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Register a new userRadial velocity information content of M dwarf spectra in the near-infrared
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Aims: We evaluate the radial velocity (RV) information content and achievable precision on M0-M9 spectra covering the ZYJHK bands. We do so while considering both a perfect atmospheric transmission correction and discarding areas polluted by deep telluric features, as done in previous works. Methods: To simulate the M-dwarf spectra, PHOENIX-ACES model spectra were employed; they were convolved with rotational kernels and instrumental profiles to reproduce stars with a $v.sin{i}$ of 1.0, 5.0, and 10.0 km/s when observed at resolutions of 60 000, 80 000, and 100 000. We considered the RV precision as calculated on the whole spectra, after discarding strongly polluted areas, and after applying a perfect telluric correction. In our simulations we paid particular attention to the details of the convolution and sampling of the spectra, and we discuss their impact on the final spectra. Results: Our simulations show that the most important parameter ruling the difference in attainable precision between the considered bands is the spectral type. For M0-M3 stars, the bands that deliver the most precise RV measurements are the Z, Y, and H band, with relative merits depending on the parameters of the simulation. For M6-M9 stars, the bands show a difference in precision that is within a factor of $sim$2 and does not clearly depend on the band; this difference is reduced to a factor smaller than $sim$1.5 if we consider a non-rotating star seen at high resolution. We also show that an M6-M9 spectrum will deliver a precision about two times better as an M0-M3 spectra with the same signal-to-noise ratio. Finally, we note that the details of modelling the Earth atmosphere and interpreting the results have a significant impact on which wavelength regions are discarded when setting a limit threshold at 2-3%. (abridged)
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Precision radial velocity (RV) measurements in the near-infrared are a powerful tool to detect and characterize exoplanets around low-mass stars or young stars with higher magnetic activity. However, the presence of strong telluric absorption lines and emission lines in the near infrared that significantly vary in time can prevent extraction of RV information from these spectra by classical techniques, which ignore or mask the telluric lines. We present a methodology and pipeline to derive precision RVs from near-infrared spectra using a forward-modeling technique. We applied this to spectra with a wide wavelength coverage (Y, J, and H bands, simultaneously), taken by the InfraRed Doppler (IRD) spectrograph on the Subaru 8.2-m telescope. Our pipeline extracts the instantaneous instrumental profile of the spectrograph for each spectral segment, based on a reference spectrum of the laser-frequency comb that is injected into the spectrograph simultaneously with the stellar light. These profiles are used to derive the intrinsic stellar template spectrum, which is free from instrumental broadening and telluric features, as well as model and fit individual observed spectra in the RV analysis. Implementing a series of numerical simulations using theoretical spectra that mimic IRD data, we test the pipeline and show that IRD can achieve $<2$ m s$^{-1}$ precision for slowly rotating mid-to-late M dwarfs with a signal-to-noise ratio $> 100$ per pixel at 1000 nm. Dependences of RV precision on various stellar parameters (e.g., $T_{rm eff}$, $vsin i$, [Fe/H]) and the impact of telluric-line blendings on the RV accuracy are discussed through the mock spectra analyses. We also apply the RV-analysis pipeline to the observed spectra of GJ 699 and TRAPPIST-1, demonstrating that the spectrograph and the pipeline are capable of an RV accuracy of $<3$ m s$^{-1}$ at least on a time scale of a few months.
SPIRou is the newest spectropolarimeter and high-precision velocimeter that has recently been installed at the Canada-France-Hawaii Telescope on Maunakea, Hawaii. It operates in the near-infrared and simultaneously covers the 0.98-2.35 {mu}m domain at high spectral resolution. SPIRou is optimized for exoplanet search and characterization with the radial-velocity technique, and for polarization measurements in stellar lines and subsequent magnetic field studies. The host of the transiting hot Jupiter HD 189733 b has been observed during early science runs. We present the first near-infrared spectropolarimetric observations of the planet-hosting star as well as the stellar radial velocities as measured by SPIRou throughout the planetary orbit and two transit sequences. The planetary orbit and Rossiter-McLaughlin anomaly are both investigated and modeled. The orbital parameters and obliquity are all compatible with the values found in the optical. The obtained radial-velocity precision is compatible with about twice the photon-noise estimates for a K2 star under these conditions. The additional scatter around the orbit, of about 8 m/s, agrees with previous results that showed that the activity-induced scatter is the dominant factor. We analyzed the polarimetric signal, Zeeman broadening, and chromospheric activity tracers such as the 1083nm HeI and the 1282nm Pab{eta} lines to investigate stellar activity. First estimates of the average unsigned magnetic flux from the Zeeman broadening of the FeI lines give a magnetic flux of 290+-58 G, and the large-scale longitudinal field shows typical values of a few Gauss. These observations illustrate the potential of SPIRou for exoplanet characterization and magnetic and stellar activity studies.
