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Determining the Type, Redshift, and Age of a Supernova Spectrum
Open accessAbstract
We present an algorithm to identify the type of an SN spectrum and to determine its redshift and age. This algorithm, based on the correlation techniques of Tonry & Davis, is implemented in the Supernova Identification (SNID) code. It is used by members of ongoing high-redshift SN searches to distinguish between Type Ia and Ib/c SNe and to identify "peculiar" SNe Ia. We develop a diagnostic to quantify the quality of a correlation between the input and template spectra, which enables a formal evaluation of the associated redshift error. Furthermore, by comparing the correlation redshifts obtained using SNID with those determined from narrow lines in the SN host galaxy spectrum, we show that accurate redshifts (with a typical error σ z ≲ 0.01) can be determined for SNe Ia without a spectrum of the host galaxy. Last, the age of an input spectrum is determined with a typical accuracy σ t ≲ 3 days, shown here by using high-redshift SNe Ia with well-sampled light curves. The success of the correlation technique confirms the similarity of some SNe Ia at low and high redshifts. The SNID code, which will be made available to the community, can also be used for comparative studies of SN spectra, as well as comparisons between data and models.
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Spectroscopy of High-Redshift Supernovae from the ESSENCE Project: The First 2 Years
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Metadata
- Authors
- Blondin, S., & Tonry, J. L.
- Venue
- The Astrophysical Journal
- Publisher
- American Astronomical Society
- Published
- 2007-09-10
- Volume
- 666
- Issue
- 2
- Pages
- 1024-1047
- ISSN
- 0004-637X (ISSN-L)
- 1538-4357
- OpenAlex
- W2070951133
- Cited by
- 507
- References
- 88
- FWCI
- 9.06
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.