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The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will discover an unprecedented number of supernovae (SNe), making spectroscopic classification for all the events infeasible. LSST will thus rely on photometric classification, whose accuracy depends on the not-yet-finalized LSST observing strategy. In this work, we analyze the impact of cadence choices on classification performance using simulated multiband light curves. First, we simulate SNe with an LSST baseline cadence, a nonrolling cadence, and a presto-color cadence, which observes each sky location three times per night instead of twice. Each simulated data set includes a spectroscopically confirmed training set, which we augment to be representative of the test set as part of the classification pipeline. Then we use the photometric transient classification library snmachine to build classifiers. We find that the active region of the rolling cadence used in the baseline observing strategy yields a 25% improvement in classification performance relative to the background region. This improvement in performance in the actively rolling region is also associated with an increase of up to a factor of 2.7 in the number of cosmologically useful Type Ia SNe relative to the background region. However, adding a third visit per night as implemented in presto-color degrades classification performance due to more irregularly sampled light curves. Overall, our results establish desiderata on the observing cadence related to classification of full SNe light curves, which in turn impacts photometric SNe cosmology with LSST.

Future constraints of cosmological parameters from Type Ia supernovae (SNe Ia) will depend on the use of photometric samples, those samples without spectroscopic measurements of the SNe Ia. There is a growing number of analyses that show that photometric samples can be utilised for precision cosmological studies with minimal systematic uncertainties. To investigate this claim, we perform the first analysis that combines two separate photometric samples, SDSS and Pan-STARRS, without including a low-redshift anchor. We evaluate the consistency of the cosmological parameters from these two samples and find they are consistent with each other to under $1\sigma$. From the combined sample, named Amalgame, we measure $\Omega_M = 0.328 \pm 0.024$ with SN alone in a flat $\Lambda$CDM model, and $\Omega_M = 0.330 \pm 0.018$ and $w = -1.016^{+0.055}_{-0.058}$ when combining with a Planck data prior and a flat $w$CDM model. These results are consistent with constraints from the Pantheon+ analysis of only spectroscopically confirmed SNe Ia, and show that there are no significant impediments to analyses of purely photometric samples of SNe Ia., Comment: Submitting to MNRAS; comments welcome

Bayesian Estimation Applied to Multiple Species (BEAMS) is implemented in the BEAMS with Bias Corrections (BBC) framework to produce a redshift-binned Hubble diagram (HD) for Type Ia supernovae (SNe Ia). BBC corrects for selection effects and non-SNIa contamination, and systematic uncertainties are described by a covariance matrix with dimension matching the number of BBC redshift bins. For spectroscopically confirmed SN Ia samples, a recent "Binning is Sinning" article (BHS21, arxiv:2012.05900) showed that an unbinned HD and covariance matrix reduces the systematic uncertainty by a factor of ~1.5 compared to the binned approach. Here we extend their analysis to obtain an unbinned HD for a photometrically identified sample processed with BBC. To test this new method, we simulate and analyze 50 samples corresponding to the Dark Energy Survey (DES) witha low-redshift anchor; the simulation includes SNe Ia, and contaminants from core-collapse SNe and peculiar SNe Ia. The analysis includes systematic uncertainties for calibration, and measures the dark energy equation of state parameter (w). Compared to a redshift-binned HD, the unbinned HD with nearly 2000 events results in a smaller systematic uncertainty, in qualitative agreement with BHS21, and averaging results among the 50 samples we find no evidence for a w-bias. To reduce computation time for fitting an unbinned HD with large samples, we propose an HD-rebinning method that defines the HD in bins of redshift, color, and stretch; the rebinned HD results in similar uncertainty as the unbinned case, and shows no evidence for a w-bias.

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will discover an unprecedented number of supernovae (SNe), making spectroscopic classification for all the events infeasible. LSST will thus rely on photometric classification, whose accuracy depends on the not-yet-finalized LSST observing strategy. In this work, we analyze the impact of cadence choices on classification performance using simulated multi-band light curves. First, we simulate SNe with an LSST baseline cadence, a non-rolling cadence, and a presto-color cadence which observes each sky location three times per night instead of twice. Each simulated dataset includes a spectroscopically-confirmed training set, which we augment to be representative of the test set as part of the classification pipeline. Then, we use the photometric transient classification library snmachine to build classifiers. We find that the active region of the rolling cadence used in the baseline observing strategy yields a 25% improvement in classification performance relative to the background region. This improvement in performance in the actively-rolling region is also associated with an increase of up to a factor of 2.7 in the number of cosmologically-useful Type Ia supernovae relative to the background region. However, adding a third visit per night as implemented in presto-color degrades classification performance due to more irregularly sampled light curves. Overall, our results establish desiderata on the observing cadence related to classification of full SNe light curves, which in turn impacts photometric SNe cosmology with LSST., Comment: 22 pages, 14 figures. Changed to match version accepted by the Astrophysical Journal Supplement Series (accepted 06/02/2023)

We perform a rigorous cosmology analysis on simulated type Ia supernovae (SN~Ia) and evaluate the improvement from including photometric host-galaxy redshifts compared to using only the "zspec" subset with spectroscopic redshifts from the host or SN. We use the Deep Drilling Fields (~50 deg^2) from the Photometric LSST Astronomical Time-Series Classification Challenge (PLaSTiCC), in combination with a low-z sample based on Data Challenge2 (DC2). The analysis includes light curve fitting to standardize the SN brightness, a high-statistics simulation to obtain a bias-corrected Hubble diagram, a statistical+systematics covariance matrix including calibration and photo-z uncertainties, and cosmology fitting with a prior from the cosmic microwave background. Compared to using the zspec subset, including events with SN+host photo-z results in i) more precise distances for z>0.5, ii) a Hubble diagram that extends 0.3 further in redshift, and iii) a 50 % increase in the Dark Energy Task Force figure of merit (FoM) based on the w0-wa CDM model. Analyzing 25 simulated data samples, the average bias on w0 and wa is consistent with zero. The host photo-z systematic of 0.01 reduces FoM by only 2 % because i) most z<0.5 events are in the zspec subset, ii) the combined SN+host photo-z has X 2 smaller bias, and iii) the anti-correlation between fitted redshift and color self corrects distance errors. To prepare for analysing real data, the next SNIa-cosmology analysis with photo-z's should include non SN-Ia contamination and host galaxy mis-associations., Comment: 13 pages, 9 figures, 7 tables. Submitted to ApJ

As we observe a rapidly growing number of astrophysical transients, we learn more about the diverse host galaxy environments in which they occur. Host galaxy information can be used to purify samples of cosmological Type Ia supernovae, uncover the progenitor systems of individual classes, and facilitate low-latency follow-up of rare and peculiar explosions. In this work, we develop a novel data-driven methodology to simulate the time-domain sky that includes detailed modeling of the probability density function for multiple transient classes conditioned on host galaxy magnitudes, colours, star formation rates, and masses. We have designed these simulations to optimize photometric classification and analysis in upcoming large synoptic surveys. We integrate host galaxy information into the SNANA simulation framework to construct the Simulated Catalogue of Optical Transients and Correlated Hosts (SCOTCH), a publicly-available catalogue of 5 million idealized transient light curves in LSST passbands and their host galaxy properties over the redshift range $0

