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| 2. |
- Rigault, M., et al.
(author)
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Strong dependence of Type Ia supernova standardization on the local specific star formation rate
- 2020
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In: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 644
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Journal article (peer-reviewed)abstract
- As part of an on-going effort to identify, understand and correct for astrophysics biases in the standardization of Type Ia supernovae (SN Ia) for cosmology, we have statistically classified a large sample of nearby SNe Ia into those that are located in predominantly younger or older environments. This classification is based on the specific star formation rate measured within a projected distance of 1 kpc from each SN location (LsSFR). This is an important refinement compared to using the local star formation rate directly, as it provides a normalization for relative numbers of available SN progenitors and is more robust against extinction by dust. We find that the SNe Ia in predominantly younger environments are ΔY = 0.163 ± 0.029 mag (5.7σ) fainter than those in predominantly older environments after conventional light-curve standardization. This is the strongest standardized SN Ia brightness systematic connected to the host-galaxy environment measured to date. The well-established step in standardized brightnesses between SNe Ia in hosts with lower or higher total stellar masses is smaller, at ΔM = 0.119 ± 0.032 mag (4.5σ), for the same set of SNe Ia. When fit simultaneously, the environment-age offset remains very significant, with ΔY = 0.129 ± 0.032 mag (4.0σ), while the global stellar mass step is reduced to ΔM = 0.064 ± 0.029 mag (2.2σ). Thus, approximately 70% of the variance from the stellar mass step is due to an underlying dependence on environment-based progenitor age. Also, we verify that using the local star formation rate alone is not as powerful as LsSFR at sorting SNe Ia into brighter and fainter subsets. Standardization that only uses the SNe Ia in younger environments reduces the total dispersion from 0.142 ± 0.008 mag to 0.120 ± 0.010 mag. We show that as environment-ages evolve with redshift, a strong bias, especially on the measurement of the derivative of the dark energy equation of state, can develop. Fortunately, data that measure and correct for this effect using our local specific star formation rate indicator, are likely to be available for many next-generation SN Ia cosmology experiments.
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| 3. |
- Léget, P-F, et al.
(author)
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SUGAR : An improved empirical model of Type Ia supernovae based on spectral features
- 2020
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In: Astronomy and Astrophysics. - : EDP Sciences. - 0004-6361 .- 1432-0746. ; 636
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Journal article (peer-reviewed)abstract
- Context. Type Ia supernovae (SNe Ia) are widely used to measure the expansion of the Universe. Improving distance measurements of SNe Ia is one technique to better constrain the acceleration of expansion and determine its physical nature.Aims. This document develops a new SNe Ia spectral energy distribution (SED) model, called the SUpernova Generator And Reconstructor (SUGAR), which improves the spectral description of SNe Ia, and consequently could improve the distance measurements.Methods. This model was constructed from SNe Ia spectral properties and spectrophotometric data from the Nearby Supernova Factory collaboration. In a first step, a principal component analysis-like method was used on spectral features measured at maximum light, which allowed us to extract the intrinsic properties of SNe Ia. Next, the intrinsic properties were used to extract the average extinction curve. Third, an interpolation using Gaussian processes facilitated using data taken at different epochs during the lifetime of an SN Ia and then projecting the data on a fixed time grid. Finally, the three steps were combined to build the SED model as a function of time and wavelength. This is the SUGAR model.Results. The main advancement in SUGAR is the addition of two additional parameters to characterize SNe Ia variability. The first is tied to the properties of SNe Ia ejecta velocity and the second correlates with their calcium lines. The addition of these parameters, as well as the high quality of the Nearby Supernova Factory data, makes SUGAR an accurate and efficient model for describing the spectra of normal SNe Ia as they brighten and fade.Conclusions. The performance of this model makes it an excellent SED model for experiments like the Zwicky Transient Facility, the Large Synoptic Survey Telescope, or the Wide Field Infrared Survey Telescope.
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| 4. |
- Broekman, Maarten J. E., et al.
(author)
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Evaluating expert-based habitat suitability information of terrestrial mammals with GPS-tracking data
- 2022
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In: Global Ecology and Biogeography. - : Wiley. - 1466-822X .- 1466-8238. ; 31:8, s. 1526-1541
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Journal article (peer-reviewed)abstract
- Aim: Macroecological studies that require habitat suitability data for many species often derive this information from expert opinion. However, expert-based information is inherently subjective and thus prone to errors. The increasing availability of GPS tracking data offers opportunities to evaluate and supplement expert-based information with detailed empirical evidence. Here, we compared expert-based habitat suitability information from the International Union for Conservation of Nature (IUCN) with habitat suitability information derived from GPS-tracking data of 1,498 individuals from 49 mammal species.Location: Worldwide.Time period: 1998-2021.Major taxa studied: Forty-nine terrestrial mammal species.Methods: Using GPS data, we estimated two measures of habitat suitability for each individual animal: proportional habitat use (proportion of GPS locations within a habitat type), and selection ratio (habitat use relative to its availability). For each individual we then evaluated whether the GPS-based habitat suitability measures were in agreement with the IUCN data. To that end, we calculated the probability that the ranking of empirical habitat suitability measures was in agreement with IUCN's classification into suitable, marginal and unsuitable habitat types.Results: IUCN habitat suitability data were in accordance with the GPS data (> 95% probability of agreement) for 33 out of 49 species based on proportional habitat use estimates and for 25 out of 49 species based on selection ratios. In addition, 37 and 34 species had a > 50% probability of agreement based on proportional habitat use and selection ratios, respectively.Main conclusions: We show how GPS-tracking data can be used to evaluate IUCN habitat suitability data. Our findings indicate that for the majority of species included in this study, it is appropriate to use IUCN habitat suitability data in macroecological studies. Furthermore, we show that GPS-tracking data can be used to identify and prioritize species and habitat types for re-evaluation of IUCN habitat suitability data.
