Light Curves Of Type Ia Supernovae Research Articles

Probing the Diversity of Type Ia Supernova Light Curves in the Open Supernova Catalog

The ever-growing sample of observed supernovae (SNe) enhances our capacity for comprehensive SN population studies, providing a richer data set for understanding the diverse characteristics of Type Ia supernovae (SNe Ia) and possibly those of their progenitors. Here, we present a data-driven analysis of observed SN Ia photometric light curves collected in the Open Supernova Catalog. Where available, we add the environmental information from the host galaxy. We focus on identifying subclasses of SNe Ia without imposing the predefined subclasses found in the literature to date. To do so, we employ an implicit rank-minimizing autoencoder neural network for developing low-dimensional data representations, providing a compact representation of the SN light-curve diversity. When we analyze light curves alone, we find that one of our resulting latent variables is strongly correlated with redshift, allowing us to approximately “de-redshift” the other latent variables describing each event. After doing so, we find that three of our latent variables account for ∼95% of the variance in our sample, and provide a natural separation between 91T and 91bg thermonuclear SNe. Of note, the 02cx subclass is not unambiguously delineated from the 91bg sample in our results, nor do either the overluminous 91T or the underluminous 91bg/02cx samples form a clearly distinct population from the broader sample of “other” SN Ia events. We identify the physical characteristics of SN light curves that best distinguish SNe 91T from SNe 91bg and 02cx, and discuss prospects for future refinements and applications to other classes of SNe as well as other transients.

Four Years of Type Ia Supernovae Observed by TESS: Early-time Light-curve Shapes and Constraints on Companion Interaction Models

We present 307 type Ia supernova (SN) light curves from the first 4 yr of the Transiting Exoplanet Survey Satellite mission. We use this sample to characterize the shapes of the early-time light curves, measure the rise times from first light to peak, and search for companion star interactions. Using simulations, we show that light curves must have noise <10% of the peak flux to avoid biases in the early-time light-curve shape, restricting our quantitative analysis to 74 light curves. We find that the mean power-law index of the early-time light curves is β 1 = 1.93 ± 0.57, and the mean rise time to peak is 15.7 ± 3.5 days. The underlying population distribution for β 1 may instead consist of a Gaussian component with mean 2.29, width 0.34, and a long tail extending to values less than 1.0. We find that the data can rarely distinguish between models with and without companion interaction models. Nevertheless, we find three high-quality light curves that tentatively prefer the addition of a companion interaction model, but the statistical evidence for the companion interactions is not robust. We also find two SNe that disfavor the addition of a companion interaction model to a curved power-law model. Taking the 74 SNe together, we calculate 3σ upper limits on the presence of companion signatures to control for orientation effects that can hide companions in individual light curves. Our results rule out common progenitor systems with companions having Roche lobe radii >31 R ⊙ (separations >5.7 × 1012 cm, 99.9% confidence level) and disfavor companions having Roche lobe radii >10 R ⊙ (separations >1.9 × 1012 cm, 95% confidence level). Lastly, we discuss the implications of our results for the intrinsic fraction of single degenerate progenitor systems.

Iron-rich Metal-poor Stars and the Astrophysics of Thermonuclear Events Observationally Classified as Type Ia Supernovae. I. Establishing the Connection

The progenitor systems and explosion mechanisms responsible for the thermonuclear events observationally classified as Type Ia supernovae are uncertain and difficult to uniquely constrain using traditional observations of Type Ia supernova host galaxies, progenitors, light curves, and remnants. For the subset of thermonuclear events that are prolific producers of iron, we use published theoretical nucleosynthetic yields to identify a set of elemental abundance ratios infrequently observed in metal-poor stars but shared across a range of progenitor systems and explosion mechanisms: [Na, Mg, Co/Fe] < 0. We label stars with this abundance signature “iron-rich metal-poor,” or IRMP stars. We suggest that IRMP stars formed in environments dominated by thermonuclear nucleosynthesis and consequently that their elemental abundances can be used to constrain both the progenitor systems and explosion mechanisms responsible for thermonuclear explosions. We identify three IRMP stars in the literature and homogeneously infer their elemental abundances. We find that the elemental abundances of BD +80 245, HE 0533–5340, and SMSS J034249.53–284216.0 are best explained by the (double) detonations of sub-Chandrasekhar-mass CO white dwarfs. If our interpretation of IRMP stars is accurate, then they should be very rare in globular clusters and more common in the Magellanic Clouds and dwarf spheroidal galaxies than in the Milky Way’s halo. We propose that future studies of IRMP stars will quantify the relative occurrences of different thermonuclear event progenitor systems and explosion mechanisms.

