Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMicrolens mass determination for Gaias predicted photometric events
158
0
0.0
(
0
)
Authors
Peter McGill
Ask ChatGPT about the research
No Arabic abstract
We used Gaia Data Release 2 to search for upcoming photometric microlensing events, identifying two candidates with high amplification. In the case of candidate 1, a spectrum of the lens (l1) confirms it is a usdM3 subdwarf with mass $approx 0.11 M_odot$, while the event reaches maximum amplification of $20^{+20}_{-10}$ mmag on November 3rd 2019 ($pm$1d). For candidate 2, the lens (l2) is a metal-poor M dwarf with mass $approx 0.38 M_odot$ derived from spectral energy distribution (SED) fitting, and maximum amplification of $10^{+40}_{-10}$ mmag occurs on June 3rd 2019 ($pm$4d). This permits a new algorithm for mass inference on the microlens. Given the predicted time, the photometric lightcurve of these events can be densely sampled by ground-based telescopes. The lightcurve is a function of the unknown lens mass, together with 8 other parameters for all of which Gaia provides measurements and uncertainties. Leveraging this prior information on the source and lens provided by Gaias astrometric solution, and assuming that a ground-based campaign can provide 50 measurements at mmag precision, we show for example that the mass of l1 can be recovered to within 20 per cent (68 per cent confidence limit).
rate research
Read More
Microlensing events can be used to directly measure the masses of single field stars to a precision of $sim$1-10%. The majority of direct mass measurements for stellar and sub-stellar objects typically only come from observations of binary systems. Hence microlensing provides an important channel for direct mass measurements of single stars. The Gaia satellite has observed $sim$1.7 billion objects, and analysis of the second data release has recently yielded numerous event predictions for the next few decades. However, the Gaia catalog is incomplete for nearby very-low-mass objects such as brown dwarfs for which mass measurements are most crucial. We employ a catalog of very-low-mass objects from Pan-STARRS data release 1 (PDR1) as potential lens stars, and we use the objects from Gaia data release 2 (GDR2) as potential source stars. We then search for future microlensing events up to the year 2070. The Pan-STARRS1 objects are first cross-matched with GDR2 to remove any that are present in both catalogs. This leaves a sample of 1,718 possible lenses. We fit MIST isochrones to the Pan-STARRS1, AllWISE and 2MASS photometry to estimate their masses. We then compute their paths on the sky, along with the paths of the GDR2 source objects, until the year 2070, and search for potential microlensing events. Source-lens pairs that will produce a microlensing signal with an astrometric amplitude of greater than 0.131 mas, or a photometric amplitude of greater than 0.4 mmag, are retained.
Stellar variability studies are now reaching a completely new level thanks to ESAs Gaia mission, which enables us to locate many variable stars in the Hertzsprung-Russell diagram and determine the various instability strips/bands. Furthermore, this mission also allows us to detect, characterise and classify many millions of new variable stars thanks to its very unique nearly simultaneous multi-epoch survey with different instruments (photometer, spectro-photometer, radial velocity spectrometer). An overview of what can be found in literature in terms of mostly data products by the Gaia consortium is given. This concerns the various catalogues of variable stars derived from the Gaia time series and also the location and motion of variable stars in the observational Hertzsprung-Russell diagram. In addition, we provide a list of a few thousands of variable white dwarf candidates derived from the DR2 published data, among them probably many hundreds of new pulsating white dwarfs. On a very different topic, we also show how Gaia allows us to reveal the 3D structures of and around the Milky Way thanks to the RR Lyrae stars.
We present an adaptive optics (AO) analysis of images from the Keck-II telescope NIRC2 instrument of the planetary microlensing event MOA-2009-BLG-319. The $sim$10 year baseline between the event and the Keck observations allows the planetary host star to be detected at a separation of $66.5pm 1.7,$mas from the source star, consistent with the light curve model prediction. The combination of the host star brightness and light curve parameters yield host star and planet masses of M_host = 0.514 $pm$ 0.063M_Sun and m_p = 66.0 $pm$ 8.1M_Earth at a distance of $D_L = 7.0 pm 0.7,$kpc. The star-planet projected separation is $2.03 pm 0.21,$AU. The planet-star mass ratio of this system, $q = (3.857 pm 0.029)times 10^{-4}$, places it in the predicted planet desert at $10^{-4} < q < 4times 10^{-4}$ according to the runaway gas accretion scenario of the core accretion theory. Seven of the 30 planets in the Suzuki et al. (2016) sample fall in this mass ratio range, and this is the third with a measured host mass. All three of these host stars have masses of 0.5 $leq$ M_host/M_Sun $leq$ 0.7, which implies that this predicted mass ratio gap is filled with planets that have host stars within a factor of two of 1M_Sun. This suggests that runaway gas accretion does not play a major role in determining giant planet masses for stars somewhat less massive than the Sun. Our analysis has been accomplished with a modified DAOPHOT code that has been designed to measure the brightness and positions of closely blended stars. This will aid in the development of the primary method that the Nancy Grace Roman Space Telescope mission will use to determine the masses of microlens planets and their hosts.
In the coming years, next-generation space-based infrared observatories will significantly increase our samples of rare massive stars, representing a tremendous opportunity to leverage modern statistical tools and methods to test massive stellar evolution in entirely new environments. Such work is only possible if the observed objects can be reliably classified. Spectroscopic observations are infeasible with more distant targets, and so we wish to determine whether machine learning methods can classify massive stars using broadband infrared photometry. We find that a Support Vector Machine classifier is capable of coarsely classifying massive stars with labels corresponding to hot, cool, and emission line stars with high accuracy, while rejecting contaminating low mass giants. Remarkably, 76% of emission line stars can be recovered without the need for narrowband or spectroscopic observations. We classify a sample of ${sim}2500$ objects with no existing labels, and identify fourteen candidate emission line objects. Unfortunately, despite the high precision of the photometry in our sample, the heterogeneous origins of the labels for the stars in our sample severely inhibits our classifier from distinguishing classes of stars with more granularity. Ultimately, no large and homogeneously labeled sample of massive stars currently exists. Without significant efforts to robustly classify evolved massive stars -- which is feasible given existing data from large all-sky spectroscopic surveys -- shortcomings in the labeling of existing data sets will hinder efforts to leverage the next-generation of space observatories.
New spectroscopic surveys offer the promise of consistent stellar parameters and abundances (stellar labels) for hundreds of thousands of stars in the Milky Way: this poses a formidable spectral modeling challenge. In many cases, there is a sub-set of reference objects for which the stellar labels are known with high(er) fidelity. We take advantage of this with The Cannon, a new data-driven approach for determining stellar labels from spectroscopic data. The Cannon learns from the known labels of reference stars how the continuum-normalized spectra depend on these labels by fitting a flexible model at each wavelength; then, The Cannon uses this model to derive labels for the remaining survey stars. We illustrate The Cannon by training the model on only 542 stars in 19 clusters as reference objects, with Teff, log g and [Fe/H] as the labels, and then applying it to the spectra of 56,000 stars from APOGEE DR10. The Cannon is very accurate. Its stellar labels compare well to the stars for which APOGEE pipeline (ASPCAP) labels are provided in DR10, with rms differences that are basically identical to the stated ASPCAP uncertainties. Beyond the reference labels, The Cannon makes no use of stellar models nor any line-list, but needs a set of reference objects that span label-space. The Cannon performs well at lower signal-to-noise, as it delivers comparably good labels even at one ninth the APOGEE observing time. We discuss the limitations of The Cannon and its future potential, particularly, to bring different spectroscopic surveys onto a consistent scale of stellar labels.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
