV-2 data sets to validate our findings in the lung tissue from additional cohorts of patients with COVID-19. The resulting model of severe COVID-19 immune-mediated tissue pathology may inform future therapeutic strategies

Women, resettlement and desistance

With the numbers of women imprisoned increasing across Western jurisdictions over the last 15 or so years, so too have the numbers of women returning to the community following a period in custody. Despite increasing policy attention in the UK and elsewhere to prisoner resettlement, women’s experiences on release from prison have received limited empirical and policy attention. Drawing upon interviews with women leaving prison in Victoria, Australia, this article discusses the resettlement challenges faced by the women and highlights their similarity to the experiences of women leaving prison in other jurisdictions. Women had mixed (and predominantly negative) experiences and views of accessing services and supports following release, though experiences of parole supervision by community corrections officers were often positive, especially if women felt valued and supported by workers who demonstrated genuine concern. Analysis of factors associated with further offending and with desistance, points to the critical role of flexible, tailored and women-centred post-release support building, and, where possible, upon relationships established with women while they are still in prison

Modeling the spectral evolution of PWNe inside SNRs

We present a new model for the spectral evolution of Pulsar Wind Nebulae inside Supernova Remnants. The model couples the long-term dynamics of these systems, as derived in the 1-D approximation, with a 1-zone description of the spectral evolution of the emitting plasma. Our goal is to provide a simplified theoretical description that can be used as a tool to put constraints on unknown properties of PWN-SNR systems: a piece of work that is preliminary to any more accurate and sophisticated modeling. In the present paper we apply the newly developed model to a few objects of different ages and luminosities. We find that an injection spectrum in the form of a broken-power law gives a satisfactory description of the emission for all the systems we consider. More surprisingly, we also find that the intrinsic spectral break turns out to be at a similar energy for all sources, in spite of the differences mentioned above. We discuss the implications of our findings on the workings of pulsar magnetospheres, pair multiplicity and on the particle acceleration mechanism(s) that might be at work at the pulsar wind termination shock.Comment: 20 Pages, 6 Figures, Submitted to MNRA

Social Vulnerabilities Conference 2020: post conference report

The Social Vulnerabilities Research Group represents research carried out across a range of Social Sciences disciplines, in particular members of the Human Geography, Sociology, and Criminology communities. The research group emphasizes the importance and application of interdisciplinary approaches to better understand the challenges facing vulnerable people in different contexts. The 2020 conference, hosted virtually during the pandemic, showcases work from staff and research students working with the Social Vulnerabilities group

The Compton Spectrometer and Imager

The Compton Spectrometer and Imager (COSI) is a NASA Small Explorer (SMEX) satellite mission in development with a planned launch in 2027. COSI is a wide-field gamma-ray telescope designed to survey the entire sky at 0.2-5 MeV. It provides imaging, spectroscopy, and polarimetry of astrophysical sources, and its germanium detectors provide excellent energy resolution for emission line measurements. Science goals for COSI include studies of 0.511 MeV emission from antimatter annihilation in the Galaxy, mapping radioactive elements from nucleosynthesis, determining emission mechanisms and source geometries with polarization measurements, and detecting and localizing multimessenger sources. The instantaneous field of view for the germanium detectors is >25% of the sky, and they are surrounded on the sides and bottom by active shields, providing background rejection as well as allowing for detection of gamma-ray bursts and other gamma-ray flares over most of the sky. In the following, we provide an overview of the COSI mission, including the science, the technical design, and the project status.Comment: 8 page

The cosipy library: COSI's high-level analysis software

The Compton Spectrometer and Imager (COSI) is a selected Small Explorer (SMEX) mission launching in 2027. It consists of a large field-of-view Compton telescope that will probe with increased sensitivity the under-explored MeV gamma-ray sky (0.2-5 MeV). We will present the current status of cosipy, a Python library that will perform spectral and polarization fits, image deconvolution, and all high-level analysis tasks required by COSI's broad science goals: uncovering the origin of the Galactic positrons, mapping the sites of Galactic nucleosynthesis, improving our models of the jet and emission mechanism of gamma-ray bursts (GRBs) and active galactic nuclei (AGNs), and detecting and localizing gravitational wave and neutrino sources. The cosipy library builds on the experience gained during the COSI balloon campaigns and will bring the analysis of data in the Compton regime to a modern open-source likelihood-based code, capable of performing coherent joint fits with other instruments using the Multi-Mission Maximum Likelihood framework (3ML). In this contribution, we will also discuss our plans to receive feedback from the community by having yearly software releases accompanied by publicly-available data challenges