The DEHVILS survey overview and initial data release: high-quality near-infrared Type Ia supernova light curves at low redshift

ABSTRACT While the sample of optical Type Ia supernova (SN Ia) light curves (LCs) usable for cosmological parameter measurements surpasses 2000, the sample of published, cosmologically viable near-infrared (NIR) SN Ia LCs, which have been shown to be good ‘standard candles,’ is still ≲ 200. Here, we present high-quality NIR LCs for 83 SNe Ia ranging from 0.002 &amp;lt; z &amp;lt; 0.09 as a part of the Dark Energy, H0, and peculiar Velocities using Infrared Light from Supernovae (DEHVILS) survey. Observations are taken using UKIRT’s WFCAM, where the median depth of the images is 20.7, 20.1, and 19.3 mag (Vega) for Y, J, and H-bands, respectively. The median number of epochs per SN Ia is 18 for all three bands (Y,J, and H) combined and 6 for each band individually. We fit 47 SN Ia LCs that pass strict quality cuts using three LC models, SALT3, snoopy, and B ayeSN and find scatter on the Hubble diagram to be comparable to or better than scatter from optical-only fits in the literature. Fitting NIR-only LCs, we obtain standard deviations ranging from 0.128 to 0.135 mag. Additionally, we present a refined calibration method for transforming 2MASS magnitudes to WFCAM magnitudes using HST CALSPEC stars that results in a 0.03 mag shift in the WFCAM Y-band magnitudes.

SALT2 versus SALT3: updated model surfaces and their impacts on type Ia supernova cosmology

ABSTRACT For the past decade, SALT2 has been the most common model used to fit Type Ia supernova (SN Ia) light curves for dark energy analyses. Recently, the SALT3 model was released, which upgraded a number of model features but has not yet been used for measurements of dark energy. Here, we evaluate the impact of switching from SALT2 to SALT3 for a SN cosmology analysis. We train SALT2 and SALT3 on an identical training sample of 1083 well-calibrated Type Ia supernovae, ensuring that any differences found come from the underlying model framework. We publicly release the results of this training (the SALT ‘surfaces’). We then run a cosmology analysis on the public Dark Energy Survey 3-Yr Supernova data sample (DES-SN3YR), and on realistic simulations of those data. We provide the first estimate of the SN + CMB systematic uncertainty arising from the choice of SALT model framework (i.e. SALT2 versus SALT3), Δw = + 0.001 ± 0.005 – a negligible effect at the current level of dark energy analyses. We also find that the updated surfaces are less sensitive to photometric calibration uncertainties than previous SALT2 surfaces, with the average spectral energy density dispersion reduced by a factor of two over optical wavelengths. This offers an opportunity to reduce the contribution of calibration errors to SN cosmology uncertainty budgets.

Data-driven photometric redshift estimation from type Ia supernovae light curves

ABSTRACT Redshift measurement has always been a constant need in modern astronomy and cosmology. And as new surveys have been providing an immense amount of data on astronomical objects, the need to process such data automatically proves to be increasingly necessary. In this article, we use simulated data from the Dark Energy Survey, and from a pipeline originally created to classify supernovae, we developed a linear regression algorithm optimized through novel automated machine learning (AutoML) frameworks achieving an error score better than ordinary data pre-processing methods when compared with other modern algorithms (such as xgboost). Numerically, the photometric prediction RMSE of type Ia supernovae events was reduced from 0.16 to 0.09 and the RMSE of all supernovae types decreased from 0.20 to 0.14. Our pipeline consists of four steps: through spectroscopic data points we interpolate the light curve using Gaussian process fitting algorithm, then using a wavelet transform we extract the most important features of such curves; in sequence we reduce the dimensionality of such features through principal component analysis, and in the end we applied super learning techniques (stacked ensemble methods) through an AutoML framework dedicated to optimize the parameters of several different machine learning models, better resolving the problem. As a final check, we obtained probability distribution functions (PDFs) using Gaussian kernel density estimations through the predictions of more than 50 models trained and optimized by AutoML. Those PDFs were calculated to replicate the original curves that used SALT2 model, a model used for the simulation of the raw data itself.