We present results from simulating slitless spectroscopic observations with the Nancy Grace Roman Space Telescope's (Roman) Wide-Field Instrument (WFI) P127 prism spanning 0.75 $\mu m$ to 1.8 $\mu m$. We quantify the efficiency of recovered Type Ia supernovae (SNe Ia) redshifts, as a function of P127 prism exposure time, to guide planning for future observing programs with the Roman prism. Generating the two-dimensional dispersed images and extracting one-dimensional spectra is done with the slitless spectroscopy package pyLINEAR along with custom-written software. From the analysis of 1698 simulated SN Ia P127 prism spectra, we show the efficiency of recovering SN redshifts to $z\lesssim3.0$, highlighting the exceptional sensitivity of the Roman P127 prism. Redshift recovery is assessed by setting a requirement of $\sigma_z = (\left|z - z_\mathrm{true} \right|)/(1+z) \leq 0.01$. We find that 3 hr exposures are sufficient for meeting this requirement, for $\gtrsim 50\%$ of the sample of mock SNe Ia at $z\approx2$ and within $\pm5$ days of rest-frame maximum light in the optical. We also show that a 1 hr integration of Roman can achieve the same precision in completeness to a depth of $24.4 \pm 0.06$ AB mag (or $z\lesssim 1$). Implications for cosmological studies with Roman P127 prism spectra of SNe Ia are also discussed., Comment: ApJ accepted. Updated to match accepted version

We examine the redshifts of a comprehensive set of published Type Ia supernovae, and provide a combined, improved catalog with updated redshifts. We improve on the original catalogs by using the most up-to-date heliocentric redshift data available; ensuring all redshifts have uncertainty estimates; using the exact formulae to convert heliocentric redshifts into the Cosmic Microwave Background (CMB) frame; and utilizing an improved peculiar velocity model that calculates local motions in redshift-space and more realistically accounts for the external bulk flow at high-redshifts. In total we reviewed 2821 supernova redshifts; 534 are comprised of repeat-observations of the same supernovae and 1764 pass the cosmology sample quality cuts. We found 5 cases of missing or incorrect heliocentric corrections, 44 incorrect or missing supernova coordinates, 230 missing heliocentric or CMB frame redshifts, and 1200 missing redshift uncertainties. Of the 2287 unique Type Ia supernovae in our sample (1594 of which satisfy cosmology-sample cuts) we updated 990 heliocentric redshifts. The absolute corrections range between $10^{-8} \leq \Delta z \leq 0.038$, and RMS$(\Delta z) \sim 3\times 10^{-3}$. The sign of the correction was essentially random, so the mean and median corrections are small: $4\times 10^{-4}$ and $4\times 10^{-6}$ respectively. We examine the impact of these improvements for $H_0$ and the dark energy equation of state $w$ and find that the cosmological results change by $\Delta H_0 = -0.11$ km s$^{-1}$ Mpc$^{-1}$ and $\Delta w = -0.001$, both significantly smaller than previously reported uncertainties for $H_0$ of 1.4 km s$^{-1}$ Mpc$^{-1}$ and $w$ of 0.04 respectively., Comment: 29 pages, 13 figures, 7 tables. Submitted to ApJ. Full data release on acceptance

Recent studies have shown that the observed colour distributions of Type Ia SNe (SNIa) are well-described by a combination of distributions from dust and intrinsic colour. Here we present a new forward-modeling fitting method (Dust2Dust) to measure the parent dust and colour distributions, including their dependence on host-galaxy mass. At each fit step, the SNIa selection efficiency is determined from a large simulated sample that is re-weighted to reflect the proposed distributions. We use five separate metrics to constrain the Dust2Dust parameters: distribution of fitted light-curve colour $c$, cosmological residual trends with $c$, cosmological residual scatter with $c$, fitted colour-luminosity relationship $\beta_{\rm SALT2}$, and intrinsic scatter $\sigma_{\rm int}$. Using the Pantheon+ data sample, we present results for a Dust2Dust fit that includes 4 parameters describing intrinsic colour variations and 8 parameters describing dust. Furthermore, we propagate the Dust2Dust parameter uncertainties and covariance to the dark energy equation-of-state $w$ and Hubble constant H$_0$: we find $\sigma_w = 0.005$ and $\sigma_{\textrm{H}_0} = 0.145~$km/s/Mpc. The Dust2Dust code is publically available.

On 2019 August 14 at 21:10:39 UTC, the LIGO/Virgo Collaboration (LVC) detected a possible neutron star-black hole merger (NSBH), the first ever identified. An extensive search for an optical counterpart of this event, designated GW190814, was undertaken using DECam on the CTIO Blanco 4-m telescope. Target of opportunity interrupts were issued on 8 separate nights to observe 11 candidates using the SOAR Goodman Spectrograph in order to assess whether any of these transients was likely to be an optical counterpart of the possible NSBH merger. Here, we describe the process of observing with the SOAR Goodman spectrograph, the analysis of our spectra, our spectroscopic typing methodology, and our resultant conclusion that none of the candidates corresponded to the black hole-neutron star merger but were all instead other transient events. Finally, we describe the lessons learned from this effort. Application of these lessons will be critical for a successful community spectroscopic follow-up program for LVC season 4 (O4) and beyond., Comment: 45 pages, 18 figures, submitted to ApJ

Recent studies have shown that the observed colour distributions of Type Ia SNe (SNIa) are well-described by a combination of distributions from dust and intrinsic colour. Here we present a new forward-modeling fitting method (Dust2Dust) to measure the parent dust and colour distributions, including their dependence on host-galaxy mass. At each fit step, the SNIa selection efficiency is determined from a large simulated sample that is re-weighted to reflect the proposed distributions. We use five separate metrics to constrain the Dust2Dust parameters: distribution of fitted light-curve colour $c$, cosmological residual trends with $c$, cosmological residual scatter with $c$, fitted colour-luminosity relationship $\beta_{\rm SALT2}$, and intrinsic scatter $\sigma_{\rm int}$. Using the Pantheon+ data sample, we present results for a Dust2Dust fit that includes 4 parameters describing intrinsic colour variations and 8 parameters describing dust. Furthermore, we propagate the Dust2Dust parameter uncertainties and covariance to the dark energy equation-of-state $w$ and Hubble constant H$_0$: we find $\sigma_w = 0.005$ and $\sigma_{\textrm{H}_0} = 0.145~$km/s/Mpc. The Dust2Dust code is publically available.

We examine the redshifts of a comprehensive set of published Type Ia supernovae, and provide a combined, improved catalogue with updated redshifts. We improve on the original catalogues by using the most up-to-date heliocentric redshift data available; ensuring all redshifts have uncertainty estimates; using the exact formulae to convert heliocentric redshifts into the Cosmic Microwave Background (CMB) frame; and utilising an improved peculiar velocity model that calculates local motions in redshift-space and more realistically accounts for the external bulk flow at high-redshifts. In total we reviewed 2821 supernova redshifts; 534 are comprised of repeat-observations of the same supernovae and 1764 pass the cosmology sample quality cuts. We found 5 cases of missing or incorrect heliocentric corrections, 44 incorrect or missing supernova coordinates, 230 missing heliocentric or CMB frame redshifts, and 1200 missing redshift uncertainties. Of the 2287 unique Type Ia supernovae in our sample (1594 of which satisfy cosmology-sample cuts) we updated 990 heliocentric redshifts. The absolute corrections range between $10^{-8} \leq \Delta z \leq 0.038$, and RMS$(\Delta z) \sim 3\times 10^{-3}$. The sign of the correction was essentially random, so the mean and median corrections are small: $4\times 10^{-4}$ and $4\times 10^{-6}$ respectively. We examine the impact of these improvements for $H_0$ and the dark energy equation of state $w$ and find that the cosmological results change by $\Delta H_0 = -0.11$ km s$^{-1}$ Mpc$^{-1}$ and $\Delta w = -0.001$, both significantly smaller than previously reported uncertainties for $H_0$ of 1.4 km s$^{-1}$ Mpc$^{-1}$ and $w$ of 0.04 respectively., Comment: 24 pages, 13 figures, 7 tables. Updated to match version published in PASA. Data and papers available at https://pantheonplussh0es.github.io