We present observations with the planet finder SPHERE of a selected sample of the most promising radial velocity (RV) companions for high-contrast imaging. Using a Monte Carlo simulation to explore all the possible inclinations of the orbit of wide RV companions, we identified the systems with companions that could potentially be detected with SPHERE. We found the most favorable RV systems to observe are : HD,142, GJ,676, HD,39091, HIP,70849, and HD,30177 and carried out observations of these systems during SPHERE Guaranteed Time Observing (GTO). To reduce the intensity of the starlight and reveal faint companions, we used Principle Component Analysis (PCA) algorithms alongside angular and spectral differential imaging. We injected synthetic planets with known flux to evaluate the self-subtraction caused by our data reduction and to determine the 5$sigma$ contrast in the J band $vs$ separation for our reduced images. We estimated the upper limit on detectable companion mass around the selected stars from the contrast plot obtained from our data reduction. Although our observations enabled contrasts larger than 15 mag at a few tenths of arcsec from the host stars, we detected no planets. However, we were able to set upper mass limits around the stars using AMES-COND evolutionary models. We can exclude the presence of companions more massive than 25-28 MJup around these stars, confirming the substellar nature of these RV companions.
In the context of large spectroscopic surveys of stars, data-driven methods are key in deducing physical parameters for millions of spectra in a short time. Convolutional neural networks (CNNs) enable us to connect observables (e.g. spectra, stellar magnitudes) to physical properties (atmospheric parameters, chemical abundances, or labels in general). We trained a CNN, adopting stellar atmospheric parameters and chemical abundances from APOGEE DR16 (resolution R=22500) data as training set labels. As input, we used parts of the intermediate-resolution RAVE DR6 spectra (R~7500) overlapping with the APOGEE DR16 data as well as broad-band ALL_WISE and 2MASS photometry, together with Gaia DR2 photometry and parallaxes. We derived precise atmospheric parameters Teff, log(g), and [M/H] along with the chemical abundances of [Fe/H], [alpha/M], [Mg/Fe], [Si/Fe], [Al/Fe], and [Ni/Fe] for 420165 RAVE spectra. The precision typically amounts to 60K in Teff, 0.06 in log(g) and 0.02-0.04 dex for individual chemical abundances. Incorporating photometry and astrometry as additional constraints substantially improves the results in terms of the accuracy and precision of the derived labels. We provide a catalogue of CNN-trained atmospheric parameters and abundances along with their uncertainties for 420165 stars in the RAVE survey. CNN-based methods provide a powerful way to combine spectroscopic, photometric, and astrometric data without the need to apply any priors in the form of stellar evolutionary models. The developed procedure can extend the scientific output of RAVE spectra beyond DR6 to ongoing and planned surveys such as Gaia RVS, 4MOST, and WEAVE. We call on the community to place a particular collective emphasis and on efforts to create unbiased training samples for such future spectroscopic surveys.
Context. The HARPS spectrograph provides state-of-the-art stellar radial velocity (RV) measurements with a precision down to 1 m/s. The spectra are extracted with a dedicated data-reduction software (DRS) and the RVs are computed by CCF with a numerical mask. Aims. The aim of this study is three-fold: (i) Create easy access to the public HARPS RV data set. (ii) Apply the new public SERVAL pipeline to the spectra, and produce a more precise RV data set. (iii) Check whether the precision of the RVs can be further improved by correcting for small nightly systematic effects. Methods. For each star observed with HARPS, we downloaded the publicly available spectra from the ESO archive and recomputed the RVs with SERVAL. We then computed nightly zero points (NZPs) by averaging the RVs of quiet stars. Results. Analysing the RVs of the most RV-quiet stars, whose RV scatter is < 5 m/s, we find that SERVAL RVs are on average more precise than DRS RVs by a few percent. We find three significant systematic effects, whose magnitude is independent of the software used for the RV derivation: (i) stochastic variations with a magnitude of 1 m/s; (ii) long-term variations, with a magnitude of 1 m/s and a typical timescale of a few weeks; and (iii) 20-30 NZPs significantly deviating by a few m/s. In addition, we find small (< 1 m/s) but significant intra-night drifts in DRS RVs before the 2015 intervention, and in SERVAL RVs after it. We confirm that the fibre exchange in 2015 caused a discontinuous RV jump, which strongly depends on the spectral type of the observed star: from 14 m/s for late F-type stars, to -3 m/s for M dwarfs. Conclusions. Our NZP-corrected SERVAL RVs can be retrieved from a user-friendly, public database. It provides more than 212 000 RVs for about 3000 stars along with many auxiliary information, NZP corrections, various activity indices, and DRS-CCF products.
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