13,377 research outputs found

    The Dark Energy Survey: Cosmology Results With ~1500 New High-redshift Type Ia Supernovae Using The Full 5-year Dataset

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    International audienceWe present cosmological constraints from the sample of Type Ia supernovae (SN Ia) discovered during the full five years of the Dark Energy Survey (DES) Supernova Program. In contrast to most previous cosmological samples, in which SN are classified based on their spectra, we classify the DES SNe using a machine learning algorithm applied to their light curves in four photometric bands. Spectroscopic redshifts are acquired from a dedicated follow-up survey of the host galaxies. After accounting for the likelihood of each SN being a SN Ia, we find 1635 DES SN in the redshift range 0.100.50.100.5 SNe compared to the previous leading compilation of Pantheon+, and results in the tightest cosmological constraints achieved by any SN data set to date. To derive cosmological constraints we combine the DES supernova data with a high-quality external low-redshift sample consisting of 194 SNe Ia spanning 0.025<z<0.100.025<z<0.10. Using SN data alone and including systematic uncertainties we find ΩM=0.352±0.017\Omega_{\rm M}=0.352\pm 0.017 in a flat Λ\LambdaCDM model, and (ΩM,w)=(0.264−0.096+0.074,−0.80−0.16+0.14)(\Omega_{\rm M},w)=(0.264^{+0.074}_{-0.096},-0.80^{+0.14}_{-0.16}) in a flat wwCDM model. For a flat w0waw_0w_aCDM model, we find (ΩM,w0,wa)=(0.495−0.043+0.033,−0.36−0.30+0.36,−8.8−4.5+3.7)(\Omega_{\rm M},w_0,w_a)=(0.495^{+0.033}_{-0.043},-0.36^{+0.36}_{-0.30},-8.8^{+3.7}_{-4.5}), consistent with a constant equation of state to within ∌2σ\sim2 \sigma. Including Planck CMB data, SDSS BAO data, and DES 3×23\times2-point data gives (ΩM,w)=(0.321±0.007,−0.941±0.026)(\Omega_{\rm M},w)=(0.321\pm0.007,-0.941\pm0.026). In all cases dark energy is consistent with a cosmological constant to within ∌2σ\sim2\sigma. In our analysis, systematic errors on cosmological parameters are subdominant compared to statistical errors; these results thus pave the way for future photometrically classified supernova analyses

    Instructional Decision Making in a Gateway Quantitative Reasoning Course

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    Many educators and professional organizations recommend Quantitative Reasoning as the best entry-level postsecondary mathematics course for non-STEM majors. However, novice and veteran instructors who have no prior experience in teaching a QR course often express their ignorance of the content to choose for this course, the instruction to offer students, and the assessments to measure student learning. We conducted a case study to investigate the initial implementation of an entry-level university quantitative reasoning course during fall semester, 2018. The participants were the course instructor and students. We examined the instructor’s motives and actions and the students’ responses to the course. The instructor had no prior experience teaching a QR course but did have 15 years of experience teaching student-centered mathematics. Data included course artifacts, class observations, an instructor interview, and students’ written reflections. Because this was a new course—and to adapt to student needs—the instructor employed his instructional autonomy and remained flexible in designing and enacting the course content, instruction, and assessment. His instructional decision making and flexible approach helped the instructor tailor the learning activities and teaching practices to the needs and interests of the students. The students generally appreciated and benefited from this approach, enjoyed the course, and provided positive remarks about the instructors’ practices

    The Dark Energy Survey: Cosmology Results With ~1500 New High-redshift Type Ia Supernovae Using The Full 5-year Dataset