On 2019 August 14 at 21:10:39 UTC, the LIGO/Virgo Collaboration (LVC) detected a possible neutron star-black hole merger (NSBH), the first ever identified. An extensive search for an optical counterpart of this event, designated GW190814, was undertaken using the Dark Energy Camera (DECam) on the 4m Victor M. Blanco Telescope at the Cerro Tololo Inter-American Observatory. Target of Opportunity interrupts were issued on 8 separate nights to observe 11 candidates using the 4.1m Southern Astrophysical Research (SOAR) telescope's Goodman High Throughput Spectrograph in order to assess whether any of these transients was likely to be an optical counterpart of the possible NSBH merger. Here, we describe the process of observing with SOAR, the analysis of our spectra, our spectroscopic typing methodology, and our resultant conclusion that none of the candidates corresponded to the gravitational wave merger event but were all instead other transients. Finally, we describe the lessons learned from this effort. Application of these lessons will be critical for a successful community spectroscopic follow-up program for LVC observing run 4 (O4) and beyond., Comment: 32 pages, 18 figures, accepted for publication by ApJ

Improving the use of Type Ia supernovae (SNIa) as standard candles requires a better approach to incorporate the relationship between SNIa and the properties of their host galaxies. Using a spectroscopically-confirmed sample of $\sim$1600 SNIa, we develop the first empirical model of underlying populations for SNIa light-curve properties that includes their dependence on host-galaxy stellar mass. These populations are important inputs to simulations that are used to model selection effects and correct distance biases within the BEAMS with Bias Correction (BBC) framework. Here we improve BBC to also account for SNIa-host correlations, and we validate this technique on simulated data samples. We recover the input relationship between SNIa luminosity and host-galaxy stellar mass (the mass step, $\gamma$) to within 0.004 mags, which is a factor of 5 improvement over the previous method that results in a $\gamma$-bias of ${\sim}0.02$. We adapt BBC for a novel dust-based model of intrinsic brightness variations, which results in a greatly reduced mass step for data ($\gamma = 0.017 \pm 0.008$), and for simulations ($\gamma =0.006 \pm 0.007$). Analysing simulated SNIa, the biases on the dark energy equation-of-state, $w$, vary from $\Delta w = 0.006(5)$ to $0.010(5)$ with our new BBC method; these biases are significantly smaller than the $0.02(5)$ $w$-bias using previous BBC methods that ignore SNIa-host correlations.

Binary supermassive black holes (BSBHs) are expected to be a generic byproduct from hierarchical galaxy formation. The final coalescence of BSBHs is thought to be the loudest gravitational wave (GW) siren, yet no confirmed BSBH is known in the GW-dominated regime. While periodic quasars have been proposed as BSBH candidates, the physical origin of the periodicity has been largely uncertain. Here we report discovery of a periodicity (P=1607$\pm$7 days) at 99.95% significance (with a global p-value of ~$10^{-3}$ accounting for the look elsewhere effect) in the optical light curves of a redshift 1.53 quasar, SDSS J025214.67-002813.7. Combining archival Sloan Digital Sky Survey data with new, sensitive imaging from the Dark Energy Survey, the total ~20-yr time baseline spans ~4.6 cycles of the observed 4.4-yr (restframe 1.7-yr) periodicity. The light curves are best fit by a bursty model predicted by hydrodynamic simulations of circumbinary accretion disks. The periodicity is likely caused by accretion rate modulation by a milli-parsec BSBH emitting GWs, dynamically coupled to the circumbinary accretion disk. A bursty hydrodynamic variability model is statistically preferred over a smooth, sinusoidal model expected from relativistic Doppler boost, a kinematic effect proposed for PG1302-102. Furthermore, the frequency dependence of the variability amplitudes disfavors Doppler boost, lending independent support to the circumbinary accretion variability hypothesis. Given our detection rate of one BSBH candidate from circumbinary accretion variability out of 625 quasars, it suggests that future large, sensitive synoptic surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time may be able to detect hundreds to thousands of candidate BSBHs from circumbinary accretion with direct implications for Laser Interferometer Space Antenna., Comment: Accepted to MNRAS, 17 pages, 9 figures

Improvement in the precision of measurements of cosmological parameters with Type Ia Supernovae (SNIa) is expected to come from large photometrically identified (photometric) SN samples. Here we re-analyse the SDSS photometric SN sample, with roughly 700 high-quality, likely but unconfirmed SNIa light-curves, to develop new analysis tools aimed at evaluating systematic uncertainties on the dark energy equation-of-state parameter $w$. Since we require a spectroscopically measured host galaxy redshift for each SN, we determine the associated selection efficiency of host galaxies in order to simulate bias corrections. We determine that the mis-association rate of host galaxies is $0.6\%$; ignoring this effect in simulated bias corrections leads to a $w$-bias of $\Delta w = +0.0007$, where $w$ is evaluated from SNIa and priors from measurements of baryon acoustic oscillations and the cosmic microwave background. We assess the uncertainty in our modeling of the host galaxy selection efficiency and find the associated $w$ uncertainty to be $-0.0072$. Finally, we explore new core collapse (CC) models in simulated training samples and find that adjusting the CC luminosity distribution to be in agreement with previous Pan-STARRS analyses yields a better match to the SDSS data. The impact of ignoring this adjustment is $\Delta w = -0.0109$; the impact of replacing the new CC models with those used by Pan-STARRS is $\Delta w = -0.0028$. These systematic uncertainties are subdominant to the statistical constraints from the SDSS sample, but must be considered in future photometric analyses of large SN samples such as those from DES, LSST and WFIRST.

Improvement in the precision of measurements of cosmological parameters with Type Ia Supernovae (SNIa) is expected to come from large photometrically identified (photometric) SN samples. Here we re-analyse the SDSS photometric SN sample, with roughly 700 high-quality, likely but unconfirmed SNIa light-curves, to develop new analysis tools aimed at evaluating systematic uncertainties on the dark energy equation-of-state parameter $w$. Since we require a spectroscopically measured host galaxy redshift for each SN, we determine the associated selection efficiency of host galaxies in order to simulate bias corrections. We determine that the mis-association rate of host galaxies is $0.6\%$; ignoring this effect in simulated bias corrections leads to a $w$-bias of $\Delta w = +0.0007$, where $w$ is evaluated from SNIa and priors from measurements of baryon acoustic oscillations and the cosmic microwave background. We assess the uncertainty in our modeling of the host galaxy selection efficiency and find the associated $w$ uncertainty to be $-0.0072$. Finally, we explore new core collapse (CC) models in simulated training samples and find that adjusting the CC luminosity distribution to be in agreement with previous Pan-STARRS analyses yields a better match to the SDSS data. The impact of ignoring this adjustment is $\Delta w = -0.0109$; the impact of replacing the new CC models with those used by Pan-STARRS is $\Delta w = -0.0028$. These systematic uncertainties are subdominant to the statistical constraints from the SDSS sample, but must be considered in future photometric analyses of large SN samples such as those from DES, LSST and WFIRST.

