arXiv subjects

Coryn A. L. Bailer-Jones

Publications and source records attributed to Coryn A. L. Bailer-Jones.

At least 19 recordsLinked to original sources

arXiv2026

Stellar masses and ages in Gaia Data Release 4 from the Final Luminosity Age Mass Estimator algorithm

The masses and ages of stars are key quantities for understanding exoplanetary, stellar, and galactic evolution. In the context of Gaia, these parameters provide insights into the stellar populations, helping to trace the formation and history of the Galaxy. As part of the Gaia Data Processing and Analysis Consortium (DPAC), the Final Luminosity Age Mass Estimator (FLAME) pipeline processes Gaia data to derive stellar parameters comprising luminosities, radii, masses and ages. This paper discusses the methods and data used in FLAME for Gaia Data releases and the expected performances of FLAME for the 4th Gaia Data Release. FLAME comprises two main components: the first one, which is analytical, is used to estimate luminosity, radius, and radial velocity correction due to gravitational redshift by exploiting the atmospheric, astrometric, and photometric parameters produced within Gaia. The second is a model inference based on two main approaches: a classical minimization approach, and a Bayesian framework. It aims to derive mass, age, and evolutionary stage. The two step implementation offers flexibility in handling photometric properties that are prone to systematic errors. Tests with simulated data, the Sun, and well characterised samples of stars show that the methods in FLAME perform as expected, producing results in statistical agreement with the literature. We provide new stellar fundamental parameters for some high velocity stars, stars with very low mass companions, and a selection of stars in the Plato Field of View. In Gaia Data Release 4 approximately 500 million sources will have results from the pipeline. [abridged]

astro-ph.SR↗

arXiv2026

Performance analysis of extragalactic classifications in Gaia Data Release 4

The Discrete Source Classifier (DSC) provides probabilistic classifications of sources in Gaia Data Release 4 (GDR4) based on empirically-trained Bayesian classifiers. Using Gaia astrometry, photometry, and low-resolution spectra (XP), DSC classifies all sources as quasars, galaxies, or stars. DSC comprises three trained neural networks and three combinations of their probabilities. When evaluated as a function of brightness and sky position on a test set excluding the Magellanic Clouds, the DSC purity in GDR4 has improved for a small loss in completeness. The average performance of the best classifiers at magnitudes brighter than G=20 is at least 88% completeness and 96% purity for the extragalactic classes, namely the quasar and galaxy classes. At fainter magnitudes, performance is lower due to increased noise. The average performance at magnitudes of 20$\leq$G<20.5 is a minimum of 55% completeness and 71% purity for the extragalactic classes. At G>20.5 mag, completeness is considerably reduced, primarily for the models that depend on the XP spectra. Furthermore, we train additional models on Gaia optical data together with mid-infrared photometry from the CatWISE2020 catalogue. Inclusion of infrared photometry increases the completeness of extragalactic samples at G>20 mag between 9 and 29 percentage points, at the cost of reducing purity between 1 and 9 percentage points. In GDR4, the best DSC-combined classifier prioritising completeness identifies three million quasars and two million galaxies, but with expected high contamination among fainter sources. In contrast, the combined classifiers prioritising purity identify approximately two million quasars and 1.3 million galaxies with an expected lower level of contamination. Finally, we provide recommendations for enhancing the purity of the DSC extragalactic selection by applying quality cuts to the Gaia photometry and astrometry.

astro-ph.GA↗

arXiv2024

Improved source classification and performance analysis using Gaia DR3

The Discrete Source Classifier (DSC) provides probabilistic classification of sources in Gaia Data Release 3 using a Bayesian framework and a global prior. The DSC Combmod classifier in GDR3 achieved for the extragalactic classes (quasars and galaxies) a high completeness of 92%, but a low purity of 22% due to contamination from the far larger star class. However, these single metrics mask significant variation in performance with magnitude and sky position. Furthermore, a better combination of the individual classifiers is possible. Here we compute two-dimensional representations of the completeness and the purity as function of Galactic latitude and source brightness, and also exclude the Magellanic Clouds where stellar contamination significantly reduces the purity. Reevaluated on a cleaner validation set and without introducing changes to the published GDR3 DSC probabilities themselves, we achieve for Combmod average 2D completenesses of 92% and 95% and average 2D purities of 55% and 89% for the quasar and galaxy classes, respectively. Since the relative proportions of extragalactic objects to stars in Gaia is expected to vary significantly with brightness and latitude, we introduce a new prior as a continuous function of brightness and latitude, and compute new class probabilities. This variable prior only improves the performance by a few percentage points, mostly at the faint end. Significant improvement, however, is obtained by a new additive combination of Specmod and Allosmod. This classifier, Combmod-$α$, achieves average 2D completenesses of 82% and 93% and average 2D purities of 79% and 93% for the quasar and galaxy classes, respectively, when using the global prior. Thus, we achieve a significant improvement in purity for a small loss of completeness. The improvement is most significant for faint quasars where the purity rises from 20% to 62%.