Spline estimation of functional principal components via manifold conjugate gradient algorithm

Functional principal component analysis has become the most important dimension reduction technique in functional data analysis. Based on B-spline approximation, functional principal components (FPCs) can be efficiently estimated by the expectation-maximization (EM) and the geometric restricted maximum likelihood (REML) algorithms under the strong assumption of Gaussianity on the principal component scores and observational errors. When computing the solution, the EM algorithm does not exploit the underlying geometric manifold structure, while the performance of REML is known to be unstable. In this article, we propose a conjugate gradient algorithm over the product manifold to estimate FPCs. This algorithm exploits the manifold geometry structure of the overall parameter space, thus improving its search efficiency and estimation accuracy. In addition, a distribution-free interpretation of the loss function is provided from the viewpoint of matrix Bregman divergence, which explains why the proposed method works well under general distribution settings. We also show that a roughness penalization can be easily incorporated into our algorithm with a potentially better fit. The appealing numerical performance of the proposed method is demonstrated by simulation studies and the analysis of a Type Ia supernova light curve dataset.

SNIa Cosmology Analysis Results from Simulated LSST Images: From Difference Imaging to Constraints on Dark Energy

The Vera Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to process ∼106 transient detections per night. For precision measurements of cosmological parameters and rates, it is critical to understand the detection efficiency, magnitude limits, artifact contamination levels, and biases in the selection and photometry. Here we rigorously test the LSST Difference Image Analysis (DIA) pipeline using simulated images from the Rubin Observatory LSST Dark Energy Science Collaboration Data Challenge (DC2) simulation for the Wide-Fast-Deep survey area. DC2 is the first large-scale (300 deg2) image simulation of a transient survey that includes realistic cadence, variable observing conditions, and CCD image artifacts. We analyze ∼15 deg2 of DC2 over a 5 yr time span in which artificial point sources from Type Ia supernova (SNIa) light curves have been overlaid onto the images. The magnitude limits per filter are u = 23.66 mag, g = 24.69 mag, r = 24.06 mag, i = 23.45 mag, z = 22.54 mag, and y = 21.62 mag. The artifact contamination levels are ∼90% of all detections, corresponding to ∼1000 artifacts deg–2 in g band, and falling to 300 deg–2 in y band. The photometry has biases <1% for magnitudes 19.5 < m < 23. Our DIA performance on simulated images is similar to that of the Dark Energy Survey difference-imaging pipeline on real images. We also characterize DC2 image properties to produce catalog-level simulations needed for distance bias corrections. We find good agreement between DC2 data and simulations for distributions of signal-to-noise ratio, redshift, and fitted light-curve properties. Applying a realistic SNIa cosmology analysis for redshifts z < 1, we recover the input cosmology parameters to within statistical uncertainties.

Comparisons of Type Ia Supernova Light Curves in the UV and Optical with the Swift Ultra-violet/Optical Telescope

We examine the light-curve parameters of 97 nearby Type Ia supernovae (SN Ia) in the ultraviolet and optical using observations from the Swift Ultra-violet/Optical Telescope. Our light-curve models used linear combinations of templates, which were based on functional principal component analysis of the optical photometry of SN Ia. The weights for each principal component used in the fit capture certain aspects of the light-curve properties. We find that there is little difference in the overall variability of principal component scores across filters. We also find correlations between certain filters and PC components, indicating that the UV and optical properties seem to be tied together. This is a promising step in UV SN Ia analysis and suggests that UV light curves may be used to infer optical properties, paving the way for future cosmological studies.

