ترغب بنشر مسار تعليمي؟ اضغط هنا

The First Data Release of CNIa0.02 -- A Complete Nearby (Redshift <0.02) Sample of Type Ia Supernova Light Curves

85   0   0.0 ( 0 )
 نشر من قبل Subo Dong
 تاريخ النشر 2020
  مجال البحث فيزياء
والبحث باللغة English




اسأل ChatGPT حول البحث

The CNIa0.02 is a complete, nearby sample of Type Ia supernova (SN Ia) multiband light curves, and it is volume-limited with host-galaxy redshift z_host<0.02. The scientific goal of CNIa0.02 is to infer the distributions of key properties (e.g., the luminosity function) of local SNe Ia in a complete and unbiased fashion in order to study SN explosion physics. We spectroscopically classify any SN candidate detected (discovered or recovered) by the All-Sky Automated Survey for Supernovae (ASAS-SN) that reaches peak brightness <16.5 mag. Since ASAS-SN scans the full sky and does not target specific galaxies, the sample is effectively unbiased by host-galaxy properties. We obtain multiband photometric observations starting from the time of discovery. In the first data release (DR1), we present the optical light curves obtained for 240 SNe (including 182 with multiband data), and we derive parameters such as the peak fluxes and dm15.

قيم البحث

اقرأ أيضاً

The Carnegie Supernova Project (CSP) is a five-year survey being carried out at the Las Campanas Observatory to obtain high-quality light curves of ~100 low-redshift Type Ia supernovae in a well-defined photometric system. Here we present the first r elease of photometric data that contains the optical light curves of 35 Type Ia supernovae, and near-infrared light curves for a subset of 25 events. The data comprise 5559 optical (ugriBV) and 1043 near-infrared (YJHKs) data points in the natural system of the Swope telescope. Twenty-eight supernovae have pre-maximum data, and for 15 of these, the observations begin at least 5 days before B maximum. This is one of the most accurate datasets of low-redshift Type Ia supernovae published to date. When completed, the CSP dataset will constitute a fundamental reference for precise determinations of cosmological parameters, and serve as a rich resource for comparison with models of Type Ia supernovae.
In recent years, wide-field sky surveys providing deep multi-band imaging have presented a new path for indirectly characterizing the progenitor populations of core-collapse supernovae (SN): systematic light curve studies. We assemble a set of 76 gri zy-band Type IIP SN light curves from Pan-STARRS1, obtained over a constant survey program of 4 years and classified using both spectroscopy and machine learning-based photometric techniques. We develop and apply a new Bayesian model for the full multi-band evolution of each light curve in the sample. We find no evidence of a sub-population of fast-declining explosions (historically referred to as Type IIL SNe). However, we identify a highly significant relation between the plateau phase decay rate and peak luminosity among our SNe IIP. These results argue in favor of a single parameter, likely determined by initial stellar mass, predominantly controlling the explosions of red supergiants. This relation could also be applied for supernova cosmology, offering a standardizable candle good to an intrinsic scatter of 0.2 mag. We compare each light curve to physical models from hydrodynamic simulations to estimate progenitor initial masses and other properties of the Pan-STARRS1 Type IIP SN sample. We show that correction of systematic discrepancies between modeled and observed SN IIP light curve properties and an expanded grid of progenitor properties, are needed to enable robust progenitor inferences from multi-band light curve samples of this kind. This work will serve as a pathfinder for photometric studies of core-collapse SNe to be conducted through future wide field transient searches.
We present a sample of normal type Ia supernovae from the Nearby Supernova Factory dataset with spectrophotometry at sufficiently late phases to estimate the ejected mass using the bolometric light curve. We measure $^{56}$Ni masses from the peak bol ometric luminosity, then compare the luminosity in the $^{56}$Co-decay tail to the expected rate of radioactive energy re- lease from ejecta of a given mass. We infer the ejected mass in a Bayesian context using a semi-analytic model of the ejecta, incorporating constraints from contemporary numerical models as priors on the density structure and distribution of $^{56}$Ni throughout the ejecta. We find a strong correlation between ejected mass and light curve decline rate, and consequently $^{56}$Ni mass, with ejected masses in our data ranging from 0.9-1.4 $M_odot$. Most fast-declining (SALT2 $x_1 < -1$) normal SNe Ia have significantly sub-Chandrasekhar ejected masses in our fiducial analysis.
CCD BVRI photometry is presented for type Ia supernova 2008gy. The light curves match the template curves for fast-declining SN Ia, but the colors appear redder than average, and the SN may also be slightly subluminous. SN 2008gy is found to be locat ed far outside the boundaries of three nearest galaxies, each of them has nearly equal probability to be the host galaxy.
Upcoming high-cadence transient survey programmes will produce a wealth of observational data for Type Ia supernovae. These data sets will contain numerous events detected very early in their evolution, shortly after explosion. Here, we present synth etic light curves, calculated with the radiation hydrodynamical approach Stella for a number of different explosion models, specifically focusing on these first few days after explosion. We show that overall the early light curve evolution is similar for most of the investigated models. Characteristic imprints are induced by radioactive material located close to the surface. However, these are very similar to the signatures expected from ejecta-CSM or ejecta-companion interaction. Apart from the pure deflagration explosion models, none of our synthetic light curves exhibit the commonly assumed power-law rise. We demonstrate that this can lead to substantial errors in the determination of the time of explosion. In summary, we illustrate with our calculations that even with very early data an identification of specific explosion scenarios is challenging, if only photometric observations are available.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا