arXiv subjects

L. Nielsen

Publications and source records attributed to L. Nielsen.

3 recordsLinked to original sources

arXiv2023

DREAM I. Orbital architecture orrery

The distribution of close-in exoplanets is shaped by a complex interplay between atmospheric and dynamical processes. The Desert-Rim Exoplanets Atmosphere and Migration (DREAM) program aims at disentangling those processes through the study of the hot Neptune desert, whose rim hosts planets that are undergoing, or survived, atmospheric evaporation and orbital migration. In this first paper, we use the Rossiter-McLaughlin Revolutions (RMR) technique to investigate the orbital architecture of 14 close-in planets ranging from mini-Neptune to Jupiter-size and covering a broad range of orbital distances. While no signal is detected for the two smallest planets, we were able to constrain the sky-projected spin--orbit angle of six planets for the first time, to revise its value for six others, and, thanks to constraints on the stellar inclination, to derive the 3D orbital architecture in seven systems. These results reveal a striking three-quarters of polar orbits in our sample, all being systems with a single close-in planet but of various stellar and planetary types. High-eccentricity migration is favored to explain such orbits for several evaporating warm Neptunes, supporting the role of late migration in shaping the desert and populating its rim. Putting our measurements in the wider context of the close-in planet population will be useful to investigate the various processes shaping their architectures.

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arXiv2020

HATS-47b, HATS-48Ab, HATS-49b and HATS-72b: Four Warm Giant Planets Transiting K Dwarfs

We report the discovery of four transiting giant planets around K dwarfs. The planets HATS-47b, HATS-48Ab, HATS-49b, and HATS-72b have masses of $0.369_{-0.021}^{+0.031}$ $M_{J}$, $0.243_{-0.030}^{+0.022}$ $M_{J}$, $0.353_{-0.027}^{+0.038}$ $M_{J}$ and $0.1254\pm0.0039$ $M_{J}$, respectively, and radii of $1.117\pm0.014$ $R_{J}$, $0.800\pm0.015$ $R_{J}$, $0.765\pm0.013$ $R_{J}$, and $0.7224\pm0.0032$ $R_{J}$, respectively. The planets orbit close to their host stars with orbital periods of $3.9228$ d, $3.1317$ d, $4.1480$ d and $7.3279$ d, respectively. The hosts are main sequence K dwarfs with masses of $0.674_{-0.012}^{+0.016}$ $M_{\odot}$, $0.7279\pm0.0066$ $M_{\odot}$, $0.7133\pm0.0075$ $M_{\odot}$, and $0.7311\pm0.0028$ $M_{\odot}$ and with $V$-band magnitudes of $V = 14.829\pm0.010$, $14.35\pm0.11$, $14.998\pm0.040$ and $12.469\pm0.010$. The Super-Neptune HATS-72b (a.k.a.\ WASP-191b and TOI 294.01) was independently identified as a transiting planet candidate by the HATSouth, WASP and TESS surveys, and we present a combined analysis of all of the data gathered by each of these projects (and their follow-up programs). An exceptionally precise mass is measured for HATS-72b thanks to high-precision radial velocity (RV) measurements obtained with VLT/ESPRESSO, FEROS, HARPS and Magellan/PFS. We also incorporate TESS observations of the warm Saturn-hosting systems HATS-47 (a.k.a. TOI 1073.01), HATS-48A and HATS-49. HATS-47 was independently identified as a candidate by the TESS team, while the other two systems were not previously identified from the TESS data. The RV orbital variations are measured for these systems using Magellan/PFS. HATS-48A has a resolved $5.\!\!^{\prime\prime}4$ neighbor in Gaia~DR2, which is a common-proper-motion binary star companion to HATS-48A with a mass of $0.22$ $M_{\odot}$ and a current projected physical separation of $\sim$1,400 au.

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arXiv2018

Machine-learning Approaches to Exoplanet Transit Detection and Candidate Validation in Wide-field Ground-based Surveys

Since the start of the Wide Angle Search for Planets (WASP) program, more than 160 transiting exoplanets have been discovered in the WASP data. In the past, possible transit-like events identified by the WASP pipeline have been vetted by human inspection to eliminate false alarms and obvious false positives. The goal of the present paper is to assess the effectiveness of machine learning as a fast, automated, and reliable means of performing the same functions on ground-based wide-field transit-survey data without human intervention. To this end, we have created training and test datasets made up of stellar light curves showing a variety of signal types including planetary transits, eclipsing binaries, variable stars, and non-periodic signals. We use a combination of machine learning methods including Random Forest Classifiers (RFCs) and Convolutional Neural Networks (CNNs) to distinguish between the different types of signals. The final algorithms correctly identify planets in the test data ~90% of the time, although each method on its own has a significant fraction of false positives. We find that in practice, a combination of different methods offers the best approach to identifying the most promising exoplanet transit candidates in data from WASP, and by extension similar transit surveys.

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