Most of supernova-originating presolar grains, such as silicon carbide type X (SiC X) and low-density graphite, show excesses of ^28^Si. Some of them also indicate evidence for the original presence of short-lived nuclei ^44^Ti. In order to reproduce isotopic and elemental signatures of these grains, large-scale heterogeneous mixing in supernova ejecta is required. I investigate supernova mixtures that reproduce as many isotopic ratios as possible of 18 individual SiC X and 26 individual low-density graphite grains.
To measure the supernova (SN) rates at intermediate redshift we performed a search, the Southern inTermediate Redshift ESO Supernova Search (STRESS). Unlike most of the current high redshift SN searches, this survey was specifically designed to estimate the rate for both type Ia and core collapse (CC) SNe. We counted the SNe discovered in a selected galaxy sample measuring SN rate per unit blue band luminosity. Our analysis is based on a sample of ~43000 galaxies and on 25 spectroscopically confirmed SNe plus 64 selected SN candidates. Our approach is aimed at obtaining a direct comparison of the high redshift and local rates and at investigating the dependence of the rates on specific galaxy properties, most notably their colour.
The vast majority of Galactic supernova remnants (SNRs) were detected by their synchrotron radio emission. Recently, the evolved SNR G107.0+9.0 with a diameter of about 3D or 75pc up to 100pc in size was optically detected with an indication of faint associated radio emission. This SNR requires a detailed radio study. We searched for radio emission from SNR G107.0+9.0 by analysing new data from the Effelsberg 100-m and the Urumqi 25-m radio telescopes in addition to available radio surveys. Radio SNRs outside of the Galactic plane, where confusion is rare, must be very faint if they have not been identified so far. Guided by the H{alpha} emission of G107.0+9.0, we separated its radio emission from the Galactic large-scale emission. Radio emission from SNR G107.0+9.0 is detected between 22MHz and 4.8GHz with a steep non-thermal spectrum, which confirms G107.0+9.0 as an SNR. Its surface brightness is among the lowest known for Galactic SNRs. Polarised emission is clearly detected at 1.4GHz but is fainter at 4.8GHz. We interpret the polarised emission as being caused by a Faraday screen associated with G107.0+9.0 and its surroundings. Its ordered magnetic field along the line of sight is below 1-microG. At 4.8GHz, we identified a depolarised filament along the western periphery of G107.0+9.0 with a magnetic field strength along the line of sight B_parallel_~15-microG, which requires magnetic field compression. G107.0+9.0 adds to the currently small number of known, evolved, large-diameter, low-surface-brightness Galactic SNRs. We have shown that such objects can be successfully extracted from radio-continuum surveys despite the dominating large-scale diffuse Galactic emission.
The total flux densities of more than one hundred galactic supernova remnants (SNR) at 111, 102, and 83MHz, measured at Pushchino using the E-W WBCR-1000 and LSA radio telescopes, to an accuracy of 2Jy or better; the spectral indices, with their errors, obtained from the compiled spectra; and optical depths at 100MHz in the direction of the supernova remnants are reported. The latter values are obtained from a low frequency cutoff caused by interstellar gas absorption, which was detected at meter and decimeter wavelengths in the direction of 38% of the supernova remnants.
Using radio data to identify and optical data to confirm, we have established the largest and most complete sample of extragalactic radio-bright supernova remnants (SNRs) in the nearby spiral galaxy M33. We have identified 53 radio SNRs, doubling the size of the earlier survey by Duric et al. (1993A&AS...99..217D).
We have carried out a study of the X-ray properties of the supernova remnant (SNR) population in M33 with XMM-Newton, comprising deep observations of eight fields in M33 covering all of the area within the D_25_ contours, and with a typical luminosity of 7.1x10^34^erg/s (0.2-2.0keV). Here, we report our work to characterize the X-ray properties of the previously identified SNRs in M33, as well as our search for new X-ray detected SNRs. With our deep observations and large field of view we have detected 105 SNRs at the 3{sigma} level, of which 54 SNRs are newly detected in X-rays, and three are newly discovered SNRs. Combining XMM-Newton data with deep Chandra survey data allows detailed spectral fitting of 15 SNRs, for which we have measured temperatures, ionization time-scales and individual abundances. This large sample of SNRs allows us to construct an X-ray luminosity function, and compare its shape to luminosity functions from host galaxies of differing metallicities and star formation rates to look for environmental effects on SNR properties. We conclude that while metallicity may play a role in SNR population characteristics, differing star formation histories on short time-scales, and small-scale environmental effects appear to cause more significant differences between X-ray luminosity distributions. In addition, we analyse the X-ray detectability of SNRs, and find that in M33 SNRs with higher [SII]/H{alpha} ratios, as well as those with smaller galactocentric distances, are more detectable in X-rays.