This paper describes the data release of the Sloan Digital Sky Survey-II (SDSS-II) Supernova Survey conducted between 2005 and 2007. Light curves, spectra, classifications, and ancillary data are presented for 10,258 variable and transient sources discovered through repeat ugriz imaging of SDSS Stripe 82, a 300 deg(2) area along the celestial equator. This data release is comprised of all transient sources brighter than r similar or equal to 22.5 mag with no history of variability prior to 2004. Dedicated spectroscopic observations were performed on a subset of 889 transients, as well as spectra for thousands of transient host galaxies using the SDSS-III BOSS spectrographs. Photometric classifications are provided for the candidates with good multi-color light curves that were not observed spectroscopically, using host galaxy redshift information when available. From these observations, 4607 transients are either spectroscopically confirmed, or likely to be, supernovae, making this the largest sample of supernova candidates ever compiled. We present a new method for SN host-galaxy identification and derive host-galaxy properties including stellar masses, star formation rates, and the average stellar population ages from our SDSS multi-band photometry. We derive SALT2 distance moduli for a total of 1364 SN. Ia with spectroscopic redshifts as well as photometric redshifts for a further 624 purely photometric SN. Ia candidates. Using the spectroscopically confirmed subset of the three-year SDSS-II SN. Ia sample and assuming a flat.CDM cosmology, we determine Omega(M) = 0.315 +/- 0.093 (statistical error only) and detect a non-zero cosmological constant at 5.7 sigma.

The Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC) is an open data challenge to classify simulated astronomical time-series data in preparation for observations from the Large Synoptic Survey Telescope (LSST), which will achieve first light in 2019 and commence its 10-year main survey in 2022. LSST will revolutionize our understanding of the changing sky, discovering and measuring millions of time-varying objects. In this challenge, we pose the question: how well can we classify objects in the sky that vary in brightness from simulated LSST time-series data, with all its challenges of non-representativity? In this note we explain the need for a data challenge to help classify such astronomical sources and describe the PLAsTiCC data set and Kaggle data challenge, noting that while the references are provided for context, they are not needed to participate in the challenge., Comment: Research note to accompany the https://www.kaggle.com/c/PLAsTiCC-2018 challenge

The Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC) is an open data challenge to classify simulated astronomical time-series data in preparation for observations from the Large Synoptic Survey Telescope (LSST), which will achieve first light in 2019 and commence its 10-year main survey in 2022. LSST will revolutionize our understanding of the changing sky, discovering and measuring millions of time-varying objects. In this challenge, we pose the question: how well can we classify objects in the sky that vary in brightness from simulated LSST time-series data, with all its challenges of non-representativity? In this note we explain the need for a data challenge to help classify such astronomical sources and describe the PLAsTiCC data set and Kaggle data challenge, noting that while the references are provided for context, they are not needed to participate in the challenge., Comment: Research note to accompany the https://www.kaggle.com/c/PLAsTiCC-2018 challenge

The Large Synoptic Survey Telescope is designed to provide an unprecedented optical imaging dataset that will support investigations of our Solar System, Galaxy and Universe, across half the sky and over ten years of repeated observation. However, exactly how the LSST observations will be taken (the observing strategy or "cadence") is not yet finalized. In this dynamically-evolving community white paper, we explore how the detailed performance of the anticipated science investigations is expected to depend on small changes to the LSST observing strategy. Using realistic simulations of the LSST schedule and observation properties, we design and compute diagnostic metrics and Figures of Merit that provide quantitative evaluations of different observing strategies, analyzing their impact on a wide range of proposed science projects. This is work in progress: we are using this white paper to communicate to each other the relative merits of the observing strategy choices that could be made, in an effort to maximize the scientific value of the survey. The investigation of some science cases leads to suggestions for new strategies that could be simulated and potentially adopted. Notably, we find motivation for exploring departures from a spatially uniform annual tiling of the sky: focusing instead on different parts of the survey area in different years in a "rolling cadence" is likely to have significant benefits for a number of time domain and moving object astronomy projects. The communal assembly of a suite of quantified and homogeneously coded metrics is the vital first step towards an automated, systematic, science-based assessment of any given cadence simulation, that will enable the scheduling of the LSST to be as well-informed as possible., Comment: 312 pages, 90 figures. Browse the current version at https://github.com/LSSTScienceCollaborations/ObservingStrategy, new contributions welcome!

The Large Synoptic Survey Telescope is designed to provide an unprecedented optical imaging dataset that will support investigations of our Solar System, Galaxy and Universe, across half the sky and over ten years of repeated observation. However, exactly how the LSST observations will be taken (the observing strategy or "cadence") is not yet finalized. In this dynamically-evolving community white paper, we explore how the detailed performance of the anticipated science investigations is expected to depend on small changes to the LSST observing strategy. Using realistic simulations of the LSST schedule and observation properties, we design and compute diagnostic metrics and Figures of Merit that provide quantitative evaluations of different observing strategies, analyzing their impact on a wide range of proposed science projects. This is work in progress: we are using this white paper to communicate to each other the relative merits of the observing strategy choices that could be made, in an effort to maximize the scientific value of the survey. The investigation of some science cases leads to suggestions for new strategies that could be simulated and potentially adopted. Notably, we find motivation for exploring departures from a spatially uniform annual tiling of the sky: focusing instead on different parts of the survey area in different years in a "rolling cadence" is likely to have significant benefits for a number of time domain and moving object astronomy projects. The communal assembly of a suite of quantified and homogeneously coded metrics is the vital first step towards an automated, systematic, science-based assessment of any given cadence simulation, that will enable the scheduling of the LSST to be as well-informed as possible., Comment: 312 pages, 90 figures. Browse the current version at https://github.com/LSSTScienceCollaborations/ObservingStrategy, new contributions welcome!

In response to a recently reported observation of evidence for two classes of Type Ia Supernovae (SNe Ia) distinguished by their brightness in the rest-frame near ultraviolet (NUV), we search for the phenomenon in publicly available light-curve data. We use the SNANA supernova analysis package to simulate SN Ia-light curves in the Sloan Digital Sky Survey Supernova Search (SDSS) and the Supernova Legacy Survey (SNLS) with a model of two distinct ultraviolet classes of SNe Ia and a conventional model with a single broad distribution of SN-Ia ultraviolet brightnesses. We compare simulated distributions of rest-frame colors with these two models to those observed in 158 SNe Ia in the SDSS and SNLS data. The SNLS sample of 99 SNe Ia is in clearly better agreement with a model with one class of SN Ia light curves and shows no evidence for distinct NUV sub-classes. The SDSS sample of 59 SNe Ia with poorer color resolution does not distinguish between the two models., Comment: 8 pages, in review at MNRAS

We present a new technique to create a bin-averaged Hubble Diagram (HD) from photometrically identified SN~Ia data. The resulting HD is corrected for selection biases and contamination from core collapse (CC) SNe, and can be used to infer cosmological parameters. This method, called "BBC" (BEAMS with Bias Corrections), includes two fitting stages. The first BBC fitting stage uses a posterior distribution that includes multiple SN likelihoods, a Monte Carlo simulation to bias-correct the fitted SALT-II parameters, and CC probabilities determined from a machine learning technique. The BBC fit determines 1) a bin-averaged HD (average distance vs. redshift), and 2) the nuisance parameters alpha and beta, which multiply the stretch and color (respectively) to standardize the SN brightness. In the second stage, the bin-averaged HD is fit to a cosmological model where priors can be imposed. We perform high precision tests of the BBC method by simulating large (150,000 event) data samples corresponding to the Dark Energy Survey Supernova Program. Our tests include three models of intrinsic scatter, each with two different CC rates. In the BBC fit, the SALT-II nuisance parameters alpha and beta are recovered to within 1% of their true values. In the cosmology fit, we determine the dark energy equation of state parameter w using a fixed value of Omega_matter as a prior: averaging over all six tests based on 6 x 150,000 = 900,000 SNe, there is a small w-bias of 0.006 +- 0.002. Finally, the BBC fitting code is publicly available in the SNANA package.

