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1.
iPTF Discoveries of Recent Core-Collapse Supernovae
Francesco Taddia, Tanja Petrushevska, 2014, other component parts

Abstract: The intermediate Palomar Transient Factory (ATel#4807) reports the discovery and classification of the following core-collapse supernovae:
Keywords: supernovae, transients
Published in RUNG: 26.01.2018; Views: 2947; Downloads: 0
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2.
iPTF Discoveries of Recent Core-Collapse Supernovae
Francesco Taddia, Tanja Petrushevska, 2015, other component parts

Abstract: The intermediate Palomar Transient Factory (ATel #4807) reports the discovery and classification of the following Core-Collapse SNe. Our automated candidate vetting to distinguish a real astrophysical source (1.0) from bogus artifacts (0.0) is powered by three generations of machine learning algorithms: RB2 (Brink et al. 2013MNRAS.435.1047B), RB4 (Rebbapragada et al. 2015AAS...22543402R) and RB5 (Wozniak et al. 2013AAS...22143105W). See ATel #7112 for additional details. The approved classification (and/or subsequent) spectra are made publicly available through WISeREP (Yaron & Gal-Yam 2012).
Keywords: supernova, transient
Published in RUNG: 25.01.2018; Views: 3032; Downloads: 0
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3.
iPTF Transient Classification Report for 2016-05-15
Francesco Taddia, Tanja Petrushevska, 2016, other component parts

Keywords: Transient, Supernova, 2016ccz, SN2016ccz, SN 2016ccz
Published in RUNG: 25.01.2018; Views: 2949; Downloads: 0
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4.
iPTF Discoveries of Recent Core-Collapse Supernovae
Francesco Taddia, 2015, other component parts

Abstract: The intermediate Palomar Transient Factory (ATel #4807) reports the discovery and classification of the following Core-Collapse SNe. Our automated candidate vetting to distinguish a real astrophysical source (1.0) from bogus artifacts (0.0) is powered by three generations of machine learning algorithms: RB2 (Brink et al. 2013MNRAS.435.1047B), RB4 (Rebbapragada et al. 2015AAS...22543402R) and RB5 (Wozniak et al. 2013AAS...22143105W). See ATel #7112 for additional details. The approved classification (and/or subsequent) spectra are made publicly available through WISeREP (Yaron & Gal-Yam 2012).
Keywords: Optical, Supernovae, Transient
Published in RUNG: 24.01.2018; Views: 2852; Downloads: 0
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5.
iPTF Discoveries of Recent Core-Collapse Supernovae
Francesco Taddia, Tanja Petrushevska, 2016, other component parts

Abstract: Abstract The intermediate Palomar Transient Factory (ATel #4807) reports the discovery and classification of the following core-collapse SNe. Our automated candidate vetting to distinguish a real astrophysical source (1.0) from bogus artifacts (0.0) is powered by three generations of machine learning algorithms: RB2 (Brink et al. 2013MNRAS.435.1047B), RB4 (Rebbapragada et al. 2015AAS...22543402R) and RB5 (Wozniak et al. 2013AAS...22143105W).
Keywords: Supernovae, Transient
Published in RUNG: 23.01.2018; Views: 2848; Downloads: 0
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6.
Supernova spectra below strong circumstellar interaction
Giorgos Leloudas, E.Y. Hsiao, Joel Johansson, Keichi Maeda, T.J. Moriya, Jakob Nordin, Tanja Petrushevska, J. M. Silverman, Jesper Sollerman, M.D. Stritzinger, Francesco Taddia, D. Xu, 2015, original scientific article

Abstract: We construct spectra of supernovae (SNe) interacting strongly with a circumstellar medium (CSM) by adding SN templates, a blackbody continuum, and an emission-line spectrum. In a Monte Carlo simulation we vary a large number of parameters, such as the SN type, brightness and phase, the strength of the CSM interaction, the extinction, and the signal to noise ratio (S/N) of the observed spectrum. We generate more than 800 spectra, distribute them to ten different human classifiers, and study how the different simulation parameters affect the appearance of the spectra and their classification. The SNe IIn showing some structure over the continuum were characterized as “SNe IInS” to allow for a better quantification. We demonstrate that the flux ratio of the underlying SN to the continuum fV is the single most important parameter determining whether a spectrum can be classified correctly. Other parameters, such as extinction, S/N, and the width and strength of the emission lines, do not play a significant role. Thermonuclear SNe get progressively classified as Ia-CSM, IInS, and IIn as fV decreases. The transition between Ia-CSM and IInS occurs at fV ∼ 0.2−0.3. It is therefore possible to determine that SNe Ia-CSM are found at the (un-extincted) magnitude range −19.5 > M > −21.6, in very good agreement with observations, and that the faintest SN IIn that can hide a SN Ia has M = −20.1. The literature sample of SNe Ia-CSM shows an association with 91T-like SNe Ia. Our experiment does not support that this association can be attributed to a luminosity bias (91T-like being brighter than normal events). We therefore conclude that this association has real physical origins and we propose that 91T-like explosions result from single degenerate progenitors that are responsible for the CSM. Despite the spectroscopic similarities between SNe Ibc and SNe Ia, the number of misclassifications between these types was very small in our simulation and mostly at low S/N. Combined with the SN luminosity function needed to reproduce the observed SN Ia-CSM luminosities, it is unlikely that SNe Ibc constitute an important contaminant within this sample. We show how Type II spectra transition to IIn and how the Hα profiles vary with fV . SNe IIn fainter than M = −17.2 are unable to mask SNe IIP brighter than M = −15. A more advanced simulation, including radiative transfer, shows that our simplified model is a good first order approximation. The spectra obtained are in good agreement with real data.
Keywords: supernovae
Published in RUNG: 22.01.2018; Views: 3444; Downloads: 0
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