Gamma-ray Bursts (GRBs) are ﬂashes of gammarays linked with extremely energetic explosions indistant galaxies. Since the launch of the revolutionary Swift Gamma-ray Burst satellite in 2004, well over 500 GRB afterglows have been observed. As the number of observed GRBs continues to accumulate, a natural consequence is that the available follow-up resources need to be used more conservatively in order to maximize the science output from available telescope time. As such, it is becoming increasingly important to be able to rapidly identify bursts of interest using early-time metrics. High-redshift GRBs - those which lie the furthest from us and thus allow us to peer into the earliest ages of the universe - are of particular interest. In this project, we are making use of machine-learned classification with CART (Classification and Regression Trees) for identification of high-redshift candidates using a selection of early-time and post-processed metrics from the three telescopes onboard Swift.
When a main sequence star passes within the sphere of influence of a super-massive black hole, tidal forces will overcome the star's self-gravity, tearing the star apart. For sufficiently low-mass black holes, the disruption occurs outside the event horizon. As the stellar debris returns to the black hole, the infalling material forms an accretion disk, leading to a bright outburst known as a tidal disruption flare (TDF). As such, TDFs represent a relatively unique probe of the masses and gravitational potential of the central black holes in distant, otherwise quiescent galaxies.
Prof. Bloom is principal investigator of the TDF key project for the Palomar Transient Factory collaboration.
Once a typical star exhausts its core hydrogen fuel on the Main Sequence it expands in size and begins fusing hydrogen in a shell surrounding its core of inert helium. This shift in energy generation defines the Red Giant Branch. Eventually, the shell hydrogen is also exhausted and the star's core gravitationally contracts, heating the core helium until helium fusion ignites, signifying the star's transition to the Horizontal Branch. As the star evolves on the Horizontal Branch it passes through the Instability Strip. This is a small region of stellar parameter-space in which the star exists as a Pulsating Variable, typified by an atmosphere with a harmonically oscillating opacity (kappa mechanism). The periodic expansion and contraction of the star's radius is matched by similar periodic oscillations in luminosity (observed brightness) and temperature (observed color).
We leverage small telescope (1-meter) observing resources to study the brightness oscillations of pulsating variable stars at wavelengths ranging from 0.35 to 2.2 microns. Because of their driving physical mechanism, pulsating variables oscillate with a period that is related to their intrinsic luminosity. For a century astronomers have utilized these Period-Luminosity Relations to derive distances, and have continually improved these relations to increase the accuracy of these distance measurements. Our goal is to even further improve these P-L Relations by combining broad wavelength coverage of stars with parallactic distances (HIPPARCOS) and machine learning analysis of resultant observational features. Infrared-based P-L Relations will facilitate the next generation of ground- and space-based telescopes (TMT, JWST) in deriving highly accurate distances to nearby galaxies, which in turn will play an important part in further constraining our understanding of basic cosmological parameters.