Host galaxy identification is a crucial step for modern supernova (SN) surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope (LSST), which will discover SNe by the thousands. Spectroscopic resources are limited, so in the absence of real-time SN spectra these surveys must rely on host galaxy spectra to obtain accurate redshifts for the Hubble diagram and to improve photometric classification of SNe. In addition, SN luminosities are known to correlate with host-galaxy properties. Therefore, reliable identification of host galaxies is essential for cosmology and SN science. We simulate SN events and their locations within their host galaxies to develop and test methods for matching SNe to their hosts. We use both real and simulated galaxy catalog data from the Advanced Camera for Surveys General Catalog and MICECATv2.0, respectively. We also incorporate "hostless" SNe residing in undetected faint hosts into our analysis, with an assumed hostless rate of 5%. Our fully automated algorithm is run on catalog data and matches SNe to their hosts with 91% accuracy. We find that including a machine learning component, run after the initial matching algorithm, improves the accuracy (purity) of the matching to 97% with a 2% cost in efficiency (true positive rate). Although the exact results are dependent on the details of the survey and the galaxy catalogs used, the method of identifying host galaxies we outline here can be applied to any transient survey., Comment: 22 pages, 18 figures; Accepted by AJ, revised to incorporate referee comments

Simulations of Type Ia Supernovae (SNIa) surveys are a critical tool for correcting biases in the analysis of SNIa to infer cosmological parameters. Large scale Monte Carlo simulations include a thorough treatment of observation history, measurement noise, intrinsic scatter models and selection effects. In this paper, we improve simulations with a robust technique to evaluate the underlying populations of SNIa color and stretch that correlate with luminosity. In typical analyses, the standardized SNIa brightness is determined from linear `Tripp' relations between the light curve color and luminosity and between stretch and luminosity. However, this solution produces Hubble residual biases because intrinsic scatter and measurement noise result in measured color and stretch values that do not follow the Tripp relation. We find a $10\sigma$ bias (up to 0.3 mag) in Hubble residuals versus color and $5\sigma$ bias (up to 0.2 mag) in Hubble residuals versus stretch in a joint sample of 920 spectroscopically confirmed SNIa from PS1, SNLS, SDSS and several low-z surveys. After we determine the underlying color and stretch distributions, we use simulations to predict and correct the biases in the data. We show that removing these biases has a small impact on the low-z sample, but reduces the intrinsic scatter $\sigma_{\textrm{int}}$ from $0.101$ to $0.083$ in the combined PS1, SNLS and SDSS sample. Past estimates of the underlying populations were too broad, leading to a small bias in the equation-of-state of dark energy $w$ of $\Delta w=0.005$., Comment: Accepted by ApJL

In response to a recently reported observation of evidence for two classes of Type Ia Supernovae (SNe Ia) distinguished by their brightness in the rest-frame near ultraviolet (NUV), we search for the phenomenon in publicly available light-curve data. We use the SNANA supernova analysis package to simulate SN Ia-light curves in the Sloan Digital Sky Survey Supernova Search (SDSS) and the Supernova Legacy Survey (SNLS) with a model of two distinct ultraviolet classes of SNe Ia and a conventional model with a single broad distribution of SN-Ia ultraviolet brightnesses. We compare simulated distributions of rest-frame colors with these two models to those observed in 158 SNe Ia in the SDSS and SNLS data. The SNLS sample of 99 SNe Ia is in clearly better agreement with a model with one class of SN Ia light curves and shows no evidence for distinct NUV sub-classes. The SDSS sample of 59 SNe Ia with poorer color resolution does not distinguish between the two models., Comment: 8 pages, in review at MNRAS

We present a new technique to create a bin-averaged Hubble Diagram (HD) from photometrically identified SN~Ia data. The resulting HD is corrected for selection biases and contamination from core collapse (CC) SNe, and can be used to infer cosmological parameters. This method, called "BBC" (BEAMS with Bias Corrections), includes two fitting stages. The first BBC fitting stage uses a posterior distribution that includes multiple SN likelihoods, a Monte Carlo simulation to bias-correct the fitted SALT-II parameters, and CC probabilities determined from a machine learning technique. The BBC fit determines 1) a bin-averaged HD (average distance vs. redshift), and 2) the nuisance parameters alpha and beta, which multiply the stretch and color (respectively) to standardize the SN brightness. In the second stage, the bin-averaged HD is fit to a cosmological model where priors can be imposed. We perform high precision tests of the BBC method by simulating large (150,000 event) data samples corresponding to the Dark Energy Survey Supernova Program. Our tests include three models of intrinsic scatter, each with two different CC rates. In the BBC fit, the SALT-II nuisance parameters alpha and beta are recovered to within 1% of their true values. In the cosmology fit, we determine the dark energy equation of state parameter w using a fixed value of Omega_matter as a prior: averaging over all six tests based on 6 x 150,000 = 900,000 SNe, there is a small w-bias of 0.006 +- 0.002. Finally, the BBC fitting code is publicly available in the SNANA package.

Host galaxy identification is a crucial step for modern supernova (SN) surveys such as the Dark Energy Survey (DES) and the Large Synoptic Survey Telescope (LSST), which will discover SNe by the thousands. Spectroscopic resources are limited, so in the absence of real-time SN spectra these surveys must rely on host galaxy spectra to obtain accurate redshifts for the Hubble diagram and to improve photometric classification of SNe. In addition, SN luminosities are known to correlate with host-galaxy properties. Therefore, reliable identification of host galaxies is essential for cosmology and SN science. We simulate SN events and their locations within their host galaxies to develop and test methods for matching SNe to their hosts. We use both real and simulated galaxy catalog data from the Advanced Camera for Surveys General Catalog and MICECATv2.0, respectively. We also incorporate "hostless" SNe residing in undetected faint hosts into our analysis, with an assumed hostless rate of 5%. Our fully automated algorithm is run on catalog data and matches SNe to their hosts with 91% accuracy. We find that including a machine learning component, run after the initial matching algorithm, improves the accuracy (purity) of the matching to 97% with a 2% cost in efficiency (true positive rate). Although the exact results are dependent on the details of the survey and the galaxy catalogs used, the method of identifying host galaxies we outline here can be applied to any transient survey., Comment: 22 pages, 18 figures; Accepted by AJ, revised to incorporate referee comments

Simulations of Type Ia Supernovae (SNIa) surveys are a critical tool for correcting biases in the analysis of SNIa to infer cosmological parameters. Large scale Monte Carlo simulations include a thorough treatment of observation history, measurement noise, intrinsic scatter models and selection effects. In this paper, we improve simulations with a robust technique to evaluate the underlying populations of SNIa color and stretch that correlate with luminosity. In typical analyses, the standardized SNIa brightness is determined from linear `Tripp' relations between the light curve color and luminosity and between stretch and luminosity. However, this solution produces Hubble residual biases because intrinsic scatter and measurement noise result in measured color and stretch values that do not follow the Tripp relation. We find a $10\sigma$ bias (up to 0.3 mag) in Hubble residuals versus color and $5\sigma$ bias (up to 0.2 mag) in Hubble residuals versus stretch in a joint sample of 920 spectroscopically confirmed SNIa from PS1, SNLS, SDSS and several low-z surveys. After we determine the underlying color and stretch distributions, we use simulations to predict and correct the biases in the data. We show that removing these biases has a small impact on the low-z sample, but reduces the intrinsic scatter $\sigma_{\textrm{int}}$ from $0.101$ to $0.083$ in the combined PS1, SNLS and SDSS sample. Past estimates of the underlying populations were too broad, leading to a small bias in the equation-of-state of dark energy $w$ of $\Delta w=0.005$., Comment: Accepted by ApJL