    No full text
    International audienceWe present cosmological constraints from the sample of Type Ia supernovae (SN Ia) discovered during the full five years of the Dark Energy Survey (DES) Supernova Program. In contrast to most previous cosmological samples, in which SN are classified based on their spectra, we classify the DES SNe using a machine learning algorithm applied to their light curves in four photometric bands. Spectroscopic redshifts are acquired from a dedicated follow-up survey of the host galaxies. After accounting for the likelihood of each SN being a SN Ia, we find 1635 DES SN in the redshift range 0.100.50.100.5 SNe compared to the previous leading compilation of Pantheon+, and results in the tightest cosmological constraints achieved by any SN data set to date. To derive cosmological constraints we combine the DES supernova data with a high-quality external low-redshift sample consisting of 194 SNe Ia spanning 0.025<z<0.100.025<z<0.10. Using SN data alone and including systematic uncertainties we find ΩM=0.352±0.017\Omega_{\rm M}=0.352\pm 0.017 in a flat Λ\LambdaCDM model, and (ΩM,w)=(0.264−0.096+0.074,−0.80−0.16+0.14)(\Omega_{\rm M},w)=(0.264^{+0.074}_{-0.096},-0.80^{+0.14}_{-0.16}) in a flat wwCDM model. For a flat w0waw_0w_aCDM model, we find (ΩM,w0,wa)=(0.495−0.043+0.033,−0.36−0.30+0.36,−8.8−4.5+3.7)(\Omega_{\rm M},w_0,w_a)=(0.495^{+0.033}_{-0.043},-0.36^{+0.36}_{-0.30},-8.8^{+3.7}_{-4.5}), consistent with a constant equation of state to within ∌2σ\sim2 \sigma. Including Planck CMB data, SDSS BAO data, and DES 3×23\times2-point data gives (ΩM,w)=(0.321±0.007,−0.941±0.026)(\Omega_{\rm M},w)=(0.321\pm0.007,-0.941\pm0.026). In all cases dark energy is consistent with a cosmological constant to within ∌2σ\sim2\sigma. In our analysis, systematic errors on cosmological parameters are subdominant compared to statistical errors; these results thus pave the way for future photometrically classified supernova analyses

    The Dark Energy Survey Supernova Program: Cosmological Analysis and Systematic Uncertainties

    No full text
    International audienceWe present the full Hubble diagram of photometrically-classified Type Ia supernovae (SNe Ia) from the Dark Energy Survey supernova program (DES-SN). DES-SN discovered more than 20,000 SN candidates and obtained spectroscopic redshifts of 7,000 host galaxies. Based on the light-curve quality, we select 1635 photometrically-identified SNe Ia with spectroscopic redshift 0.100.50.5 supernovae by a factor of five. In a companion paper, we present cosmological results of the DES-SN sample combined with 194 spectroscopically-classified SNe Ia at low redshift as an anchor for cosmological fits. Here we present extensive modeling of this combined sample and validate the entire analysis pipeline used to derive distances. We show that the statistical and systematic uncertainties on cosmological parameters are σΩM,stat+sysΛCDM=\sigma_{\Omega_M,{\rm stat+sys}}^{\Lambda{\rm CDM}}=0.017 in a flat Λ\LambdaCDM model, and (σΩM,σw)stat+syswCDM=(\sigma_{\Omega_M},\sigma_w)_{\rm stat+sys}^{w{\rm CDM}}=(0.082, 0.152) in a flat wwCDM model. Combining the DES SN data with the highly complementary CMB measurements by Planck Collaboration (2020) reduces uncertainties on cosmological parameters by a factor of 4. In all cases, statistical uncertainties dominate over systematics. We show that uncertainties due to photometric classification make up less than 10% of the total systematic uncertainty budget. This result sets the stage for the next generation of SN cosmology surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time

    The Dark Energy Survey Supernova Program: Cosmological Analysis and Systematic Uncertainties

