The Nearby Supernova Factory

Lee, B. C.; Aldering, G.; Day, C. T.; Loken, S.; Nugent, P.; Perlmutter, S.; Siegrist, J.; Scalzo, R.; Thomas, R. C.; Wang, L.; Wood‐Vasey, W. M.; Adam, Gilles; Bacon, Roland; Bonnaud, C.; Capoani, L.; Dubet, D.; Lantz, B.; Lemonnier, J. P.; Pécontal, A.; Pécontal, É.; Hénault, François; Blanc, Nathalie; Boudoul, G.; Bongard, S.; Castera, A.; Copin, Y.; Gangler, É.; Smadja, G.; Keßler, R.; Antilogus, P.; Astier, P.; Barrelet, E.; Garavini, G.; Gérard, Sébastien; Guevara, L. A.; Imbault, D.; Juramy, C.; Pain, R.; Taillet, R.; Vincent, D.; Baltay, C.; Rabinovitz, D.; Snyder, J.

doi:10.1016/j.newar.2006.02.035

Cited by 27 publications

(20 citation statements)

References 4 publications

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“…The cosmology group at Lawrence Berkeley Labs pioneered large-scale shallow-wide surveys, initially during the "1999 spring campaign" (Regnault et al 2001) and later on running the "SN factory" project (Copin et al 2006), which used the Palomar 48" Schmidt telescope. These surveys were designed to study SNe Ia, but the fact that the discovered targets were relatively bright allowed high S/N spectroscopy and other follow-up data to be collected, leading to the discovery of the first relatively well-studied superluminous events.…”

Section: Sn Factory and The First Non-targeted Wide-field Surveysmentioning

confidence: 99%

Luminous Supernovae

Gal‐Yam

2012

Science

556

419

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Supernovae, the luminous explosions of stars, have been observed since antiquity. However, various examples of superluminous supernovae (SLSNe; luminosities >7 × 10(43) ergs per second) have only recently been documented. From the accumulated evidence, SLSNe can be classified as radioactively powered (SLSN-R), hydrogen-rich (SLSN-II), and hydrogen-poor (SLSN-I, the most luminous class). The SLSN-II and SLSN-I classes are more common, whereas the SLSN-R class is better understood. The physical origins of the extreme luminosity emitted by SLSNe are a focus of current research.

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Section: Sn Factory and The First Non-targeted Wide-field Surveysmentioning

confidence: 99%

Luminous Supernovae

Gal‐Yam

2012

Science

556

419

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“…The SNfactory is an experiment carried out using the University of Hawaii 2.2m telescope, mounted at Mauna Kea. Its goal is to obtain a sample of well observed SNe Ia in order to improve the measurements of cosmological parameters (Aldering et al 2002;Copin et al 2006). Spectra are acquired through a two-channel Supernova Integral Field Spectrograph (SNIFS, Lantz et al 2004), which simultaneously covers channels B (3200-5200Å) and R (5100-10000Å).…”

Section: The Nearby Supernova Factorymentioning

confidence: 99%

A metric space for Type Ia supernova spectra

Sasdelli

Hillebrandt

Aldering

et al. 2014

Monthly Notices of the Royal Astronomical Society

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We develop a new framework for use in exploring Type Ia Supernova (SN Ia) spectra. Combining Principal Component Analysis (PCA) and Partial Least Square analysis (PLS) we are able to establish correlations between the Principal Components (PCs) and spectroscopic/photometric SNe Ia features. The technique was applied to ∼ 120 supernova and ∼ 800 spectra from the Nearby Supernova Factory. The ability of PCA to group together SNe Ia with similar spectral features, already explored in previous studies, is greatly enhanced by two important modifications: (1) the initial data matrix is built using derivatives of spectra over the wavelength, which increases the weight of weak lines and discards extinction, and (2) we extract time evolution information through the use of entire spectral sequences concatenated in each line of the input data matrix. These allow us to define a stable PC parameter space which can be used to characterize synthetic SN Ia spectra by means of real SN features. Using PLS, we demonstrate that the information from important previously known spectral indicators (namely the pseudo-equivalent width (pEW) of Si ii 5972Å/Si ii 6355Å and the line velocity of S ii 5640Å/Si ii 6355Å) at a given epoch, is contained within the PC space and can be determined through a linear combination of the most important PCs. We also show that the PC space encompasses photometric features like B/V magnitudes, B-V colors and SALT2 parameters c and x 1 . The observed colors and magnitudes, that are heavily affected by extinction, cannot be reconstructed using this technique alone. All the above mentioned applications allowed us to construct a metric space for comparing synthetic SN Ia spectra with observations.