info:eu-repo/semantics/published

This paper describes the data release of the Sloan Digital Sky Survey-II (SDSS-II) Supernova Survey conducted between 2005 and 2007. Light curves, spectra, classifications, and ancillary data are presented for 10,258 variable and transient sources discovered through repeat ugriz imaging of SDSS Stripe 82, a 300 deg2 area along the celestial equator. This data release is comprised of all transient sources brighter than r~22.5 mag with no history of variability prior to 2004. Dedicated spectroscopic observations were performed on a subset of 889 transients, as well as spectra for thousands of transient host galaxies using the SDSS-III BOSS spectrographs. Photometric classifications are provided for the candidates with good multi-color light curves that were not observed spectroscopically. From these observations, 4607 transients are either spectroscopically confirmed, or likely to be, supernovae, making this the largest sample of supernova candidates ever compiled. We present a new method for SN host-galaxy identification and derive host-galaxy properties including stellar masses, star-formation rates, and the average stellar population ages from our SDSS multi-band photometry. We derive SALT2 distance moduli for a total of 1443 SN Ia with spectroscopic redshifts as well as photometric redshifts for a further 677 purely-photometric SN Ia candidates. Using the spectroscopically confirmed subset of the three-year SDSS-II SN Ia sample and assuming a flat Lambda-CDM cosmology, we determine Omega_M = 0.315 +/- 0.093 (statistical error only) and detect a non-zero cosmological constant at 5.7 sigmas., Comment: Submitted to ApJS. Full catalogs and datafiles are available here: http://sdssdp62.fnal.gov/sdsssn/DataRelease/index.html

This paper describes the data release of the Sloan Digital Sky Survey-II (SDSS-II) Supernova Survey conducted between 2005 and 2007. Light curves, spectra, classifications, and ancillary data are presented for 10,258 variable and transient sources discovered through repeat ugriz imaging of SDSS Stripe 82, a 300 deg2 area along the celestial equator. This data release is comprised of all transient sources brighter than r~22.5 mag with no history of variability prior to 2004. Dedicated spectroscopic observations were performed on a subset of 889 transients, as well as spectra for thousands of transient host galaxies using the SDSS-III BOSS spectrographs. Photometric classifications are provided for the candidates with good multi-color light curves that were not observed spectroscopically. From these observations, 4607 transients are either spectroscopically confirmed, or likely to be, supernovae, making this the largest sample of supernova candidates ever compiled. We present a new method for SN host-galaxy identification and derive host-galaxy properties including stellar masses, star-formation rates, and the average stellar population ages from our SDSS multi-band photometry. We derive SALT2 distance moduli for a total of 1443 SN Ia with spectroscopic redshifts as well as photometric redshifts for a further 677 purely-photometric SN Ia candidates. Using the spectroscopically confirmed subset of the three-year SDSS-II SN Ia sample and assuming a flat Lambda-CDM cosmology, we determine Omega_M = 0.315 +/- 0.093 (statistical error only) and detect a non-zero cosmological constant at 5.7 sigmas., Comment: Submitted to ApJS. Full catalogs and datafiles are available here: http://sdssdp62.fnal.gov/sdsssn/DataRelease/index.html

We present a systematic study of the pure deflagration model of Type Ia supernovae using three-dimensional, high-resolution, full-star hydrodynamical simulations, nucleosynthetic yields calculated using Lagrangian tracer particles, and light curves calculated using radiation transport. We evaluate the simulations by comparing their predicted light curves with many observed SNe Ia using the SALT2 data-driven model and find that the simulations may correspond to under-luminous SNe Iax. We explore the effects of the initial conditions on our results by varying the number of randomly selected ignition points from 63 to 3500, and the radius of the centered sphere they are confined in from 128 to 384 km. We find that the rate of nuclear burning depends on the number of ignition points at early times, the density of ignition points at intermediate times, and the radius of the confining sphere at late times. The results depend primarily on the number of ignition points, but we do not expect this to be the case in general. The simulations with few ignition points release more nuclear energy $E_{\mathrm{nuc}}$, have larger kinetic energies $E_{\mathrm{K}}$, and produce more $^{56}$Ni than those with many ignition points, and differ in the distribution of $^{56}$Ni, Si, and C/O in the ejecta. For these reasons, the simulations with few ignition points exhibit higher peak B-band absolute magnitudes $M_\mathrm{B}$ and light curves that rise and decline more quickly; their $M_\mathrm{B}$ and light curves resemble those of under-luminous SNe Iax, while those for simulations with many ignition points are not., Comment: Submitted to ApJ, 23 pages, 19 figures. A 3D animation is provided at http://flash.uchicago.edu/~long/PureDef/8km-PureDef-DDT-Compositions-1365x768.mp4

We propose a robust, quantitative method to compare the synthetic light curves of a Type Ia Supernova (SNIa) explosion model with a large set of observed SNeIa, and derive a figure of merit for the explosion model's agreement with observations. The synthetic light curves are fit with the data-driven model SALT2 which returns values for stretch, color, and magnitude at peak brightness, as well as a goodness-of-fit parameter. Each fit is performed multiple times with different choices of filter bands and epoch range in order to quantify the systematic uncertainty on the fitted parameters. We use a parametric population model for the distribution of observed SNIa parameters from large surveys, and extend it to represent red, dim, and bright outliers found in a low-redshift SNIa data set. We discuss the potential uncertainties of this population model and find it to be reliable given the current uncertainties on cosmological parameters. Using our population model, we assign each set of fitted parameters a likelihood of being observed in nature, and a figure of merit based on this likelihood. We define a second figure of merit based on the quality of the light curve fit, and combine the two measures into an overall figure of merit for each explosion model. We compute figures of merit for a variety of 1D, 2D and 3D explosion models and show that our evaluation method allows meaningful inferences across a wide range of light curve quality and fitted parameters., Comment: Published version, minor changes

We present a systematic study of the pure deflagration model of Type Ia supernovae using three-dimensional, high-resolution, full-star hydrodynamical simulations, nucleosynthetic yields calculated using Lagrangian tracer particles, and light curves calculated using radiation transport. We evaluate the simulations by comparing their predicted light curves with many observed SNe Ia using the SALT2 data-driven model and find that the simulations may correspond to under-luminous SNe Iax. We explore the effects of the initial conditions on our results by varying the number of randomly selected ignition points from 63 to 3500, and the radius of the centered sphere they are confined in from 128 to 384 km. We find that the rate of nuclear burning depends on the number of ignition points at early times, the density of ignition points at intermediate times, and the radius of the confining sphere at late times. The results depend primarily on the number of ignition points, but we do not expect this to be the case in general. The simulations with few ignition points release more nuclear energy $E_{\mathrm{nuc}}$, have larger kinetic energies $E_{\mathrm{K}}$, and produce more $^{56}$Ni than those with many ignition points, and differ in the distribution of $^{56}$Ni, Si, and C/O in the ejecta. For these reasons, the simulations with few ignition points exhibit higher peak B-band absolute magnitudes $M_\mathrm{B}$ and light curves that rise and decline more quickly; their $M_\mathrm{B}$ and light curves resemble those of under-luminous SNe Iax, while those for simulations with many ignition points are not., Comment: Submitted to ApJ, 23 pages, 19 figures. A 3D animation is provided at http://flash.uchicago.edu/~long/PureDef/8km-PureDef-DDT-Compositions-1365x768.mp4

