Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1403.0430

Bayesian search for low-mass planets around nearby M dwarfs. Estimates for occurrence rate based on global detectability statistics

Mikko Tuomi·Hugh R. A. Jones·John R. Barnes·Guillem Anglada-Escudé·James S. Jenkins
View original source ↗Read source document ↗Export metadata ↓

Abstract

Due to their higher planet-star mass-ratios, M dwarfs are the easiest targets for detection of low-mass planets orbiting nearby stars using Doppler spectroscopy. Furthermore, because of their low masses and luminosities, Doppler measurements enable the detection of low-mass planets in their habitable zones that correspond to closer orbits than for Solar-type stars. We re-analyse literature UVES radial velocities of 41 nearby M dwarfs in a combination with new velocities obtained from publicly available spectra from the HARPS-ESO spectrograph of these stars in an attempt to constrain any low-amplitude Keplerian signals. We apply Bayesian signal detection criteria, together with posterior sampling techniques, in combination with noise models that take into account correlations in the data and obtain estimates for the number of planet candidates in the sample. More generally, we use the estimated detection probability function to calculate the occurrence rate of low-mass planets around nearby M dwarfs. We report eight new planet candidates in the sample (orbiting GJ 27.1, GJ 160.2, GJ 180, GJ 229, GJ 422, and GJ 682), including two new multiplanet systems, and confirm two previously known candidates in the GJ 433 system based on detections of Keplerian signals in the combined UVES and HARPS radial velocity data that cannot be explained by periodic and/or quasiperiodic phenomena related to stellar activities. Finally, we use the estimated detection probability function to calculate the occurrence rate of low-mass planets around nearby M dwarfs. According to our results, M dwarfs are hosts to an abundance of low-mass planets and the occurrence rate of planets less massive than 10 M$_{\oplus}$ is of the order of one planet per star, possibly even greater. ...

Explore related subjects

astro-ph.EP

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mikko Tuomi, Hugh R. A. Jones, John R. Barnes, Guillem Anglada-Escudé, James S. Jenkins. 2014-03-03. Bayesian search for low-mass planets around nearby M dwarfs. Estimates for occurrence rate based on global detectability statistics. https://doi.org/10.1093/mnras%2Fstu358

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Efficient reduction of stellar contamination and noise in planetary transmission spectra using neural networks

The characterization of exoplanetary atmospheres has been transformed by the James Webb Space Telescope (JWST), whose infrared sensitivity enables transmission spectroscopy at unprecedented precision. However, stellar heterogeneities (e.g., spots and faculae) remain a dominant source of contamination that can bias atmospheric retrievals if not properly corrected. We present a methodology for reducing stellar contamination and instrument-specific noise from exoplanet transmission spectra using neural networks, in particular the so-called Denoising AutoEncoders (DAEs). Our goals are to enable fast, accurate corrections that improve the reliability of atmospheric parameter retrievals and to promote the use of unsupervised algorithms for efficient data processing. We designed and trained DAE architectures using large synthetic datasets of terrestrial (TRAPPIST-1e analogues) and sub-Neptune (K2-18b analogues) planets. Atmospheric retrieval experiments were then performed on contaminated spectra in order to compare our deep-learning approach against standard correction methods in terms of accuracy and computational cost. Our autoencoders successfully reconstruct uncontaminated spectra, preserving essential molecular features even in low-S/N regimes. In retrieval tests, the denoising autoencoder pre-processing yields atmospheric parameter estimates broadly comparable to those obtained with simultaneous stellar-contamination fitting. Notably, our method maintains a much lower computational cost, approximately one order of magnitude smaller. These results demonstrate that DAEs outperform conventional correction methods in computational efficiency while maintaining high accuracy, paving the way for their integration into future atmospheric characterization pipelines for both rocky and sub-Neptune exoplanets.

astro-ph.EP↗
arXiv2026

You Shall Not Pass (Without Modeling): High-Resolution Analysis of KMT-2019-BLG-0253 using MORIA

We present the Microlensing Object high-Resolution Imaging Analysis pipeline, or \texttt{MORIA}. This is an automated procedure to reduce high-resolution \textit{HST} images of microlensing targets, build empirical point-spread function models from the data, and perform simultaneous multi-star PSF fitting to blended sources, lenses, and neighbor stars. We have developed and tested this pipeline using HST observations of the microlensing event KMT-2019-BLG-0253, which shows clear evidence of three highly-blended stars in HST. Under our assumed source and lens identification, we determine a host mass of $M_{host} = 0.65 \pm 0.04M_{\odot}$. We have reduced the number of possible solutions for this target by a factor of two, with the remaining solution subject to the well known close-wide degeneracy. We determine a planet mass of $m_{p} = 7.18 \pm 0.40 M_{\oplus}$ (close) or $m_{p} = 9.48 \pm 1.13 M_{\oplus}$ (wide), and distance to the lens system of $D_L= 2.64 \pm 0.22$ kpc. This target lies within the upcoming Roman Telescope Galactic Bulge Time Domain Survey (GBTDS) field. Future analysis of the Roman data for this event can confirm or reject the initial source and lens identifications presented in this work. Finally, this work demonstrates the importance of using an automated high-resolution imaging tool to inform light curve modeling for microlensing planets found during the GBTDS.

astro-ph.EP↗
arXiv2026

Satellite Survival through Gas Redistribution in a Low-viscosity Disk

We investigate satellite survival through gas redistribution in a dense circumplanetary disk with a low dimensionless viscosity parameter $α\sim 10^{-6}$. The model combines modal Lindblad excitation, launch-dependent shock deposition, 3D effects, and an instantaneous Rayleigh adjustment that conserves angular momentum and suppresses sharp density gradients. An isolated Ganymede-mass satellite depletes the disk outside its orbit and stalls under a calibrated three-dimensional Lindblad torque. This behavior is consistent with the non-feedback branch of Rafikov's stalling criterion. The inward migration stalls near 15 Jupiter radii ($R_J$) when the gas depletion exterior to the satellite's orbit reduces the outer torque by the amount required to balance the torque of the undepleted inner disk, even when we adopt a transport prescription that begins smoothing density gradients halfway to Rayleigh marginality. Likewise, two Callisto masses form an extended depleted region and stall in nearly steady orbits before a late close encounter; however, a self-consistent disk response to satellite eccentricity remains to be modeled. A simulation of a Ganymede-mass satellite adds a specified source of unsaturated local angular momentum deposition by buoyancy torques, reducing the late radial oscillations indirectly caused by non-local shock deposition. Lastly, we redistribute gas while conserving angular momentum to proactively inhibit Rossby-wave unstable pressure bumps at the gap edges. We find that this approach preserves gap clearing and strong migration suppression for Ganymede and the two Callisto masses. In the pressure-adjusted run, the two Callisto masses are captured in a 7:5 resonance and retain eccentricities below 0.06.

astro-ph.EP↗