We searched for the optical/UV/IR counterparts of seven supersoft X-ray sources (SSSs) in M31 in the Hubble Space Telescope} (HST}) 'Panchromatic Hubble Andromeda Treasury' (PHAT) archival images and photometric catalogue. Three of the SSSs were transient; the other four are persistent sources. The PHAT offers the opportunity to identify SSSs hosting very massive white dwarfs (WDs) that may explode as Type Ia supernovae in single degenerate binaries, with magnitudes and colour indexes typical of symbiotics, high-mass close binaries, or systems with an optically luminous accretion disc. We find evidence that the transient SSSs were classical or recurrent novae; two probable counterparts that we identified are probably symbiotic binaries undergoing mass transfer at a very high rate. There is a candidate accreting WD binary in the error circle of one of the persistent sources, r3-8. In the spatial error circle of the best-studied SSS in M31, r2-12, no red giants or AGB stars are sufficiently luminous in the optical and UV bands to be symbiotic systems hosting an accreting and hydrogen-burning WD. This SSS has a known modulation of the X-ray flux with a 217.7s period, and we measured an upper limit on its derivative, namely |dP/dt|<~0.82x10^11. This limit can be reconciled with the rotation period of a WD accreting at a high rate in a binary with an orbital period of a few hours. However, there is no luminous counterpart with colour indexes typical of an accretion disc irradiated by a hot central source. Adopting a semi-empirical relationship, the upper limit for the disc optical luminosity implies an upper limit of only 169-min for the orbital period of the WD binary.
In this work, we describe the pipeline for the fast supervised classification of light curves observed by the CoRoT exoplanet CCDs. We present the classification results obtained for the first four measured fields, which represent a one-year in-orbit operation. The basis of the adopted supervised classification methodology has been described in detail in a previous paper, as is its application to the OGLE database. Here, we present the modifications of the algorithms and of the training set, to optimize the performance when applied to the CoRoT data. Classification results are presented for the observed fields IRa01, SRc01, LRc01, and LRa01 of the CoRoT mission. Statistics on the number of variables and the number of objects per class are given and typical light curves of high-probability candidates are shown. We also report on new stellar variability types discovered in the CoRoT data. The full classification results are publicly available.
The fast classification of new variable stars is an important step in making them available for further research. Selection of science targets from large databases is much more efficient if they have been classified first. Defining the classes in terms of physical parameters is also important to get an unbiased statistical view on the variability mechanisms and the borders of instability strips. Our goal is twofold: provide an overview of the stellar variability classes that are presently known, in terms of some relevant stellar parameters; use the class descriptions obtained as the basis for an automated `supervised classification' of large databases. Such automated classification will compare and assign new objects to a set of pre-defined variability training classes. For every variability class, a literature search was performed to find as many well-known member stars as possible, or a considerable subset if too many were present. Next, we searched on-line and private databases for their light curves in the visible band and performed period analysis and harmonic fitting. The derived light curve parameters are used to describe the classes and define the training classifiers. We compared the performance of different classifiers in terms of percentage of correct identification, of confusion among classes and of computation time. We describe how well the classes can be separated using the proposed set of parameters and how future improvements can be made, based on new large databases such as the light curves to be assembled by the CoRoT and Kepler space missions. The derived classifiers' performances are so good in terms of success rate and computational speed that we will evaluate them in practice from the application of our methodology to a large subset of variable stars in the OGLE database and from comparison of the results with published OGLE variable star classifications based on human intervention. These results will be published in a subsequent paper.