We propose a robust, quantitative method to compare the synthetic light curves of a Type Ia Supernova (SNIa) explosion model with a large set of observed SNeIa, and derive a figure of merit for the explosion model's agreement with observations. The synthetic light curves are fit with the data-driven model SALT2 which returns values for stretch, color, and magnitude at peak brightness, as well as a goodness-of-fit parameter. Each fit is performed multiple times with different choices of filter bands and epoch range in order to quantify the systematic uncertainty on the fitted parameters. We use a parametric population model for the distribution of observed SNIa parameters from large surveys, and extend it to represent red, dim, and bright outliers found in a low-redshift SNIa data set. We discuss the potential uncertainties of this population model and find it to be reliable given the current uncertainties on cosmological parameters. Using our population model, we assign each set of fitted parameters a likelihood of being observed in nature, and a figure of merit based on this likelihood. We define a second figure of merit based on the quality of the light curve fit, and combine the two measures into an overall figure of merit for each explosion model. We compute figures of merit for a variety of 1D, 2D and 3D explosion models and show that our evaluation method allows meaningful inferences across a wide range of light curve quality and fitted parameters., Comment: Published version, minor changes

Using data from the Sloan Digital Sky Supernova Survey-II (SDSS-II SN Survey), we measure the rate of Type Ia supernovae (SNe Ia) as a function of galaxy properties at intermediate redshift. A sample of 342 SNe Ia with 0.05 < z < 0.25 is constructed. Using broadband photometry and redshifts, we use the PEGASE. 2 spectral energy distributions to estimate host galaxy stellar masses and recent star formation rates (SFRs). We find that the rate of SNe Ia per unit stellar mass is significantly higher (by a factor of similar to 30) in highly star-forming galaxies compared to passive galaxies. When parameterizing the SN Ia rate (SNRIa) based on host galaxy properties, we find that the rate of SNe Ia in passive galaxies is not linearly proportional to the stellar mass; instead an SNRIa proportional to M-0.68 is favored. However, such a parameterization does not describe the observed SNRIa in star-forming galaxies. The SNRIa in star-forming galaxies is well fitted by SNRIa = (0.41 +/- 0.15) x 10(-10) M0.72+/-0.15 + (0.65 +/- 0.25) x 10(-3) SFR1.01+/-0.22 (statistical errors only), where M is the host galaxy stellar mass (in M-circle dot) and SFR is the SFR (in M-circle dot yr(-1)). We show that our results, for SNe Ia in passive galaxies, are consistent with those at higher redshifts (favoring SNRIa proportional to M) when accounting for the difference in the ages of our galaxies. This suggests that the rate of SNe Ia is correlated with the age of the stellar population. The MLCS extinction parameter, A(V), is similar in passive and moderately star-forming galaxies, but we find indications that it is smaller, on average, in highly star-forming galaxies. This result appears to be driven by a deficit of the reddest (A(V) > 0.15) SNe Ia in highly star-forming galaxies. We consider that the high levels of dust in these systems may be obscuring the reddest and faintest SNe Ia., AuthorCount:14

We present Keck high-quality rest-frame ultraviolet (UV) through optical spectra of 21 Type Ia supernovae (SNe Ia) in the redshift range 0.11 <= z <= 0.37 and a mean redshift of 0.22 that were discovered during the Sloan Digital Sky Survey-II (SDSS-II) SN Survey. Using the broadband photometry of the SDSS survey, we are able to reconstruct the SN host-galaxy spectral energy distributions (SEDs), allowing for a correction for the host-galaxy contamination in the SN Ia spectra. Comparison of composite spectra constructed from a subsample of 17 high-quality spectra to those created from a low-redshift sample with otherwise similar properties shows that the Keck/SDSS SNe Ia have, on average, extremely similar rest-frame optical spectra but show a UV flux excess. This observation is confirmed by comparing synthesized broadband colors of the individual spectra, showing a difference in mean colors at the 2.4 sigma-4.4 sigma level for various UV colors. We further see a slight difference in the UV spectral shape between SNe with low-mass and high-mass host galaxies. Additionally, we detect a relationship between the flux ratio at 2770 and 2900 angstrom and peak luminosity that differs from that observed at low redshift. We find that changing the UV SED of an SN Ia within the observed dispersion can change the inferred distance moduli by similar to 0.1 mag. This effect only occurs when the data probe the rest-frame UV. We suggest that this discrepancy could be due to differences in the host-galaxy population of the two SN samples or to small-sample statistics., AuthorCount:14

For spectroscopically confirmed Type Ia supernovae we evaluate models of intrinsic brightness variations with detailed data/Monte Carlo comparisons of the dispersion in the following quantities: Hubble-diagram scatter, color difference (B-V-c) between the true B-V color and the fitted color (c) from the SALT-II light curve model, and photometric redshift residual. The data sample includes 251 ugriz light curves from the 3-season Sloan Digital Sky Survey-II, and 191 griz light curves from the Supernova Legacy Survey 3-year data release. We find that the simplest model of a wavelength-independent (coherent) scatter is not adequate, and that to describe the data the intrinsic scatter model must have wavelength-dependent variations. We use Monte Carlo simulations to examine the standard approach of adding a coherent scatter term in quadrature to the distance-modulus uncertainty in order to bring the reduced chi2 to unity when fitting a Hubble diagram. If the light curve fits include model uncertainties with the correct wavelength dependence of the scatter, we find that the bias on the dark energy equation of state parameter $w$ is negligible. However, incorrect model uncertainties can lead to a significant bias on the distance moduli, with up to ~0.05 mag redshift-dependent variation. For the recent SNLS3 cosmology results we estimate that this effect introduces an additional systematic uncertainty on $w$ of ~0.02, well below the total uncertainty. However, this uncertainty depends on the samples used, and thus this small $w$-uncertainty is not guaranteed in future cosmology results., Comment: accepted by ApJ

For spectroscopically confirmed Type Ia supernovae we evaluate models of intrinsic brightness variations with detailed data/Monte Carlo comparisons of the dispersion in the following quantities: Hubble-diagram scatter, color difference (B-V-c) between the true B-V color and the fitted color (c) from the SALT-II light curve model, and photometric redshift residual. The data sample includes 251 ugriz light curves from the 3-season Sloan Digital Sky Survey-II, and 191 griz light curves from the Supernova Legacy Survey 3-year data release. We find that the simplest model of a wavelength-independent (coherent) scatter is not adequate, and that to describe the data the intrinsic scatter model must have wavelength-dependent variations. We use Monte Carlo simulations to examine the standard approach of adding a coherent scatter term in quadrature to the distance-modulus uncertainty in order to bring the reduced chi2 to unity when fitting a Hubble diagram. If the light curve fits include model uncertainties with the correct wavelength dependence of the scatter, we find that the bias on the dark energy equation of state parameter $w$ is negligible. However, incorrect model uncertainties can lead to a significant bias on the distance moduli, with up to ~0.05 mag redshift-dependent variation. For the recent SNLS3 cosmology results we estimate that this effect introduces an additional systematic uncertainty on $w$ of ~0.02, well below the total uncertainty. However, this uncertainty depends on the samples used, and thus this small $w$-uncertainty is not guaranteed in future cosmology results., Comment: accepted by ApJ

We analyze the three-year Sloan Digital Sky Survey II (SDSS-II) Supernova (SN) Survey data and identify a sample of 1070 photometric Type Ia supernova (SN Ia) candidates based on their multiband light curve data. This sample consists of SN candidates with no spectroscopic confirmation, with a subset of 210 candidates having spectroscopic redshifts of their host galaxies measured while the remaining 860 candidates are purely photometric in their identification. We describe a method for estimating the efficiency and purity of photometric SN Ia classification when spectroscopic confirmation of only a limited sample is available, and demonstrate that SN Ia candidates from SDSS-II can be identified photometrically with ~91% efficiency and with a contamination of ~6%. Although this is the largest uniform sample of SN candidates to date for studying photometric identification, we find that a larger spectroscopic sample of contaminating sources is required to obtain a better characterization of the background events. A Hubble diagram using SN candidates with no spectroscopic confirmation, but with host galaxy spectroscopic redshifts, yields a distance modulus dispersion that is only ~20%-40% larger than that of the spectroscopically confirmed SN Ia sample alone with no significant bias. A Hubble diagram with purely photometric classification and redshift-distance measurements, however, exhibits biases that require further investigation for precision cosmology.

