The unprecedented volume and rate of transient events that will be discovered by the Large Synoptic Survey Telescope (LSST) demands that the astronomical community update its followup paradigm. Alert-brokers -automated software system to sift through, characterize, annotate and prioritize events for followup -will be critical tools for managing alert streams in the LSST era. The Arizona-NOAO Temporal Analysis and Response to Events System (ANTARES) is one such broker. In this work, we develop a machine learning pipeline to characterize and classify variable and transient sources only using the available multiband optical photometry. We describe three illustrative stages of the pipeline, serving the three goals of early, intermediate and retrospective classification of alerts. The first takes the form of variable vs transient categorization, the second, a multi-class typing of the combined variable and transient dataset, and the third, a purity-driven subtyping of a transient class. While several similar algorithms have proven themselves in simulations, we validate their performance on real observations for the first time. We quantitatively evaluate our pipeline on sparse, unevenly sampled, heteroskedastic data from various existing observational campaigns, and demonstrate very competitive classification performance. We describe our progress towards adapting the pipeline developed in this work into a real-time broker working on live alert streams from time-domain surveys.

Most massive stars end their lives as Red Supergiants (RSGs), a short-lived evolution phase when they are known to pulsate with varying amplitudes. The RSG period-luminosity (PL) relation has been measured in the Milky Way, the Magellanic Clouds and M33 for about 120 stars in total. Using over 1500 epochs of R band monitoring from the Palomar Transient Factory (PTF) survey over a five-year period, we study the variability of 255 spectroscopically cataloged RSGs in M31. We find that all RGSs brighter than M K ≈ −10 mag (log(L/L ) > 4.8) are variable at ∆m R > 0.05 mag. Our period analysis finds 63 with significant pulsation periods. Using the periods found and the known values of M K for these stars, we derive the RSG PL relation in M31 and show that it is consistent with those derived earlier in other galaxies of different metallicities. We also detect, for the first time, a sequence of likely first-overtone pulsations. Comparison to stellar evolution models from MESA confirms the first overtone hypothesis and indicates that the variable stars in this sample have 12 M < M < 24 M . As these RSGs are the immediate progenitors to Type II-P core-collapse supernovae (SNe), we also explore the implication of their variability in the initial-mass estimates for SN progenitors based on archival images of the progenitors. We find that this effect is small compared to the present measurement errors.

Novae undergo a supersoft X-ray phase of varying duration after the optical outburst. Such transient post-nova supersoft X-ray sources (SSSs) are the majority of the observed SSSs in M31. In this paper, we use the post-nova evolutionary models of Wolf et al. to compute the expected population of post-nova SSSs in M31. We predict that depending on the assumptions about the WD mass distribution in novae, at any instant there are about 250 -600 post-nova SSSs in M31 with (unabsorbed) 0.2-1.0 keV luminosity L x 10 36 erg/s. Their combined unabsorbed luminosity is of the order of ∼ 10 39 erg/s. Their luminosity distribution shows significant steepening around log(L x ) ∼ 37.7 -38 and becomes zero at L x ≈ 2 × 10 38 erg/s, the maximum L x achieved in the post-nova evolutionary tracks. Their effective temperature distribution has a roughly power law shape with differential slope of ≈ 4-6 up to the maximum temperature of T eff ≈ 1.5 × 10 6 K.We compare our predictions with the results of the XMM-Newton monitoring of the central field of M31 between 2006 and 2009. The predicted number of post-nova SSSs exceed the observed number by a factor of ≈ 2-5, depending on the assumed WD mass distribution in novae. This is good agreement, considering the number and magnitude of uncertainties involved in calculations of the post-nova evolutionary models and their X-ray output. Furthermore, only a moderate circumstellar absorption, with hydrogen column density of the order of ∼ 10 21 cm −2 , will remove the discrepancy.

We propose a new strategy of finding strongly-lensed supernovae (SNe) by monitoring known galaxyscale strong-lens systems. Strongly lensed SNe are potentially powerful tools for the study of cosmology, galaxy evolution, and stellar populations, but they are extremely rare. By targeting known strongly lensed starforming galaxies, our strategy significantly boosts the detection efficiency for lensed SNe compared to a blind search. As a reference sample, we compile the 128 galaxy-galaxy stronglens systems from the Sloan Lens ACS Survey (SLACS), the SLACS for the Masses Survey, and the Baryon Oscillation Spectroscopic Survey Emission-Line Lens Survey. Within this sample, we estimate the rates of strongly-lensed Type Ia SN (SNIa) and core-collapse SN (CCSN) to be 1.23 ± 0.12 and 10.4±1.1 events per year, respectively. The lensed SN images are expected to be widely separated with a median separation of 2 arcsec. Assuming a conservative fiducial lensing magnification factor of 5 for the most highly magnified SN image, we forecast that a monitoring program with a single-visit depth of 24.7 mag (5σ point source, r band) and a cadence of 5 days can detect 0.49 strongly-lensed SNIa event and 2.1 strongly-lensed CCSN events per year within this sample. Our proposed targeted-search strategy is particularly useful for prompt and efficient identifications and follow-up observations of strongly-lensed SN candidates. It also allows telescopes with small field of views and limited time to efficiently discover strongly-lensed SNe with a pencil-beam scanning strategy.