astro-ph.GA↗

arXiv2023

Distinguishing exoplanet companions from field stars in direct imaging using Gaia astrometry

Direct imaging searches for exoplanets around stars detect many spurious candidates that are in fact background field stars. To help distinguish these from genuine companions, multi-epoch astrometry can be used to identify a common proper motion with the host star. Although this is frequently done, many approaches lack an appropriate model for the motions of the background population, or do not use a statistical framework to properly quantify the results. Here we use Gaia astrometry combined with 2MASS photometry to model the parallax and proper motion distributions of field stars around exoplanet host stars as a function of candidate magnitude. We develop a likelihood-based method that compares the positions of a candidate at multiple epochs with the positions expected under both this field star model and a co-moving companion model. Our method propagates the covariances in the Gaia astrometry and the candidate positions. True companions are assumed to have long periods compared to the observational baseline, so we currently neglect orbital motion. We apply our method to a sample of 23 host stars with 263 candidates identified in the B-Star Exoplanet Abundance Study (BEAST) survey on VLT/SPHERE. We identify seven candidates in which the odds ratio favours the co-moving companion model by a factor of 100 or more. Most of these detections are based on only two or three epochs separated by less than three years, so further epochs should be obtained to reassess the companion probabilities. Our method is publicly available as an open-source python package from https://github.com/herzphi/compass to use with any data.

astro-ph.EP↗

arXiv2022

Quasar and galaxy classification using Gaia EDR3 and CatWise2020

In this work, we assess the combined use of Gaia photometry and astrometry with infrared data from CatWISE in improving the identification of extragalactic sources compared to the classification obtained using Gaia data. We evaluate different input feature configurations and prior functions, with the aim of presenting a classification methodology integrating prior knowledge stemming from realistic class distributions in the universe. In our work, we compare different classifiers, namely Gaussian Mixture Models (GMMs), XGBoost and CatBoost, and classify sources into three classes - star, quasar, and galaxy, with the target quasar and galaxy class labels obtained from SDSS16 and the star label from Gaia EDR3. In our approach, we adjust the posterior probabilities to reflect the intrinsic distribution of extragalactic sources in the universe via a prior function. We introduce two priors, a global prior reflecting the overall rarity of quasars and galaxies, and a mixed prior that incorporates in addition the distribution of the these sources as a function of Galactic latitude and magnitude. Our best classification performances, in terms of completeness and purity of the galaxy and quasar classes, are achieved using the mixed prior for sources at high latitudes and in the magnitude range G = 18.5 to 19.5. We apply our identified best-performing classifier to three application datasets from Gaia DR3, and find that the global prior is more conservative in what it considers to be a quasar or a galaxy compared to the mixed prior. In particular, when applied to the pure quasar and galaxy candidates samples, we attain a purity of 97% for quasars and 99.9% for galaxies using the global prior, and purities of 96% and 99% respectively using the mixed prior. We conclude our work by discussing the importance of applying adjusted priors portraying realistic class distributions in the universe.

astro-ph.GA↗

arXiv2022

Improving white dwarfs as chronometers with Gaia parallaxes and spectroscopic metallicities