We examine the correlation between supernova host galaxy properties and their residuals on the Hubble diagram. We use supernovae discovered during the Sloan Digital Sky Survey II - Supernova Survey, and focus on objects at a redshift of z < 0.15, where the selection effects of the survey are known to yield a complete Type Ia supernova sample. To minimize the bias in our analysis with respect to measured host-galaxy properties, spectra were obtained for nearly all hosts, spanning a range in magnitude of -23 < M_r < -17. In contrast to previous works that use photometric estimates of host mass as a proxy for global metallicity, we analyze host-galaxy spectra to obtain gas-phase metallicities and star-formation rates from host galaxies with active star formation. From a final sample of ~ 40 emission-line galaxies, we find that light-curve corrected Type Ia supernovae are ~ 0.1 magnitudes brighter in high-metallicity hosts than in low-metallicity hosts. We also find a significant (> 3{\sigma}) correlation between the Hubble residuals of Type Ia supernovae and the specific star-formation rate of the host galaxy. We comment on the importance of supernova/host-galaxy correlations as a source of systematic bias in future deep supernova surveys., Comment: The Astrophysical Journal (in press)

We improve estimates of stellar mass and mass-weighted average age of Type Ia supernova (SN Ia) host galaxies by combining UV and near-IR photometry with optical photometry in our analysis. Using 206 SNe Ia drawn from the full three-year SDSS-II Supernova Survey (median redshift of z {\approx} 0.2) and multi-wavelength host-galaxy photometry from SDSS, GALEX, and UKIDSS, we present evidence of a correlation (1.9{\sigma} confidence level) between the residuals of SNe Ia about the best-fit Hubble relation and the mass-weighted average age of their host galaxies. The trend is such that older galaxies host SNe Ia that are brighter than average after standard light-curve corrections are made. We also confirm, at the 3.0{\sigma} level, the trend seen by previous studies that more massive galaxies often host brighter SNe Ia after light-curve correction., Comment: The Astrophysical Journal (in press)

We analyze the three-year SDSS-II Superernova (SN) Survey data and identify a sample of 1070 photometric SN Ia candidates based on their multi-band light curve data. This sample consists of SN candidates with no spectroscopic confirmation, with a subset of 210 candidates having spectroscopic redshifts of their host galaxies measured, while the remaining 860 candidates are purely photometric in their identification. We describe a method for estimating the efficiency and purity of photometric SN Ia classification when spectroscopic confirmation of only a limited sample is available, and demonstrate that SN Ia candidates from SDSS-II can be identified photometrically with ~91% efficiency and with a contamination of ~6%. Although this is the largest uniform sample of SN candidates to date for studying photometric identification, we find that a larger spectroscopic sample of contaminating sources is required to obtain a better characterization of the background events. A Hubble diagram using SN candidates with no spectroscopic confirmation, but with host galaxy spectroscopic redshifts, yields a distance modulus dispersion that is only ~20 - 40% larger than that of the spectroscopically-confirmed SN Ia sample alone with no significant bias. A Hubble diagram with purely photometric classification and redshift-distance measurements, however, exhibit biases that require further investigation for precision cosmology., Comment: The Astrophysical Journal (in press)

Using data from the Sloan Digital Sky Supernova Survey-II, we measure the rate of Type Ia Supernovae (SNe Ia) as a function of galaxy properties at intermediate redshift. A sample of 342 SNe Ia with 0.05

We describe a new formalism to fit the parameters $\alpha$ and $\beta$ that are used in the SALT2 model to determine the standard magnitudes of Type Ia supernovae. The new formalism describes the intrinsic scatter in Type Ia supernovae by a covariance matrix in place of the single parameter normally used. We have applied this formalism to the Sloan Digital Sky Survey Supernova Survey (SDSS-II) data and conclude that the data are best described by $\alpha=0.135_{-.017}^{+.033}$ and $\beta=3.19_{-0.24}^{+0.14}$, where the error is dominated by the uncertainty in the form of the intrinsic scatter matrix. Our result depends on the introduction of a more general form for the intrinsic scatter of the distance moduli of Type Ia supernovae than is conventional, resulting in a larger value of $\beta$ and a larger uncertainty than the conventional approach. Although this analysis results in a larger value of $\beta$ and a larger error, the SDSS data differ (at a 98% confidence level) with $\beta=4.1$, the value expected for extinction by the type of dust found in the Milky Way. We have modeled the distribution of supernovae Ia in terms of their color and conclude that there is strong evidence that variation in color is a significant contributor to the scatter of supernovae Ia around their standard candle magnitude.

We present the dependences of the properties of type Ia Supernovae (SNe Ia) on their host galaxies by analyzing the multi-band lightcurves of 118 spectroscopically confirmed SNe Ia observed by the Sloan Digital Sky Survey (SDSS) Supernova Survey and the spectra of their host galaxies. We derive the equivalent width of the \rm{H}$\alpha$ emission line, star formation rate, and gas-phase metallicity from the spectra and compare these with the lightcurve widths and colors of SNe Ia. In addition, we compare host properties with the deviation of the observed distance modulus corrected for lightcurve parameters from the distance modulus determined by the best fit cosmological parameters. This allows us to investigate uncorrected systematic effects in the magnitude standardization. We find that SNe Ia in host galaxies with a higher star formation rate have synthesized on average a larger $^{56}$Ni mass and show wider lightcurves. The $^{56}$Ni mass dependence on metallicity is consistent with a prediction of Timmes et al. 2003 based on nucleosynthesis. SNe Ia in metal-rich galaxies ({$\log_{10}(O/H)+12>8.9$) have become 0.13 $\pm$ 0.06 magnitude brighter after corrections for their lightcurve widths and colors, which corresponds to up to 6% uncertainty in the luminosity distance. We investigate whether parameters for standardizing SN Ia maximum magnitude differ among samples with different host characteristics. The coefficient of the color term is larger by 0.67 $\pm$ 0.19 for SNe Ia in metal-poor hosts than those in metal-rich hosts when no color cuts are imposed., Comment: 41 pages, 10 figures and 2 tables, submitted to ApJ

We analyze the effect that peculiar velocities have on the cosmological inferences we make using luminosity distance indicators, such as Type Ia supernovae. In particular we study the corrections required to account for (1) our own motion, (2) correlations in galaxy motions, and (3) a possible local under- or overdensity. For all of these effects we present a case study showing the impact on the cosmology derived by the Sloan Digital Sky Survey-II Supernova Survey (SDSS-II SN Survey). Correcting supernova (SN) redshifts for the cosmic microwave background (CMB) dipole slightly overcorrects nearby SNe that share some of our local motion. We show that while neglecting the CMB dipole would cause a shift in the derived equation of state of ¿w ~ 0.04 (at fixed O m ), the additional local-motion correction is currently negligible (¿w <~ 0.01). We then demonstrate a covariance-matrix approach to statistically account for correlated peculiar velocities. This down-weights nearby SNe and effectively acts as a graduated version of the usual sharp low-redshift cut. Neglecting coherent velocities in the current sample causes a systematic shift of ¿w ~ 0.02. This will therefore have to be considered carefully when future surveys aim for percent-level accuracy and we recommend our statistical approach to down-weighting peculiar velocities as a more robust option than a sharp low-redshift cut.