- NSF-PAR ID:
- 10025174
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Space Physics
- Volume:
- 122
- Issue:
- 4
- ISSN:
- 2169-9380
- Page Range / eLocation ID:
- 4348 to 4356
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
Abstract The orbits of close-in exoplanets provide clues to their formation and evolutionary history. Many close-in exoplanets likely formed far out in their protoplanetary disks and migrated to their current orbits, perhaps via high-eccentricity migration (HEM), a process that can also excite obliquities. A handful of known exoplanets are perhaps caught in the act of HEM, as they are observed on highly eccentric orbits with tidal circularization timescales shorter than their ages. One such exoplanet is Kepler-1656 b, which is also the only known nongiant exoplanet (<100
M ⊕) with an extreme eccentricity (e = 0.84). We measured the sky-projected obliquity of Kepler-1656 b by observing the Rossiter–McLaughlin effect during a transit with the Keck Planet Finder. Our data are consistent with an aligned orbit but are also consistent with moderate misalignment withλ < 50° at 95% confidence, with the most likely solution of deg. A low obliquity would be an unlikely outcome of most eccentricity-exciting scenarios, but we show that the properties of the outer companion in the system are consistent with the coplanar HEM mechanism. Alternatively, if the system is not relatively coplanar (≲20° mutual inclination), Kepler-1656 b may be presently at a rare snapshot of long-lived eccentricity oscillations that do not induce migration. Kepler-1656 b is only the fourth exoplanet withe > 0.8 to have its obliquity constrained; expanding this population will help establish the degree to which orbital misalignment accompanies migration. Future work that constrains the mutual inclinations of outer perturbers will be key for distinguishing plausible mechanisms. -
Abstract We analyze the MOA-2020-BLG-208 gravitational microlensing event and present the discovery and characterization of a new planet, MOA-2020-BLG-208Lb, with an estimated sub-Saturn mass. With a mass ratio q = 3.17 − 0.26 + 0.28 × 10 − 4 , the planet lies near the peak of the mass-ratio function derived by the MOA collaboration and near the edge of expected sample sensitivity. For these estimates we provide results using two mass-law priors: one assuming that all stars have an equal planet-hosting probability, and the other assuming that planets are more likely to orbit around more massive stars. In the first scenario, we estimate that the lens system is likely to be a planet of mass m planet = 46 − 24 + 42 M ⊕ and a host star of mass M host = 0.43 − 0.23 + 0.39 M ⊙ , located at a distance D L = 7.49 − 1.13 + 0.99 kpc . For the second scenario, we estimate m planet = 69 − 34 + 37 M ⊕ , M host = 0.66 − 0.32 + 0.35 M ⊙ , and D L = 7.81 − 0.93 + 0.93 kpc . The planet has a projected separation as a fraction of the Einstein ring radius s = 1.3807 − 0.0018 + 0.0018 . As a cool sub-Saturn-mass planet, this planet adds to a growing collection of evidence for revised planetary formation models.more » « less
-
Large models such as GPT-3 and ChatGPT have transformed deep learning (DL), powering applications that have captured the public's imagination. Such models must be trained on multiple GPUs due to their size and computational load, driving the development of a bevy of model parallelism techniques and tools. Navigating suchmore » « less
parallelism choices, however, is a new burden for DL users such as data scientists, domain scientists, etc., who may lack the necessary systems knowhow. The need formodel selection , which leads to many models to train due to hyper-parameter tuning or layer-wise finetuning, compounds the situation with two more burdens:resource apportioning andscheduling. In this work, we unify these three burdens by formalizing them as a joint problem that we call SPASE: Select a Parallelism, Allocate resources, and Schedule. We propose a new information system architecture to tackle the SPASE problem holistically, exploiting the performance opportunities presented by joint optimization. We devise an extensible template for existing parallelism schemes and combine it with an automated empirical profiler for runtime estimation. We then formulate SPASE as an MILP. We find that direct use of an MILP-solver is significantly more effective than several baseline heuristics. We optimize the system runtime further with an introspective scheduling approach. We implement all these techniques into a new data system we call Saturn. Experiments with benchmark DL workloads show that Saturn achieves 39-49% lower model selection runtimes than current DL practice.