White dwarfs (WDs) offer unrealized potential in solving two problems in astrophysics: stellar age accuracy and precision. WD cooling ages can be inferred from surface temperatures and radii, which can be constrained with precision by high quality photometry and parallaxes. Accurate and precise Gaia parallaxes along with photometric surveys provide information to derive cooling and total ages for vast numbers of WDs. Here we analyse 1372 WDs found in wide binaries with MS companions, and report on the cooling and total age precision attainable in these WD+MS systems. The total age of a WD can be further constrained if its original metallicity is known, because the main-sequence (MS) progenitor lifetime depends on metallicity at fixed mass, yet metallicity is unavailable via spectroscopy of the WD. We show that incorporating spectroscopic metallicity constraints from 38 wide binary MS companions substantially decreases internal uncertainties in WD total ages compared to a uniform constraint. Averaged over the 38 stars in our sample, the total (internal) age uncertainty improves from 21.04% to 16.77% when incorporating the spectroscopic constraint. Higher mass WDs yield better total age precision; for 8 WDs with zero age main sequence masses $\geq$ 2.0 solar masses, the mean uncertainty in total ages improves from 8.61% to 4.54% when incorporating spectroscopic metallicities. We find that it is often possible to achieve 5% total age precision for WDs with progenitor masses above 2.0 solar masses if parallaxes with $\leq$ 1% precision and Pan-STARRS $g, r$, and $i$ photometry with $\leq$ 0.01 mag precision are available.

astro-ph.SR↗

arXiv2021

Lost in space? Relativistic interstellar navigation using an astrometric star catalogue

The exploration of interstellar space will require autonomous navigation systems that do not rely on tracking from the Earth. Here I develop a method to determine the 3D position and 3D velocity of a spacecraft in deep space using a star catalogue. As a spacecraft moves away from the Sun, the observed positions and velocities of the stars will change relative to those in a Earth-based catalogue due to parallax and aberration. By measuring just the angular distances between pairs of stars, and comparing these to the catalogue, we can infer the coordinates of the spacecraft via an iterative forward-modelling process. I perform simulations with existing star catalogues to demonstrate the method and to compute its performance. Using the 20 nearest stars and a modest angular distance measurement accuracy of 1", the position and velocity of a spacecraft light years from the Sun moving at relativistic speeds can be determined to within 3 au and 2 km/s respectively. These accuracies improve linearly with the measurement accuracy, e.g. with angles measured to 1 mas the navigation accuracy is 1000 times better. Performance can also be improved using more stars, or by including onboard measurements of the stars' radial velocities, as these too are affected by the spacecraft's position and motion.

astro-ph.IM↗

arXiv2021

Deep Transfer Learning for Classification of Variable Sources

Ongoing or upcoming surveys such as Gaia, ZTF, or LSST will observe light-curves of billons or more astronomical sources. This presents new challenges for identifying interesting and important types of variability. Collecting a sufficient number of labelled data for training is difficult, however, especially in the early stages of a new survey. Here we develop a single-band light-curve classifier based on deep neural networks, and use transfer learning to address the training data paucity problem by conveying knowledge from one dataset to another. First we train a neural network on 16 variability features extracted from the light-curves of OGLE and EROS-2 variables. We then optimize this model using a small set (e.g. 5%) of periodic variable light-curves from the ASAS dataset in order to transfer knowledge inferred from OGLE/EROS-2 to a new ASAS classifier. With this we achieve good classification results on ASAS, thereby showing that knowledge can be successfully transferred between datasets. We demonstrate similar transfer learning using Hipparcos and ASAS-SN data. We therefore find that it is not necessary to train a neural network from scratch for every new survey, but rather that transfer learning can be used even when only a small set of labelled data is available in the new survey.

astro-ph.IM↗

arXiv2020

Detailed 3D structure of OrionA in dust with Gaia DR2

The unprecedented astrometry from Gaia DR2 provides us with an opportunity to study in detail molecular clouds in the solar neighbourhood. Extracting the wealth of information in these data remains a challenge, however. We have further improved our Gaussian Processes-based, three-dimensional dust mapping technique to allow us to study molecular clouds in more detail. These improvements include a significantly better scaling of the computational cost with the number of stars, and taking into account distance uncertainties to individual stars. Using Gaia DR2 astrometry together with 2MASS and WISE photometry for 30 000 stars, we infer the distribution of dust out to 600 pc in the direction of the Orion A molecular cloud. We identify a bubble-like structure in front of Orion A, centred at a distance of about 350 pc from the Sun. The main Orion A structure is visible at slightly larger distances, and we clearly see a tail extending over 100 pc that is curved and slightly inclined to the line-of-sight. The location of our foreground structure coincides with 5-10 Myr old stellar populations, suggesting a star formation episode that predates that of the Orion Nebula Cluster itself. We identify also the main structure of the Orion B molecular cloud, and in addition discover a background component to this at a distance of about 460 pc from the Sun. Finally, we associate our dust components at different distances with the plane-of-the-sky magnetic field orientation as mapped by Planck. This provides valuable information for modelling the magnetic field in 3D around star forming regions.

