Members that collaborated to generate this roadmap:
Joshua Pepper - Lehigh University, Michael Lund - Vanderbilt University, Savannah Jacklin - Vanderbilt University
Primary subgroup contact:
Subgroup MAF engineer:
- Rolf Chini (s)
- David Ciardi - Caltech (p)
- Lucas Cieza (s)
- Rosanne Di Stefano (s)
- Martin Donachie - University of Aukland (p)
- Savannah Jacklin - Vanderbilt University (p)
- Vicky Kalogera (s)
- Michael Lund - Vanderbilt University (p)
- Lucas Macri - Texas A&M (s)
- Anjum Mukadam - University of Washington (s)
- David Nidever (s)
- Joshua Pepper - Lehigh University (p)
- Peter Plavchan - Missouri State (s)
- Markus Rabus - PUC, Chile (p)
- Nicholas Rattenbury - University of Aukland (p)
- Stephen Ridgeway - NOAO (s)
- Joseph Rodriguez -Harvard Smithsonian Center for Astrophysics (p)
- Dimitar Sasselov - Harvard Smithsonian Center for Astrophysics (p)
- Avi Shporer - Caltech/JPL (p)
- Keivan Stassun - Vanderbilt University (s)
- Virginia Trimble (s)
- Lucianne Walkowicz (s)
Monthly call times:
Second Tuesday of each month, 1:30-2:30 Eastern TIme (US). Calls on Zoom, US link here, international link here, phone info: Dial: +1 646 558 8656 (US Toll) or +1 408 638 0968 (US Toll), Meeting ID: 381 852 937
LSST will acquire enough photometric measurement of individual stars that we should examine how it might discover transiting exoplanets. The challenges are significant:
- Stars will generally be too faint for RV followup, confirmation, or characterization
- Objects observed with regular cadence will generally not have a high enough number of observations for detection
- Crowding will present a large number of false positives. However, LSST will uniquely be able to discover planets in as-yet unprobed stellar populations. The most significant of these are:
- Low-mass stars
- Star clusters
- White dwarfs
- Nearby galaxies (e.g. LMC, SMC)
- Undersampled stellar populations
Current Work Published Materials
Transiting Planets with LSST. I. Potential for LSST Exoplanet Detection
Michael B. Lund, Joshua Pepper, and Keivan G. Stassun
The Astronomical Journal, Volume 149, Number 1
The Large Synoptic Survey Telescope (LSST) is designed to meet several scientific objectives over a 10 year synoptic sky survey. Beyond its primary goals, the large amount of LSST data can be exploited for additional scientific purposes. We show that LSST data are sufficient to detect the transits of exoplanets, including planets orbiting stars that are members of stellar populations that have so far been largely unexplored. Using simulated LSST light curves, we find that existing transit detection algorithms can identify the signatures of Hot Jupiters around solar-type stars, Hot Neptunes around K-dwarfs, and (in favorable cases) Super-Earths in habitable-zone orbits of M-dwarfs. We also find that LSST may identify Hot Jupiters orbiting stars in the Large Magellanic Cloud—a remarkable possibility that would advance exoplanet science into the extragalactic regime.
Transiting Planets with LSST. II. Period Detection of Planets Orbiting 1 Solar Mass Hosts
Savannah R. Jacklin, Michael B. Lund, Joshua Pepper, and Keivan G. Stassun
The Astronomical Journal, Volume 150, Number 1
The Large Synoptic Survey Telescope (LSST) will photometrically monitor ~1 billion stars for 10 years. The resulting light curves can be used to detect transiting exoplanets. In particular, as demonstrated by Lund et al., LSST will probe stellar populations currently undersampled in most exoplanet transit surveys, including out to extragalactic distances. In this paper we test the efficiency of the box-fitting least-squares (BLS) algorithm for accurately recovering the periods of transiting exoplanets using simulated LSST data. We model planets with a range of radii orbiting a solar-mass star at a distance of 7 kpc, with orbital periods ranging from 0.5 to 20 days. We find that standard-cadence LSST observations will be able to reliably recover the periods of Hot Jupiters with periods shorter than ~3 days; however, it will remain a challenge to confidently distinguish these transiting planets from false positives. At the same time, we find that the LSST deep-drilling cadence is extremely powerful: the BLS algorithm successfully recovers at least 30% of sub-Saturn-size exoplanets with orbital periods as long as 20 days, and a simple BLS power criterion robustly distinguishes ~98% of these from photometric (i.e., statistical) false positives.