The First Data Release of CNIa0.02—A Complete Nearby (Redshift &lt;0.02) Sample of Type Ia Supernova Light Curves*

Abstract The CNIa0.02 project aims to collect a complete, nearby sample of Type Ia supernovae (SNe Ia) light curves, and the SNe are volume-limited with host-galaxy redshifts z host &lt; 0.02. The main scientific goal is to infer the distributions of key properties (e.g., the luminosity function) of local SNe Ia in a complete and unbiased fashion in order to study SN explosion physics. We spectroscopically classify any SN candidate detected by the All-Sky Automated Survey for Supernovae (ASAS-SN) that reaches a peak brightness &lt;16.5 mag. Since ASAS-SN scans the full sky and does not target specific galaxies, our target selection is effectively unbiased by host-galaxy properties. We perform multiband photometric observations starting from the time of discovery. In the first data release (DR1), we present the optical light curves obtained for 247 SNe from our project (including 148 SNe in the complete sample), and we derive parameters such as the peak fluxes, Δm 15, and s BV .

A revised SALT2 surface for fitting Type Ia supernova light curves

ABSTRACT We present a revised Spectral Adaptive Light Curve Template (SALT2) surface (SALT2-2021) for fitting the light curves of Type Ia supernovae (SNe Ia), which incorporates new measurements of zero-point calibration offsets and Milky Way extinction. The most notable change in the new surface occurs in the UV region. This new surface alters the distance measurements of SNe Ia, which can be used to investigate the nature of dark energy by probing the expansion history of the Universe. Using the revised SALT2 surface on public data from the first 3 yr of the Dark Energy Survey Supernova Program (combined with an external low-z SNe Ia sample) and combining with cosmic microwave background constraints, we find a change in the dark energy equation-of-state parameter, Δw = 0.015 ± 0.004. This result highlights the continued importance of controlling and reducing systematic uncertainties, particularly with the next generation of supernova analyses aiming to improve constraints on dark energy properties.

Model-independent Constraints on Type Ia Supernova Light-curve Hyperparameters and Reconstructions of the Expansion History of the Universe

Abstract We reconstruct the expansion history of the universe using type Ia supernovae (SN Ia) in a manner independent of any cosmological model assumptions. To do so, we implement a nonparametric iterative smoothing method on the Joint Light-curve Analysis (JLA) data while exploring the SN Ia light-curve hyperparameter space by Markov Chain Monte Carlo (MCMC) sampling. We test to see how the posteriors of these hyperparameters depend on cosmology, whether using different dark energy models or reconstructions shift these posteriors. Our constraints on the SN Ia light-curve hyperparameters from our model-independent analysis are very consistent with the constraints from using different parameterizations of the equation of state of dark energy, namely the flat ΛCDM cosmology, the Chevallier–Polarski–Linder model, and the Phenomenologically Emergent Dark Energy (PEDE) model. This implies that the distance moduli constructed from the JLA data are mostly independent of the cosmological models. We also studied that the possibility the light-curve parameters evolve with redshift and our results show consistency with no evolution. The reconstructed expansion history of the universe and dark energy properties also seem to be in good agreement with the expectations of the standard ΛCDM model. However, our results also indicate that the data still allow for considerable flexibility in the expansion history of the universe.

Erratum: ‘Continuous gravitational wave from magnetized white dwarfs and neutron stars: possible missions for LISA, DECIGO, BBO, ET detectors’

Recent detection of gravitational wave from nine black hole merger events and one neutron star merger event by LIGO and VIRGO shed a new light in the field of astrophysics. On the other hand, in the past decade, a few super-Chandrasekhar white dwarf candidates have been inferred through the peak luminosity of the light-curves of a few peculiar type Ia supernovae, though there is no direct detection of these objects so far. Similarly, a number of neutron stars with mass $>2M_\odot$ have also been observed. Continuous gravitational wave can be one of the alternate ways to detect these compact objects directly. It was already argued that magnetic field is one of the prominent physics to form super-Chandrasekhar white dwarfs and massive neutron stars. If such compact objects are rotating with certain angular frequency, then they can efficiently emit gravitational radiation, provided their magnetic field and rotation axes are not aligned, and these gravitational waves can be detected by some of the upcoming detectors, e.g. LISA, BBO, DECIGO, Einstein Telescope etc. This will certainly be a direct detection of rotating magnetized white dwarfs as well as massive neutron stars.