astro-ph.GA↗

arXiv2020

The Sun Diver: Combining solar sails with the Oberth effect

A highly reflective sail provides a way to propel a spacecraft out of the solar system using solar radiation pressure. The closer the spacecraft is to the Sun when it starts its outward journey, the larger the radiation pressure and so the larger the final velocity. For a spacecraft starting on the Earth's orbit, closer proximity can be achieved via a retrograde impulse from a rocket engine. The sail is then deployed at the closest approach to the Sun. Employing the so-called Oberth effect, a second, prograde, impulse at closest approach will raise the final velocity further. Here I investigate how a fixed total impulse (Δv) can best be distributed in this procedure to maximize the sail's velocity at infinity. Once Δv exceeds a threshold that depends on the lightness number of the sail (a measure of its sun-induced acceleration), the best strategy is to use all of the Δv in the retrograde impulse to dive as close as possible to the Sun. Below the threshold the best strategy is to use all of the Δv in the prograde impulse and thus not to dive at all. Although larger velocities can be achieved with multi-stage impulsive transfers, this study shows some interesting and perhaps counter-intuitive consequences of combining impulses with solar sails.

physics.pop-ph↗

arXiv2020

A search for the origin of the interstellar comet 2I/Borisov

The discovery of the second interstellar object 2I/Borisov on 2019 August 30 raises the question of whether it was ejected recently from a nearby stellar system. Here we compute the asymptotic incoming trajectory of 2I/Borisov, based on both recent and pre-discovery data extending back to December 2018, using a range of force models that account for cometary outgassing. From Gaia DR2 astrometry and radial velocities, we trace back in time the Galactic orbits of 7.4 million stars to look for close encounters with 2I/Borisov. The closest encounter we find took place 910kyr ago with the M0V star Ross 573, at a separation of 0.068pc (90% confidence interval of 0.053-0.09pc) with a relative velocity of 23km/s. This encounter is nine times closer than the closest past encounter identified for the first interstellar object 1I/'Oumuamua. Ejection of 2I/Borisov via a three-body encounter in a binary or planetary system is possible, although such a large ejection velocity is unlikely to be obtained and Ross 573 shows no signs of binarity. We also identify and discuss some other recent close encounters, recognizing that if 2I/Borisov is more than about 10Myr old, our search would be unlikely to find its parent system.

astro-ph.EP↗

arXiv2019

Future stellar flybys of the Voyager and Pioneer spacecraft

The Pioneer 10 and 11 and Voyager 1 and 2 spacecraft, launched in the 1970s, are heading out of the solar system. Using the astrometric and radial velocity data from the second Gaia data release, we integrate the trajectories of 7.4 million stars, and the spacecraft, through a Galactic potential in order to identify those stars the spacecraft will pass closest to. The closest encounters for all spacecraft take place at separations between 0.2 and 0.5 pc within the next million years. The closest encounter will be by Pioneer 10 with the K8 dwarf HIP 117795, at 0.23 pc in 90 kyr at a high relative velocity of 291 km/s.