Metrics for Optimization of Large Synoptic Survey Telescope Observations of Stellar Variables and Transients
Michael B. Lund, Robert J. Siverd, Joshua A. Pepper, and Keivan G. Stassun
Publications of the Astronomical Society of the Pacific, Volume 128, Number 960
The Large Synoptic Survey Telescope (LSST) will be the largest time-domain photometric survey ever. In order to maximize the LSST science yield for a broad array of transient stellar phenomena, it is necessary to optimize the survey cadence, coverage, and depth via quantitative metrics that are specifically designed to characterize the time-domain behavior of various types of stellar transients. In this paper, we present three such metrics built on the LSST Metric Analysis Framework model. Two of the metrics quantify the ability of LSST to detect non-periodic and/or non-recurring transient events and the ability of LSST to reliably measure periodic signals of various timescales. The third metric provides a way to quantify the range of stellar parameters in the stellar populations that LSST will probe. We provide example uses of these metrics and discuss some implications based on these metrics for optimization of the LSST survey for observations of stellar variables and transients.
Transiting Planets with LSST III: Detection Rate per Year of OperationTransiting Planets with LSST III: Detection Rate per Year of Operation
Savannah R. Jacklin, Michael B. Lund, Joshua Pepper, Robert J. Siverd, and Keivan G. Stassun
The Astronomical Journal, Volume 153, Issue 4, article id. 186
The Large Synoptic Survey Telescope (LSST) will generate light curves for approximately 1 billion stars. Our previous work has demonstrated that, by the end of the LSST 10-year mission, large numbers of transiting exoplanetary systems could be recovered using the LSST “deep-drilling” cadence. Here, we extend our previous work to examine how the recoverability of transiting planets over a range of orbital periods and radii evolves per year of LSST operation. As specific example systems, we consider hot Jupiters orbiting solar-type stars and hot Neptunes orbiting K-dwarfs at distances from Earth of several kpc, as well as super-Earths orbiting nearby low-mass M-dwarfs. The detection of transiting planets increases steadily with the accumulation of data over time, generally becoming large (≳10%) after 4-6 years of operation. However, we also find that short-period (≲2 days) hot Jupiters orbiting G-dwarfs and hot Neptunes orbiting K-dwarfs can already be discovered within the first 1-2 years of LSST operation.
Works in Progress
- Testing and comparison of multiple transit detection algorithms for simulated LSST light curves to identify the best approach for detecting planets in the sparsely sampled LSST data - Lund
- Development of LSST metrics to use with Metric Analysis Framework that will examine period sampling and star counts per field. These metrics will also be made available via the LSST Github - Lund
- Exoplanet period recoverability in the LMC -Lund
- What is the current plan for Deep Drilling? How many fields? Where? What is the DD baseline, and does it vary between fields?
- How might we go about modelling intrinsic stellar variability? That covers eclipsing, pulsational, and rotational variables, but also includes irregular variability like flares, as well as baseline photometric jitter. Useful papers:
- Are there sets of young stars that LSST could probe? Would we be able to detect transits amidst the variability?
- Should we propose HST or Spitzer observations of DD fields? If so, what sorts of observations?
- What do we expect of LSST commissioning?
- What kind of followup of transit candidates would be possible?
- Time-series photometric confirmation of depth and duration
- Multiband time-series photometry to identify EB blends
- High angular resolution imaging (AO or speckle) to identify third light or blends
- RV spectroscopy, to identify EBs, if not planet orbits