Cosmological Constraints from Multiple Probes in the Dark Energy Survey

The combination of multiple observational probes has long been advocated as a powerful technique to constrain cosmological parameters, in particular dark energy. The Dark Energy Survey has measured 207 spectroscopically confirmed type Ia supernova light curves, the baryon acoustic oscillation feature, weak gravitational lensing, and galaxy clustering. Here we present combined results from these probes, deriving constraints on the equation of state, w, of dark energy and its energy density in the Universe. Independently of other experiments, such as those that measure the cosmic microwave background, the probes from this single photometric survey rule out a Universe with no dark energy, finding w=-0.80_{-0.11}^{+0.09}. The geometry is shown to be consistent with a spatially flat Universe, and we obtain a constraint on the baryon density of Ω_{b}=0.069_{-0.012}^{+0.009} that is independent of early Universe measurements. These results demonstrate the potential power of large multiprobe photometric surveys and pave the way for order of magnitude advances in our constraints on properties of dark energy and cosmology over the next decade.

Think Global, Act Local: The Influence of Environment Age and Host Mass on Type Ia Supernova Light Curves

Abstract The reliability of Type Ia Supernovae (SNe Ia) may be limited by the imprint of their galactic origins. To investigate the connection between supernovae and their host characteristics, we developed an improved method to estimate the stellar population age of the host as well as the local environment around the site of the supernova. We use a Bayesian method to estimate the star formation history and mass weighted age of a supernova’s environment by matching observed spectral energy distributions to a synthesized stellar population. Applying this age estimator to both the photometrically and spectroscopically classified Sloan Digital Sky Survey II supernovae (N = 103), we find a 0.114 ± 0.039 mag “step” in the average Hubble residual at a stellar age of ∼8 Gyr; it is nearly twice the size of the currently popular mass step. We then apply a principal component analysis on the SALT2 parameters, host stellar mass, and local environment age. We find that a new parameter, PC1, consisting of a linear combination of stretch, host stellar mass, and local age, shows a very significant (4.7σ) correlation with Hubble residuals. There is a much broader range of PC1 values found in the Hubble flow sample when compared with the Cepheid calibration galaxies. These samples have mildly statistically different average PC1 values, at ∼2.5σ, resulting in at most a 1.3% reduction in the evaluation of H 0. Despite accounting for the highly significant trend in SN Ia Hubble residuals, there remains a 9% discrepancy between the most recent precision estimates of H 0 using SN Ia and the CMB.

First cosmology results using Type Ia supernova from the Dark Energy Survey: simulations to correct supernova distance biases

We describe catalog-level simulations of Type Ia supernova (SN~Ia) light curves in the Dark Energy Survey Supernova Program (DES-SN), and in low-redshift samples from the Center for Astrophysics (CfA) and the Carnegie Supernova Project (CSP). These simulations are used to model biases from selection effects and light curve analysis, and to determine bias corrections for SN~Ia distance moduli that are used to measure cosmological parameters. To generate realistic light curves, the simulation uses a detailed SN~Ia model, incorporates information from observations (PSF, sky noise, zero point), and uses summary information (e.g., detection efficiency vs. signal to noise ratio) based on 10,000 fake SN light curves whose fluxes were overlaid on images and processed with our analysis pipelines. The quality of the simulation is illustrated by predicting distributions observed in the data. Averaging within redshift bins, we find distance modulus biases up to 0.05~mag over the redshift ranges of the low-z and DES-SN samples. For individual events, particularly those with extreme red or blue color, distance biases can reach 0.4~mag. Therefore, accurately determining bias corrections is critical for precision measurements of cosmological parameters. Files used to make these corrections are available at https://des.ncsa.illinois.edu/releases/sn.