    No full text
    International audienceWe present the full Hubble diagram of photometrically-classified Type Ia supernovae (SNe Ia) from the Dark Energy Survey supernova program (DES-SN). DES-SN discovered more than 20,000 SN candidates and obtained spectroscopic redshifts of 7,000 host galaxies. Based on the light-curve quality, we select 1635 photometrically-identified SNe Ia with spectroscopic redshift 0.100.50.5 supernovae by a factor of five. In a companion paper, we present cosmological results of the DES-SN sample combined with 194 spectroscopically-classified SNe Ia at low redshift as an anchor for cosmological fits. Here we present extensive modeling of this combined sample and validate the entire analysis pipeline used to derive distances. We show that the statistical and systematic uncertainties on cosmological parameters are σΩM,stat+sysΛCDM=\sigma_{\Omega_M,{\rm stat+sys}}^{\Lambda{\rm CDM}}=0.017 in a flat Λ\LambdaCDM model, and (σΩM,σw)stat+syswCDM=(\sigma_{\Omega_M},\sigma_w)_{\rm stat+sys}^{w{\rm CDM}}=(0.082, 0.152) in a flat wwCDM model. Combining the DES SN data with the highly complementary CMB measurements by Planck Collaboration (2020) reduces uncertainties on cosmological parameters by a factor of 4. In all cases, statistical uncertainties dominate over systematics. We show that uncertainties due to photometric classification make up less than 10% of the total systematic uncertainty budget. This result sets the stage for the next generation of SN cosmology surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time

    Content analysis of EU Local Climate Adaptation Plans and Strategies

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    This dataset contains information on the characteristics of Local Climate Adaptation Plans in Europe. A sample of 328 medium- to large-sized cities across the formerly EU-28 was investigated for the availability of Local Climate Plans and strategies on climate change adaptation. A set of 168 cities out of the 328 were identified to have at least one, if not more Local Climate Adaptation Plans. The contents of these plans were documented, using an elaborated framework combining indicators of state-of-the -art plan quality principles with indicators of justice/ equity theory. Used common plan quality principles are 1) Fact base - Climate change impact, risk and vulnerability assessment (related to risk, sectors, justice), 2) Adaptation goals (related to risk, quantitative); 3) Adaptation measures (distributed across 12 sectors, justice); 4) Implementation process (prioritization, responsibility, timeframe) &amp; tools (budget); 5) Monitoring &amp; evaluation (responsibility, timeframe, justice). Additionally to the information on these 5 plan quality principles information on the (potential) participation process, a communication strategy, the national and regional context, as well as with access information, access data, access type, and other meta data were retrieved and documented. The publication dates of the plans range from 2005 - 2020. The collection period ranges from March 2019 to June 2021, depending on the country and city, with the majority collected between May 2019 and June 2020

    SN 2023ixf in Messier 101: a variable red supergiant as the progenitor candidate to a type II supernova

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    We present pre-explosion optical and infrared (IR) imaging at the site of the type II supernova (SN II) 2023ixf in Messier 101 at 6.9 Mpc. We astrometrically registered a ground-based image of SN 2023ixf to archival Hubble Space Telescope (HST), Spitzer Space Telescope (Spitzer), and ground-based near-IR images. A single point source is detected at a position consistent with the SN at wavelengths ranging from HST RR-band to Spitzer 4.5 ÎŒ\mum. Fitting to blackbody and red supergiant (RSG) spectral-energy distributions (SEDs), we find that the source is anomalously cool with a significant mid-IR excess. We interpret this SED as reprocessed emission in a 8600 R⊙R_{\odot} circumstellar shell of dusty material with a mass ∌\sim5×10−5M⊙\times10^{-5} M_{\odot} surrounding a log⁥(L/L⊙)=4.74±0.07\log(L/L_{\odot})=4.74\pm0.07 and Teff=3920+200−160T_{\rm eff}=3920\substack{+200\\-160} K RSG. This luminosity is consistent with RSG models of initial mass 11 M⊙M_{\odot}, depending on assumptions of rotation and overshooting. In addition, the counterpart was significantly variable in pre-explosion Spitzer 3.6 ÎŒ\mum and 4.5 ÎŒ\mum imaging, exhibiting ∌\sim70% variability in both bands correlated across 9 yr and 29 epochs of imaging. The variations appear to have a timescale of 2.8 yr, which is consistent with Îș\kappa-mechanism pulsations observed in RSGs, albeit with a much larger amplitude than RSGs such as α\alpha Orionis (Betelgeuse).Comment: 14 pages, 5 figures, submitted to ApJL, comments welcom

    Leave-one-out sensitivity analysis for sensitivity attributes.

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    Leave-one-out sensitivity analysis showing how many marine mammal stocks changed climate vulnerability score when a given sensitivity attribute was omitted. Home range did not change any vulnerability scores when omitted.</p
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