Photometric Classification of Early-time Supernova Light Curves with SCONE

In this work, we present classification results on early supernova light curves from SCONE, a photometric classifier that uses convolutional neural networks to categorize supernovae (SNe) by type using light-curve data. SCONE is able to identify SN types from light curves at any stage, from the night of initial alert to the end of their lifetimes. Simulated LSST SNe light curves were truncated at 0, 5, 15, 25, and 50 days after the trigger date and used to train Gaussian processes in wavelength and time space to produce wavelength–time heatmaps. SCONE uses these heatmaps to perform six-way classification between SN types Ia, II, Ibc, Ia-91bg, Iax, and SLSN-I. SCONE is able to perform classification with or without redshift, but we show that incorporating redshift information improves performance at each epoch. SCONE achieved 75% overall accuracy at the date of trigger (60% without redshift), and 89% accuracy 50 days after trigger (82% without redshift). SCONE was also tested on bright subsets of SNe (r < 20 mag) and produced 91% accuracy at the date of trigger (83% without redshift) and 95% five days after trigger (94.7% without redshift). SCONE is the first application of convolutional neural networks to the early-time photometric transient classification problem. All of the data processing and model code developed for this paper can be found in the SCONE software package 1 1 github.com/helenqu/scone located at github.com/helenqu/scone (Qu 2021).

Mass Ratio and Spot Parameter Estimation from Eclipsing Binary Star Light Curves

Eclipsing binary stars have a rich history of contributing to the field of stellar astrophysics. Most of the available information on the fundamental properties of stars has come from the analysis of observations of binaries. The availability of powerful computers and sophisticated codes that apply physical models has resulted in determinations of masses and radii of sufficient accuracy to provide critical tests of theories of stellar structure and evolution. Despite their sophistication, these codes still require the guiding hand of trained scientists to extract reliable information. The computer code will produce results, but it is still imperative for the analyst to ensure that those results make astrophysical sense, and to ascertain their reliability. Care must be taken to ensure that we are asking the codes for parameters for which there is information in the data. The analysis of synthetic observations with simulated observational errors of typical size can provide valuable insight to the analysis process because the parameters used to generate the observations are known. Such observations are herein analyzed to guide the process of determining mass ratios and spot parameters from eclipsing binary light curves. The goal of this paper is to illustrate some of the subtleties that need to be recognized and treated properly when analyzing binary star data.

Systematic KMTNet Planetary Anomaly Search. II. Six New q < 2 × 10−4 Mass-ratio Planets

We apply the automated AnomalyFinder algorithm of Paper I to 2018–2019 light curves from the ≃13 deg2 covered by the six KMTNet prime fields, with cadences Γ ≥ 2 hr−1. We find a total of 11 planets with mass ratios q < 2 × 10−4, including 6 newly discovered planets, 1 planet that was reported in Paper I, and recovery of 4 previously discovered planets. One of the new planets, OGLE-2018-BLG-0977Lb, is in a planetary caustic event, while the other five (OGLE-2018-BLG-0506Lb, OGLE-2018-BLG-0516Lb, OGLE-2019-BLG-1492Lb, KMT-2019-BLG-0253, and KMT-2019-BLG-0953) are revealed by a “dip” in the light curve as the source crosses the host-planet axis on the opposite side of the planet. These subtle signals were missed in previous by-eye searches. The planet-host separations (scaled to the Einstein radius), s, and planet-host mass ratios, q, are, respectively, (s, q × 105) = (0.88, 4.1), (0.96 ± 0.10, 8.3), (0.94 ± 0.07, 13), (0.97 ± 0.07, 18), (0.97 ± 0.04, 4.1), and (0.74, 18), where the “ ± ” indicates a discrete degeneracy. The 11 planets are spread out over the range − 5 < log q < − 3.7 . Together with the two planets previously reported with q ∼ 10−5 from the 2018–2019 nonprime KMT fields, this result suggests that planets toward the bottom of this mass-ratio range may be more common than previously believed.

SN 2015bq: A Luminous Type Ia Supernova with Early Flux Excess

We present optical and ultraviolet (UV) observations of a luminous type Ia supernova (SN Ia) SN 2015bq characterized by early flux excess. This SN reaches a B-band absolute magnitude at M B = −19.68 ± 0.41 mag and a peak bolometric luminosity at L = (1.75 ± 0.37) × 1043 erg s−1, with a relatively small post-maximum decline rate [Δm 15(B) = 0.82 ± 0.05 mag]. The flux excess observed in the light curves of SN 2015bq a few days after the explosion, especially seen in the UV bands, might be due to the radioactive decay of 56Ni mixed into the surface. The radiation from the decay of the surface 56Ni heats the outer layer of this SN. It produces blue U − B color followed by monotonically reddening in the early phase, dominated iron-group lines, and weak intermediate-mass element absorption features in the early spectra. The scenario of enhanced 56Ni in the surface is consistent with a large amount of 56Ni ( M 56 Ni = 0.97 ± 0.20 M ☉) synthesized during the explosion. The properties of SN 2015bq are found to locate between SN 1991T and SN 1999aa, suggesting the latter two subclasses of SNe Ia may have a common origin.

IM Normae: The Death Spiral of a Cataclysmic Variable?