astro-ph.EP↗

arXiv2019

Quasar and galaxy classification in Gaia Data Release 2

We construct a supervised classifier based on Gaussian Mixture Models to probabilistically classify objects in Gaia data release 2 (GDR2) using only photometric and astrometric data in that release. The model is trained empirically to classify objects into three classes -- star, quasar, galaxy -- for G<=14.5 mag down to the Gaia magnitude limit of G=21.0 mag. Galaxies and quasars are identified for the training set by a cross-match to objects with spectroscopic classifications from the Sloan Digital Sky Survey. Stars are defined directly from GDR2. When allowing for the expectation that quasars are 500 times rarer than stars, and galaxies 7500 times rarer than stars (the class imbalance problem), samples classified with a threshold probability of 0.5 are predicted to have purities of 0.43 for quasars and 0.28 for galaxies, and completenesses of 0.58 and 0.72 respectively. The purities can be increased up to 0.60 by adopting a higher threshold. Not accounting for this expected low frequency of extragalactic objects (the class prior) would give both erroneously optimistic performance predictions and severely impure samples. Applying our model to all 1.20 billion objects in GDR2 with the required features, we classify 2.3 million objects as quasars and 0.37 million objects as galaxies (with individual probabilities above 0.5). The small number of galaxies is due to the strong bias of the satellite detection algorithm and on-ground data selection against extended objects. We infer the true number of quasars and galaxies -- as these classes are defined by our training set -- to be 690,000 and 110,000 respectively (+/- 50%). The aim of this work is to see how well extragalactic objects can be classified using only GDR2 data. Better classifications should be possible with the low resolution spectroscopy (BP/RP) planned for GDR3.

astro-ph.GA↗

arXiv2018

Detection of the Milky Way spiral arms in dust from 3D mapping

Large stellar surveys are sensitive to interstellar dust through the effects of reddening. Using extinctions measured from photometry and spectroscopy, together with three-dimensional (3D) positions of individual stars, it is possible to construct a three-dimensional dust map. We present the first continuous map of the dust distribution in the Galactic disk out to 7 kpc within 100 pc of the Galactic midplane, using red clump and giant stars from SDSS APOGEE DR14. We use a non-parametric method based on Gaussian Processes to map the dust density, which is the local property of the ISM rather than an integrated quantity. This method models the dust correlation between points in 3D space and can capture arbitrary variations, unconstrained by a pre-specified functional form. This produces a continuous map without line-of-sight artefacts. Our resulting map traces some features of the local Galactic spiral arms, even though the model contains no prior suggestion of spiral arms, nor any underlying model for the Galactic structure. This is the first time that such evident arm structures have been captured by a dust density map in the Milky Way. Our resulting map also traces some of the known giant molecular clouds in the Galaxy and puts some constraints on their distances, some of which were hitherto relatively uncertain.

astro-ph.GA↗

arXiv2018

Gaia Data Release 2: first stellar parameters from Apsis

The second Gaia data release (Gaia-DR2) contains, beyond the astrometry, three-band photometry for 1.38 billion sources. We have used these three broad bands to infer stellar effective temperatures, Teff, for all sources brighter than G=17 mag with Teff in the range 3000-10 000 K (161 million sources). Using in addition the parallaxes, we infer the line-of-sight extinction, A_G, and the reddening, E[BP-RP], for 88 million sources. Together with a bolometric correction we derive luminosity and radius for 77 million sources. These quantities as well as their estimated uncertainties are part of Gaia-DR2. Here we describe the procedures by which these quantities were obtained, including the underlying assumptions, comparison with literature estimates, and the limitations of our results. Typical accuracies are of order 324 K (Teff), 0.46 mag (A_G), 0.23 mag (E[BP-RP]), 15% (luminosity), and 10% (radius). Being based on only a small number of observable quantities and limited training data, our results are necessarily subject to some extreme assumptions that can lead to strong systematics in some cases (not included in the aforementioned accuracy estimates). One aspect is the non-negativity contraint of our estimates, in particular extinction. Yet in several regions of parameter space our results show very good performance, for example for red clump stars and solar analogues. Large uncertainties render the extinctions less useful at the individual star level, but they show good performance for ensemble estimates. We identify regimes in which our parameters should and should not be used and we define a "clean" sample. Despite the limitations, this is the largest catalogue of uniformly-inferred stellar parameters to date. More precise and detailed astrophysical parameters based on the full BP/RP spectrophotometry are planned as part of the third Gaia data release.

