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Register a new userCharacterization of Type Ia Supernova Light Curves Using Principal Component Analysis of Sparse Functional Data
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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 relationship 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 $lambda$6355 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.
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CCD BVRI photometry is presented for type Ia supernova 2008gy. The light curves match the template curves for fast-declining SN Ia, but the colors appear redder than average, and the SN may also be slightly subluminous. SN 2008gy is found to be located far outside the boundaries of three nearest galaxies, each of them has nearly equal probability to be the host galaxy.
Functional binary datasets occur frequently in real practice, whereas discrete characteristics of the data can bring challenges to model estimation. In this paper, we propose a sparse logistic functional principal component analysis (SLFPCA) method to handle the functional binary data. The SLFPCA looks for local sparsity of the eigenfunctions to obtain convenience in interpretation. We formulate the problem through a penalized Bernoulli likelihood with both roughness penalty and sparseness penalty terms. An efficient algorithm is developed for the optimization of the penalized likelihood using majorization-minimization (MM) algorithm. The theoretical results indicate both consistency and sparsistency of the proposed method. We conduct a thorough numerical experiment to demonstrate the advantages of the SLFPCA approach. Our method is further applied to a physical activity dataset.
We present BVRI and unfiltered light curves of 93 Type Ia supernovae (SNe Ia) from the Lick Observatory Supernova Search (LOSS) follow-up program conducted between 2005 and 2018. Our sample consists of 78 spectroscopically normal SNe Ia, with the remainder divided between distinct subclasses (three SN 1991bg-like, three SN 1991T-like, four SNe Iax, two peculiar, and three super-Chandrasekhar events), and has a median redshift of 0.0192. The SNe in our sample have a median coverage of 16 photometric epochs at a cadence of 5.4 days, and the median first observed epoch is ~4.6 days before maximum B-band light. We describe how the SNe in our sample are discovered, observed, and processed, and we compare the results from our newly developed automated photometry pipeline to those from the previous processing pipeline used by LOSS. After investigating potential biases, we derive a final systematic uncertainty of 0.03 mag in BVRI for our dataset. We perform an analysis of our light curves with particular focus on using template fitting to measure the parameters that are useful in standardising SNe Ia as distance indicators. All of the data are available to the community, and we encourage future studies to incorporate our light curves in their analyses.
Instrumental data are affected by systematic effects that dominate the errors and can be relevant when searching for small signals. This is the case of the K2 mission, a follow up of the Kepler mission, that, after a failure on two reaction wheels, has lost its stability properties rising strongly the systematics in the light curves and reducing its photometric precision. In this work, we have developed a general method to remove time related systematics from a set of light curves, that has been applied to K2 data. The method uses the Principal Component Analysis to retrieve the correlation between the light curves due to the systematics and to remove its effect without knowing any information other than the data itself. We have applied the method to all the K2 campaigns available at the Mikulski Archive for Space Telescopes, and we have tested the effectiveness of the procedure and its capability in preserving the astrophysical signal on a few transits and on eclipsing binaries. One product of this work is the identification of stable sources along the ecliptic plane that can be used as photometric calibrators for the upcoming Atmospheric Remote-sensing Exoplanet Large-survey mission.
We present near-infrared (NIR) time-series spectroscopy, as well as complementary ultraviolet (UV), optical, and NIR data, of the Type Ia supernova (SN Ia) iPTF13ebh, which was discovered within two days from the estimated time of explosion. The first NIR spectrum was taken merely 2.3 days after explosion and may be the earliest NIR spectrum yet obtained of a SN Ia. The most striking features in the spectrum are several NIR C I lines, and the C I {lambda}1.0693 {mu}m line is the strongest ever observed in a SN Ia. Interestingly, no strong optical C II counterparts were found, even though the optical spectroscopic time series began early and is densely-cadenced. Except at the very early epochs, within a few days from the time of explosion, we show that the strong NIR C I compared to the weaker optical C II appears to be general in SNe Ia. iPTF13ebh is a fast decliner with {Delta}m15(B) = 1.79 $pm$ 0.01, and its absolute magnitude obeys the linear part of the width-luminosity relation. It is therefore categorized as a transitional event, on the fast-declining end of normal SNe Ia as opposed to subluminous/91bg-like objects. iPTF13ebh shows NIR spectroscopic properties that are distinct from both the normal and subluminous/91bg-like classes, bridging the observed characteristics of the two classes. These NIR observations suggest composition and density of the inner core similar to that of 91bg-like events, and a deep reaching carbon burning layer not observed in slower declining SNe Ia. There is also a substantial difference between the explosion times inferred from the early-time light curve and the velocity evolution of the Si II {lambda}0.6355 {mu}m line, implying a long dark phase of ~ 4 days.
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