The capabilities and performance of the Automated Planet Finder Telescope with the implementation of a dynamic scheduler

TL;DR

This study evaluates the Automated Planet Finder telescope's performance, demonstrating its efficiency in obtaining precise radial velocity measurements, especially for M-type stars, and introduces a dynamic scheduler for optimized long-term surveys.

Contribution

The paper presents initial performance results of the APF telescope, quantifies its efficiency across different star types, and introduces a novel dynamic scheduling algorithm for improved survey targeting.

Findings

M-type stars require half the photons for the same precision as G and K stars.

APF/Levy achieves comparable speed-on-sky to Keck/HIRES for M stars.

Strong correlation between photon count and internal uncertainty in RV measurements.

Abstract

We report initial performance results emerging from 600 hours of observations with the Automated Planet Finder (APF) telescope and Levy Spectrometer located at UCO/Lick Observatory. We have obtained multiple spectra of 80 G, K and M-type stars, which comprise 4,954 individual Doppler radial velocity (RV) measurements with a median internal uncertainty of 1.35 ms$^{-1}$. We find a strong, expected correlation between the number of photons accumulated in the 5000-6200$\AA$ iodine region of the spectrum, and the resulting internal uncertainty estimates. Additionally, we find an offset between the population of G and K stars and the M stars within the data set when comparing these parameters. As a consequence of their increased spectral line densities, M-type stars permit the same level of internal uncertainty with 2x fewer photons than G-type and K-type stars. When observing M stars, we show that the APF/Levy has essentially the same speed-on-sky as Keck/HIRES for precision RVs. In the interest of using the APF for long-duration RV surveys, we have designed and implemented a dynamic scheduling algorithm. We discuss the operation of the scheduler, which monitors ambient conditions and combines on-sky information with a database of survey targets to make intelligent, real-time targeting decisions.