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“…The galaxy catalogue employed is taken from SDSS DR5 and is therefore limited by a comparatively shallow depth compared to the depth to which Pan-STARRS will be able to detect CCSNe (Chambers & Denneau 2007). A far more powerful method that we could implement with Pan-STARRS to detect CCSNe is to perform simply a magnitude-limited search to detect CCSNe over the whole sky and to as large distances as possible following the example of surveys such as the Nearby SN Factory (Copin et al 2006). The inherent difficulties that we will encounter with this kind of survey will be trying to distinguish SNe from all other transient events and variables such as classical novae, asteroids and AGN and further distinguishing CCSNe from the thermonuclear (type Ia) SNe.…”

Section: Strategy 3: Magnitude-limited Searches With Future All-sky Smentioning

confidence: 99%

Core-collapse supernovae in low-metallicity environments and future all-sky transient surveys

Young¹,

Smartt

Mattila³

et al. 2008

A&A

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Aims. Massive stars in low-metallicity environments may produce exotic explosions such as long-duration gamma-ray bursts and pair-instability supernovae when they die as core-collapse supernovae (CCSNe). Such events are predicted to be relatively common in the early Universe during the first episodes of star-formation. To understand these distant explosions it is vital to study nearby CCSNe arising in low-metallicity environments to determine if the explosions have different characteristics to those studied locally in high-metallicity galaxies. Many of the nearby supernova searches concentrate their efforts on high star-formation rate galaxies, hence biasing the discoveries to metal rich regimes. Here we determine the feasibility of searching for these CCSNe in metal-poor dwarf galaxies using various survey strategies. Methods. We determine oxygen abundances and star-formation rates for all spectroscopically typed star-forming galaxies in the Sloan Digital Sky Survey, Data Release 5, within z = 0.04. We then estimate the CCSN rates for sub-samples of galaxies with differing upper-metallicity limits. Using Monte-Carlo simulations we then predict the fraction of these CCSNe that we can expect to detect using different survey strategies. We test survey capabilities using a single 2 m telescope, a network of 2 m telescopes, and the upcoming all-sky surveys of the Pan-STARRS and LSST systems. Results. Using a single 2 m telescope (with a standard CCD camera) search we predict a detection rate of ∼1.3 CCSNe yr −1 in galaxies with metallicities below 12 + log(O/H) < 8.2 which are within a volume that will allow detailed follow-up with 4 m and 8 m telescopes (z = 0.04). With a network of seven 2 m telescopes we estimate ∼9.3 CCSNe yr −1 could be found, although this would require more than 1000 h of telescope time allocated to the network. Within the same radial distance, a volume-limited search in the future Pan-STARRS PS1 all-sky survey should uncover 12.5 CCSNe yr −1 in low-metallicity galaxies. Over a period of a few years this would allow a detailed comparison of their properties. We then extend our calculations to determine the total numbers of CCSNe that can potentially be found in magnitude-limited surveys with PS1 (24 000 yr −1 , within z < ∼ 0.6), PS4 (69 000 yr −1 , within z < ∼ 0.8) and LSST (160 000 yr −1 , within z < ∼ 0.9) surveys.

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The Nearby Supernova Factory

Cited by 27 publications

References 4 publications

Luminous Supernovae

Luminous Supernovae

A metric space for Type Ia supernova spectra

Core-collapse supernovae in low-metallicity environments and future all-sky transient surveys

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