We present a study of the orbital light curves of the recurrent nova IM Normae since its 2002 outburst. The broad “eclipses” recur with a 2.46 hr period, which increases on a timescale of 1.28(16) × 106 yr. Under the assumption of conservative mass transfer, this suggests a rate near 10−7 M ⊙ yr−1, and this agrees with the estimated accretion rate of the postnova, based on our estimate of luminosity. IM Nor appears to be a close match to the famous recurrent nova T Pyxidis. Both stars appear to have very high accretion rates, sufficient to drive the recurrent-nova events. Both have quiescent light curves, which suggest strong heating of the low-mass secondary, and very wide orbital minima, which suggest obscuration of a large “corona” around the primary. And both have very rapid orbital period increases, as expected from a short-period binary with high mass transfer from the low-mass component. These two stars may represent a final stage of nova—and cataclysmic variable—evolution, in which irradiation-driven winds drive a high rate of mass transfer, thereby evaporating the donor star in a paroxysm of nova outbursts.

Evidence for Gamma-Ray Pulsations from the Classical Nova ASASSN-16ma

I report here a new result extracted from the Fermi Large Area Telescope observation of the classical nova ASASSN-16ma that exhibits coherent γ-ray pulsations at 544.84(7) s during its outburst in 2016. Considering the number of independent trials, the significance of the evidence is 4.0σ, equivalent to a false-alarm probability of 5.9 × 10−5. The periodicity was steady during the 4 days of its appearance, indicating its origin as the spinning signal of the white dwarf. Given that the optical and γ-ray light curves of some shock-powered γ-ray novae have been recently shown to be closely correlated to each other, the γ-ray pulsation phenomenon likely implies an existence of associated optical pulsations, which would provide detailed ephemerides for these extreme white dwarf binaries for further investigations in the near future.

A New Method for Estimating Starspot Lifetimes Based on Autocorrelation Functions

We present a method that utilizes autocorrelation functions from long-term precision broadband differential light curves to estimate the average lifetimes of starspot groups for two large samples of Kepler stars: stars with and without previously known rotation periods. Our method is calibrated by comparing the strengths of the first few normalized autocorrelation peaks using ensembles of models that have various starspot lifetimes. We find that we must mix models of short and long lifetimes together (in heuristically determined ratios) to align the models with the Kepler data. Our fundamental result is that short starspot-group lifetimes (one to four rotations) are implied when the first normalized peak is weaker than about 0.4, long lifetimes (15 or greater) are implied when it is greater than about 0.7, and in between are the intermediate cases. Rotational lifetimes can be converted to physical lifetimes if the rotation period is known. Stars with shorter rotation periods tend to have longer rotational (but not physical) spot lifetimes, and cooler stars tend to have longer physical spot lifetimes than warmer stars with the same rotation period. The distributions of the physical lifetimes are log-normal for both samples and generally longer in the first sample. The shorter lifetimes in the stars without known periods probably explain why their periods are difficult to measure. Some stars exhibit longer than average physical starspot lifetimes; their percentage drops with increasing temperature from nearly half at 3000 K to nearly zero for hotter than 6000 K.

Assessment of Light Pollution in Parking Lots Using a Measuring System with a Drone

This article attempts to assess the light pollution of parking lots, using the proprietary measure-ment method with a drone. The main requirements, reflective features of parking lots and typical light curves of luminaires used in lighting this type of areas were presented. Calculations and simulations for various types of luminaires were performed, and then the obtained results were verified in real conditions. The main factors influencing the increase in light pollution were pre-sented and it was proved that it is possible to use the developed measurement method in order to assess the light pollution degree.

The Impact of Observing Strategy on the Reliable Classification of Standard Candle Stars: Detection of Amplitude, Period, and Phase Modulation (Blazhko Effect) of RR Lyrae Stars with LSST

The Vera C. Rubin Observatory will carry out its Legacy Survey of Space and Time (LSST) with a single-exposure depth of r ∼ 24.7 and an anticipated baseline of 10 yr, allowing access to the Milky Way’s old halo not only deeper than, but also with a longer baseline and better cadence than, e.g., PS1 3π. This will make the LSST ideal to study populations of variable stars such as RR Lyrae stars (RRL). Here, we address the question of observing strategy optimization of LSST, as survey footprint definition, single-visit exposure time, as well as the cadence of repeat visits in different filters are yet to be finalized. We present metrics used to assess the impact of different observing strategies on the reliable detectability and classification of standard candle variable stars, including detection of amplitude, period, and phase modulation effects of RRL (the so-called Blazhko effect), by evaluating metrics for simulated potential survey designs. So far, due to the depths and cadences of typical all-sky surveys, it has been nearly impossible to study this effect on a larger sample. All-sky surveys with relatively few observations over a moderately long baseline allow only for fitting phase-folded RRL light curves, thus integrating over the complete survey length and hiding any information regarding possible period or phase modulation during the survey. On the other hand, surveys with cadences fit to detect slightly changing light curves usually have a relatively small footprint. LSST’s survey strategy, however, will allow for studying variable stars in a way that makes population studies possible.

Characterizing Sparse Asteroid Light Curves with Gaussian Processes

In the era of wide-field surveys like the Zwicky Transient Facility and the Rubin Observatory’s Legacy Survey of Space and Time, sparse photometric measurements constitute an increasing percentage of asteroid observations, particularly for asteroids newly discovered in these large surveys. Follow-up observations to supplement these sparse data may be prohibitively expensive in many cases, so to overcome these sampling limitations, we introduce a flexible model based on Gaussian processes to enable Bayesian parameter inference of asteroid time-series data. This model is designed to be flexible and extensible, and can model multiple asteroid properties such as the rotation period, light-curve amplitude, changing pulse profile, and magnitude changes due to the phase-angle evolution at the same time. Here, we focus on the inference of rotation periods. Based on both simulated light curves and real observations from the Zwicky Transient Facility, we show that the new model reliably infers rotational periods from sparsely sampled light curves and generally provides well-constrained posterior probability densities for the model parameters. We propose this framework as an intermediate method between fast but very limited-period detection algorithms and much more comprehensive but computationally expensive shape-modeling based on ray-tracing codes.