The Discovery and Classification of 16 Supernovae at High Redshifts in ELAIS-S1 : the Stockholm VIMOS Supernova Survey II

Supernova surveys can be used to study a variety of subjects, such as: (i) cosmology using type Ia supernovae, (ii) star formationrates using core-collapse SNe, (iii) supernova properties and their connection to host galaxy characteristics. The Stockholm VIMOSSupernova Survey (SVISS) is a multi-band imaging survey aiming to detect supernovae at redshift ∼0.5 and derive thermonuclearand core-collapse supernova rates at high redshift. In this paper we present the supernovae discovered in the survey along with lightcurves and a photometric classification into thermonuclear and core-collapse types. To detect the supernovae in the VLT/VIMOSmulti-epoch images we used difference imaging and a combination of automatic and manual source detection to minimise the numberof spurious detections. Photometry for the found variable sources was obtained and careful simulations done to estimate correct errors.The light curves were typed using a Bayesian probability method and Monte Carlo simulations were used to study misclassification.We detected 16 supernovae, eight of which had a core-collapse origin and eight that had a thermonuclear origin. The estimatedmisclassification errors are quite small, on the order of 5%, but vary with both redshift and type. The mean redshift of the supernovaeis 0.64. Additionally, we found a variable source with a very extended light curve that could possibly be a pair instability supernova

The Discovery and Classification of 16 Supernovae at High Redshifts in ELAIS-S1 : the Stockholm VIMOS Supernova Survey II

Supernova surveys can be used to study a variety of subjects, such as: (i) cosmology using type Ia supernovae, (ii) star formationrates using core-collapse SNe, (iii) supernova properties and their connection to host galaxy characteristics. The Stockholm VIMOSSupernova Survey (SVISS) is a multi-band imaging survey aiming to detect supernovae at redshift ∼0.5 and derive thermonuclearand core-collapse supernova rates at high redshift. In this paper we present the supernovae discovered in the survey along with lightcurves and a photometric classification into thermonuclear and core-collapse types. To detect the supernovae in the VLT/VIMOSmulti-epoch images we used difference imaging and a combination of automatic and manual source detection to minimise the numberof spurious detections. Photometry for the found variable sources was obtained and careful simulations done to estimate correct errors.The light curves were typed using a Bayesian probability method and Monte Carlo simulations were used to study misclassification.We detected 16 supernovae, eight of which had a core-collapse origin and eight that had a thermonuclear origin. The estimatedmisclassification errors are quite small, on the order of 5%, but vary with both redshift and type. The mean redshift of the supernovaeis 0.64. Additionally, we found a variable source with a very extended light curve that could possibly be a pair instability supernova

The Discovery and Classification of 16 Supernovae at High Redshifts in ELAIS-S1 : the Stockholm VIMOS Supernova Survey II

Supernova surveys can be used to study a variety of subjects, such as: (i) cosmology using type Ia supernovae, (ii) star formationrates using core-collapse SNe, (iii) supernova properties and their connection to host galaxy characteristics. The Stockholm VIMOSSupernova Survey (SVISS) is a multi-band imaging survey aiming to detect supernovae at redshift ∼0.5 and derive thermonuclearand core-collapse supernova rates at high redshift. In this paper we present the supernovae discovered in the survey along with lightcurves and a photometric classification into thermonuclear and core-collapse types. To detect the supernovae in the VLT/VIMOSmulti-epoch images we used difference imaging and a combination of automatic and manual source detection to minimise the numberof spurious detections. Photometry for the found variable sources was obtained and careful simulations done to estimate correct errors.The light curves were typed using a Bayesian probability method and Monte Carlo simulations were used to study misclassification.We detected 16 supernovae, eight of which had a core-collapse origin and eight that had a thermonuclear origin. The estimatedmisclassification errors are quite small, on the order of 5%, but vary with both redshift and type. The mean redshift of the supernovaeis 0.64. Additionally, we found a variable source with a very extended light curve that could possibly be a pair instability supernova