Космологическое ускорение

An overview is given of the current status of the theory and observations of the acceleration of the expansion of the observable part of the Universe. Contents 1. Historical Introduction 2. Friedmann equations and cosmological acceleration 3. Vacuum energy problem 4. Data in favor of cosmological acceleration 5.Data on supernovae and baryon acoustic oscillations 5.1 Cosmography primer: distances in the Universe; 5.2 Photometric distance; 5.3 Cosmic distance ladder; 5.4 Variety of type-Ia supernovae light curves and their usage in cosmography; 5.5 Baryon acoustic oscillations (BAOs); 5.6 BAOs in the correlation function of galaxies; 5.7 Summary of results on supernovae combined with BAOs; 5.8 Systematics and dependence on z; 5.9 Supernovae as primary distance indicators; 5.10 Merging of neutron stars and the standard siren method 6. Dark energy 7. Modified gravity 8.Conclusion 9.Appendices 9.1 Derivation of the Friedmann equations; 9.2 Cosmological parameters; 9.3 Scalar field

Characterization of Type Ia Supernova Light Curves Using Principal Component Analysis of Sparse Functional Data

Abstract With growing data from ongoing and future supernova surveys, it is possible to empirically quantify the shapes of SNIa light curves in more detail, and to quantitatively relate the shape parameters with the intrinsic properties of SNIa. Building such relationships is critical in controlling systematic errors associated with supernova cosmology. Based on a collection of well-observed SNIa samples accumulated in the past years, we construct an empirical SNIa light curve model using a statistical method called the functional principal component analysis (FPCA) for sparse and irregularly sampled functional data. Using this method, the entire light curve of an SNIa is represented by a linear combination of principal component functions, and the SNIa is represented by a few numbers called “principal component scores.” These scores are used to establish relations between light curve shapes and physical quantities such as intrinsic color, interstellar dust reddening, spectral line strength, and spectral classes. These relations allow for descriptions of some critical physical quantities based purely on light curve shape parameters. Our study shows that some important spectral feature information is being encoded in the broad band light curves; for instance, we find that the light curve shapes are correlated with the velocity and velocity gradient of the Si II λ6355 line. This is important for supernova surveys (e.g., LSST and WFIRST). Moreover, the FPCA light curve model is used to construct the entire light curve shape, which in turn is used in a functional linear form to adjust intrinsic luminosity when fitting distance models.

Late-time Flattening of Type Ia Supernova Light Curves: Constraints from SN 2014J in M82

Abstract The very nearby Type Ia supernova 2014J in M82 offers a rare opportunity to study the physics of thermonuclear supernovae at extremely late phases (≳800 days). Using the Hubble Space Telescope, we obtained 6 epochs of high-precision photometry for SN 2014J from 277 days to 1181 days past the B-band maximum light. The reprocessing of electrons and X-rays emitted by the radioactive decay chain is needed to explain the significant flattening of both the F606W-band and the pseudo-bolometric light curves. The flattening confirms previous predictions that the late-time evolution of type Ia supernova luminosities requires additional energy input from the decay of 57Co. By assuming the F606W-band luminosity scales with the bolometric luminosity at ∼500 days after the B-band maximum light, a mass ratio is required. This mass ratio is roughly ∼3 times the solar ratio and favors a progenitor white dwarf with a mass near the Chandrasekhar limit. A similar fit using the constructed pseudo-bolometric luminosity gives a mass ratio . Astrometric tests based on the multi-epoch HST ACS/WFC images reveal no significant circumstellar light echoes in between 0.3 and 100 pc from the supernova.

EXPLORING THE POTENTIAL DIVERSITY OF EARLY TYPE IA SUPERNOVA LIGHT CURVES

ABSTRACT During the first several days after explosion, Type Ia supernova light curves probe the outer layers of the exploding star, and therefore provide important clues for identifying their progenitors. We investigate how both the shallow 56Ni distribution and the presence of circumstellar material shape these early light curves. This is performed using a series of numerical experiments with parameterized properties for systematic exploration. Although not all of the considered models may be realized in nature (and indeed there are arguments why some of them should not occur), the spirit of this work is to provide a broader exploration of the diversity of possibilities. We find that shallower 56Ni leads to steeper, bluer light curves. Differences in the shape of the rise can introduce errors in estimating the explosion time, and thus impact efforts to infer upper limits on the progenitor or companion radius from a lack of observed shock cooling emission. Circumstellar material can lead to significant luminosity during the first few days, but its presence can be difficult to identify depending on the degree of nickel mixing. In some cases, the hot emission of circumstellar material may even lead to a signature similar to an interaction with a companion, and thus in the future additional diagnostics should be gathered for properly assessing early light curves.