astro-ph.SR↗

arXiv2017

Balmer-dominated shocks in Tycho's SNR: omnipresence of CRs

We present wide-field, spatially and highly resolved spectroscopic observations of Balmer filaments in the northeastern rim of Tycho's supernova remnant in order to investigate the signal of cosmic-ray (CR) acceleration. The spectra of Balmer-dominated shocks (BDSs) have characteristic narrow (FWHM $\sim$ 10 kms$^{-1}$) and broad (FWHM $\sim$ 1000 kms$^{-1}$) H$α$ components. CRs affect the H$α$-line parameters: heating the cold neutrals in the interstellar medium results in broadening of the narrow H$α$-line width beyond 20 kms$^{-1}$, but also in reduction of the broad H$α$-line width due to energy being removed from the protons in the post-shock region. For the first time we show that the width of the narrow H$α$ line, much larger than 20 kms$^{-1}$, is not a resolution or geometric effect nor a spurious result of a neglected intermediate (FWHM $\sim$ 100 kms$^{-1}$) component resulting from hydrogen atoms undergoing charge exchange with warm protons in the broad-neutral precursor. Moreover, we show that a narrow line width $\gg$ 20 kms$^{-1}$ extends across the entire NE rim, implying CR acceleration is ubiquitous, and making it possible to relate its strength to locally varying shock conditions. Finally, we find several locations along the rim, where spectra are significantly better explained (based on Bayesian evidence) by inclusion of the intermediate component, with a width of 180 kms$^{-1}$ on average.

astro-ph.HE↗

arXiv2017

Balmer filaments in Tycho's supernova remnant: an interplay between cosmic-ray and broad-neutral precursors

We present H$α$ spectroscopic observations and detailed modelling of the Balmer filaments in the supernova remnant Tycho. We used Galaxy H$α$ Fabry-Pérot Spectrometer on the William Herschel Telescope with a 3.4'$\times$3.4' field-of-view, 0.2" pixel scale and $σ_\rm{instr}=8.1$ km/s resolution at 1" seeing for $\sim10$ hours, resulting in 82 spatial-spectral bins that resolve the narrow H$α$ line in the entire Tycho's northeastern rim. For the first time, we can mitigate artificial line broadening from unresolved differential motion, and probe H$α$ emission parameters in varying shock and ambient medium conditions. Broad H$α$ line remains unresolved within spectral coverage of 392 km/s. We employed Bayesian inference to obtain reliable parameter confidence intervals, and quantify the evidence for models with multiple line components. The median H$α$ narrow-line full-width at half-maximum of all bins and models is $W_\rm{NL}=(54.8\pm1.8)$ km/s at the $95\%$ confidence level, varying within [35, 72] km/s between bins and clearly broadened compared to the intrinsic (thermal) $\approx20$ km/s. Possible line splits are accounted for, significant in $\approx18\%$ of the filament, and presumably due to remaining projection effects. We also find wide-spread evidence for intermediate-line emission of a broad-neutral precursor, with median $W_\rm{IL}=(180\pm14)$ km/s ($95\%$ confidence). Finally, we present a measurement of the remnant's systemic velocity, $V_\rm{LSR}=-34$ km/s, and map differential line-of-sight motions. Our results confirm the existence and interplay of shock precursors in Tycho's remnant. In particular, we show that suprathermal narrow-line emission is near-universal in Tycho and that, in absence of an alternative explanation, collisionless supernova remnant shocks constitute a viable acceleration source for Galactic TeV Cosmic-Ray protons.

astro-ph.HE↗

arXiv2017

A Probabilistic Approach to Fitting Period-Luminosity Relations and Validating Gaia Parallaxes

Pulsating stars, such as Cepheids, Miras, and RR Lyrae stars, are important distance indicators and calibrators of the "cosmic distance ladder", and yet their period-luminosity-metallicity (PLZ) relations are still constrained using simple statistical methods that cannot take full advantage of available data. To enable optimal usage of data provided by the Gaia mission, we present a probabilistic approach that simultaneously constrains parameters of PLZ relations and uncertainties in Gaia parallax measurements. We demonstrate this approach by constraining PLZ relations of type $ab$ RR Lyrae stars in near-infrared W1 and W2 bands, using Tycho-Gaia Astrometric Solution (TGAS) parallax measurements for a sample of $\approx100$ type $ab$ RR Lyrae stars located within 2.5 kpc of the Sun. The fitted PLZ relations are consistent with previous studies, and in combination with other data, deliver distances precise to 6% (once various sources of uncertainty are taken into account). To a precision of 0.05 mas ($1σ$), we do not find a statistically significant offset in TGAS parallaxes for this sample of distant RR Lyrae stars (median parallax of 0.8 mas and distance of 1.4 kpc). With only minor modifications, our probabilistic approach can be used to constrain PLZ relations of other pulsating stars, and we intend to apply it to Cepheid and Mira stars in the near future.

astro-ph.GA↗