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| 5. |
- Hlozek, R., et al.
(author)
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Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)
- 2023
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In: Astrophysical Journal Supplement Series. - 0067-0049 .- 1538-4365. ; 267:2
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Journal article (peer-reviewed)abstract
- Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory (Rubin) will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we developed the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition that aimed to catalyze the development of robust classifiers under LSST-like conditions of a nonrepresentative training set for a large photometric test set of imbalanced classes. Over 1000 teams participated in PLAsTiCC, which was hosted in the Kaggle data science competition platform between 2018 September 28 and 2018 December 17, ultimately identifying three winners in 2019 February. Participants produced classifiers employing a diverse set of machine-learning techniques including hybrid combinations and ensemble averages of a range of approaches, among them boosted decision trees, neural networks, and multilayer perceptrons. The strong performance of the top three classifiers on Type Ia supernovae and kilonovae represent a major improvement over the current state of the art within astronomy. This paper summarizes the most promising methods and evaluates their results in detail, highlighting future directions both for classifier development and simulation needs for a next-generation PLAsTiCC data set.
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| 6. |
- Williams, S. C., et al.
(author)
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See Change : VLT spectroscopy of a sample of high-redshift Type Ia supernova host galaxies
- 2020
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In: Monthly notices of the Royal Astronomical Society. - : Oxford University Press (OUP). - 0035-8711 .- 1365-2966. ; 495:4, s. 3859-3880
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Journal article (peer-reviewed)abstract
- The Supernova Cosmology Project has conducted the 'See Change' programme, aimed at discovering and observing high-redshift (1.13 <= z <= 1.75) Type Ia supernovae (SNe Ia). We used multifilter Hubble Space Telescope (HST) observations of massive galaxy clusters with sufficient cadence to make the observed SN Ia light curves suitable for a cosmological probe of dark energy at z > 0.5. This See Change sample of SNe Ia with multi-colour light curves will be the largest to date at these redshifts. As part of the See Change programme, we obtained ground-based spectroscopy of each discovered transient and/or its host galaxy. Here, we present Very Large Telescope (VCT) spectra of See Change transient host galaxies, deriving their redshifts, and host parameters such as stellar mass and star formation rate. Of the 39 See Change transients/hosts that were observed with the VLT, we successfully determined the redshift for 26, including 15 SNe Ia at z > 0.97. We show that even in passive environments, it is possible to recover secure redshifts for the majority of SN hosts out to z = 1.5. We find that with typical exposure times of 3-4h on an 8-m-class telescope we can recover similar to 75 per cent of SN Ia redshifts in the range of 0.97 < z < 1.5. Furthermore, we show that the combination of HST photometry and VLT spectroscopy is able to provide estimates of host galaxy stellar mass that are sufficiently accurate for use in a mass-step correction in the cosmological analysis.
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| 7. |
- Rodriguez-Antona, C, et al.
(author)
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PharmVar GeneFocus: CYP3A5
- 2022
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In: Clinical pharmacology and therapeutics. - 1532-6535.
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Journal article (peer-reviewed)
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| 8. |
- Sheese, Patrick E., et al.
(author)
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Assessment of the quality of ACE-FTS stratospheric ozone data
- 2022
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In: Atmospheric Measurement Techniques. - : Copernicus GmbH. - 1867-1381 .- 1867-8548. ; 15:5, s. 1233-1249
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Journal article (peer-reviewed)abstract
- For the past 17 years, the Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) instrument on the Canadian SCISAT satellite has been measuring profiles of atmospheric ozone. The latest operational versions of the level 2 ozone data are versions 3.6 and 4.1. This study characterizes how both products compare with correlative data from other limb-sounding satellite instruments, namely MAESTRO, MLS, OSIRIS, SABER, and SMR. In general, v3.6, with respect to the other instruments, exhibits a smaller bias (which is on the order of similar to 3 %) in the middle stratosphere than v4.1 (similar to 2 %-9 %); however, the bias exhibited in the v4.1 data tends to be more stable, i.e. not changing significantly over time in any altitude region. In the lower stratosphere, v3.6 has a positive bias of about 3 %-5 % that is stable to within +/- 1 % per decade, and v4.1 has a bias on the order of -1 % to +5 % and is also stable to within +/- 1 % per decade. In the middle stratosphere, v3.6 has a positive bias of similar to 3 % with a significant negative drift on the order of 0.5 %-2.5 % per decade, and v4.1 has a positive bias of 2 %-9 % that is stable to within +/- 0.5 % per decade. In the upper stratosphere, v3.6 has a positive bias that increases with altitude up to similar to 16 % and a significant negative drift on the order of 2 %-3 % per decade, and v4.1 has a positive bias that increases with altitude up to similar to 15 % and is stable to within +/- 1 % per decade. Estimates indicate that both versions 3.6 and 4.1 have precision values on the order of 0.1-0.2 ppmv below 20 km and above 45 km (similar to 5 %-10 %, depending on altitude). Between 20 and 45 km, the estimated v3.6 precision of similar to 4 %-6 % is better than the estimated v4.1 precision of similar to 6 %-10 %.
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