The Supernova Legacy Survey: measurement of $\Omega_{\mathsf{M}}$, $\Omega_\mathsf{\Lambda}$ andwfrom the first year data set

Abstract

We present distance measurements to 71 high redshift type Ia supernovae discovered during the first year of the 5-year Supernova Legacy Survey (SNLS). These events were detected and their multi-color light-curves measured using the MegaPrime/MegaCam instrument at the Canada-France-Hawaii Telescope (CFHT), by repeatedly imaging four one-square degree fields in four bands, as part of the CFHT Legacy Survey (CFHTLS). Follow-up spectroscopy was performed at the VLT, Gemini and Keck telescopes to confirm the nature of the supernovae and to measure their redshift. With this data set, we have built a Hubble diagram extending to z = 1, with all distance measurements involving at least two bands. Systematic uncertainties are evaluated making use of the multi-band photometry obtained at CFHT. Cosmological fits to this first year SNLS Hubble diagram give the following results: Ω M = 0.263 ± 0.042 (stat) ± 0.032 (sys) for a flat ΛCDM model; and w = −1.023 ± 0.090 (stat) ± 0.054 (sys) for a flat cosmology with constant equation of state w when combined with the constraint from the recent Sloan Digital Sky Survey measurement of baryon acoustic oscillations.

Observational probes of cosmic acceleration

“…The largest high-redshift (z ≈ 0.4 − 1.0) data sets are those from the ESSENCE survey (Wood-Vasey et al 2007; Narayan et al, in prep. ; ∼200 spectroscopically confirmed Type Ia SNe) and the CFHT Supernova Legacy Survey (SNLS; Astier et al 2006;Conley et al 2011;Sullivan et al 2011; ∼500 spectroscopically confirmed Type Ia SNe in the three-year data set SNLS3). At very high redshifts, HST surveys (Riess et al, 2004Suzuki et al, 2012) have yielded ∼ 25 Type Ia SNe at z > 1.0, which confirm the expectation that the universe was decelerating at high redshift and limit possible systematic effects from evolution of the supernova population or intergalactic dust extinction.…”

A new mechanism for dark energy: the adaptive screening

“…10 −3 eV [1][2][3][4][5][6][7][8][9][10][11]. The same measurements allow for the suggestive interpretation of dark energy as a small but non-vanishing pure vacuum energy: the famous cosmological constant, for which w = −1 must hold at all times.…”

Sunfall: A Collaborative Visual Analytics System for Astrophysics

“…For example, the 2005 Sloan Digital Sky Survey II (SDSS) supernova program generated approximately 4,000 objects per night which needed to be visually checked by humans for verification [16]. The ESSENCE and SuperNova Legacy Survey (SNLS) supernova searches both resulted in 100-200 objects to scan per night with a significantly smaller input data load than the SNfactory [17]. Techniques used in these surveys will not scale to future, much larger data sets.…”

“…In 2005, the SNfactory pioneered the use of machine learning algorithms to replace "cuts" for supernova object detection [9], and since then, such techniques have been utilized by other supernova searches, including SDSS and SNLS [16,17]. The SDSS "autoscanner" uses a combination of heuristics and a naive Bayes classifier to filter out non-SN objects, but they do not provide a comparison of the efficacy of any other types of machine learning algorithms for supernova search.…”