arXiv subjects

Andrew Everall

Publications and source records attributed to Andrew Everall.

12 recordsLinked to original sources

arXiv2021

The Photo-Astrometric Vertical Tracer Density of the Milky Way I: The Method

We introduce a method to infer the vertical distribution of stars in the Milky Way using a Poisson likelihood function, with a view to applying our method to the Gaia catalogue. We show how to account for the sample selection function and for parallax measurement uncertainties. Our method is validated against a simulated sample drawn from a model with two exponential discs and a power-law halo profile. A mock Gaia sample is generated using the Gaia astrometry selection function, whilst realistic parallax uncertainties are drawn from the Gaia Astrometric Spread Function. The model is fit to the mock in order to rediscover the input parameters used to generate the sample. We recover posterior distributions which accurately fit the input parameters to within statistical uncertainties, demonstrating the efficacy of our method. Using the GUMS synthetic Milky Way catalogue we find that our halo parameter fits can be heavily biased by our overly simplistic model, however, the fits to the thin and thick discs are not significantly impacted. We apply this method to Gaia Early Data Release 3 in a companion paper where we also quantify the systematic uncertainties introduced by oversimplifications in our model.

astro-ph.GA↗

arXiv2021

The Photo-Astrometric Vertical Tracer Density of the Milky Way II: Results from Gaia

We use Gaia photometry and astrometry to estimate the vertical spatial structure of the Milky Way at the Solar radius, formally accounting for sample incompleteness (the selection function) and parallax measurement uncertainty. Our results show impressive precision demonstrating the power of the Gaia data. However, systematic errors dominate the parameter value uncertainties. We thoroughly test and quantify the impacts of all systematic uncertainties. The vertical tracer density is modelled as a sum of two exponential profiles for the thin and thick discs, together with a spherically symmetric power-law for the stellar halo. We constrain the thin disc scale height as ${h_\mathrm{Tn}=260 \pm 3\, (\mathrm{stat}) \pm 26\,\mathrm{pc}\, (\mathrm{sys})}$ and thick disc ${h_\mathrm{Tk}=693 \pm 7 \,(\mathrm{stat}) \pm 121\,\mathrm{pc}\, (\mathrm{sys})}$. For the halo, we obtain a power law profile with $n_\mathrm{H}=3.543\pm0.023 \,(\mathrm{stat}) \pm0.259\, (\mathrm{sys})$. We infer a local stellar mass density for non-compact object stars of ${\rho_\mathrm{local}^* = 3.66\pm0.03\,(\mathrm{stat})\pm0.52 \times10^{-2}\,\mathrm{M}_\odot/\mathrm{pc}^3\,(\mathrm{sys})}$ and surface density of ${\Sigma_\mathrm{local}^* = 23.17\pm0.08\,(\mathrm{stat})\pm2.43\,\mathrm{M}_\odot/\mathrm{pc}^2\,(\mathrm{sys})}$. We find asymmetries above and below the disc with longer disc scale heights in the north but a flatter halo in the south at the $\lesssim 10$ per cent level.

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arXiv2021

Completeness of the Gaia-verse V: Astrometry and Radial Velocity sample selection functions in Gaia EDR3

We risk reaching false scientific conclusions if we test our physical theories against subsets of the Gaia catalogue without correcting for the biased process by which stars make it into our sample. In this paper we produce selection functions for three Gaia science samples to enable the community to correct for this bias. We estimate the probability that a source in Gaia EDR3 will have i) a reported parallax and proper motion, ii) an astrometric renormalised unit weight error below 1.4, or iii) a reported radial velocity. These selection functions are estimated as a function of $G$-band apparent magnitude and position on the sky, with the latter two also being dependent on $G-G_\mathrm{RP}$ colour. The inferred selection functions have a non-trivial dependence on these observable parameters, demonstrating the importance of empirically estimating selection functions. We also produce a simple estimate for the selection function of the full Gaia EDR3 source catalogue to be used with the subset selection functions. We make all of our selection functions easily accessible through the GitHub repository \textsc{selectionfunctions}.

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arXiv2021

A selection function toolbox for subsets of astronomical catalogues

Large catalogues are ubiquitous throughout astronomy, but most scientific analyses are carried out on smaller samples selected from these catalogues by chosen cuts on catalogued quantities. The selection function of that scientific sample - the probability that a star in the catalogue will satisfy these cuts and so make it into the sample - is thus unique to each scientific analysis. We have created a general framework that can flexibly estimate the selection function of a sample drawn from a catalogue in terms of position, magnitude and colour. Our method is unique in using the binomial likelihood and accounting for correlations in the selection function across position, magnitude and colour using Gaussian processes and spherical harmonics. We have created a new open-source Python package selectionfunctiontoolbox that implements this framework and used it to make three different estimates of the APOGEE DR16 red giant sample selection function, as a subset of 2MASS, with each estimate using an increasing amount of technical knowledge of the APOGEE targeting. In a companion paper we applied our methodology to derive estimates of the astrometric and spectroscopic selection functions of Gaia EDR3. Our framework will make it trivial for astrophysicists to estimate the selection function that they should be using with the custom sample of stars that they have chosen to answer their scientific question.

astro-ph.IM↗

arXiv2021

Selection Functions in Astronomical Data Modeling, with the Space Density of White Dwarfs as Worked Example

Statistical studies of astronomical data sets, in particular of cataloged properties for discrete objects, are central to astrophysics. One cannot model those objects' population properties or incidences without a quantitative understanding of the conditions under which these objects ended up in a catalog or sample, the sample's selection function. As systematic and didactic introductions to this topic are scarce in the astrophysical literature, we aim to provide one, addressing generically the following questions: What is a selection function? What arguments $\vec{q}$ should a selection function depend on? Over what domain must a selection function be defined? What approximations and simplifications can be made? And, how is a selection function used in `modelling'? We argue that volume-complete samples, with the volume drastically curtailed by the faintest objects, reflect a highly sub-optimal selection function that needlessly reduces the number of bright and usually rare objects in the sample. We illustrate these points by a worked example, deriving the space density of white dwarfs (WD) in the Galactic neighbourhood as a function of their luminosity and Gaia color, $\Phi_0(M_G,B-R)$ in [mag$^{-2}$pc$^{-3}$]. We construct a sample of $10^5$ presumed WDs through straightforward selection cuts on the Gaia EDR3 catalog, in magnitude, color, parallax, and astrometric fidelity $\vec{q}=(m_G,B-R,\varpi,p_{af})$. We then combine a simple model for $\Phi_0$ with the effective survey volume derived from this selection function $S_C(\vec{q})$ to derive a detailed and robust estimate of $\Phi_0(M_G,B-R)$. This resulting white dwarf luminosity-color function $\Phi_0(M_G,B-R)$ differs dramatically from the initial number density distribution in the luminosity-color plane: by orders of magnitude in density and by four magnitudes in density peak location.

astro-ph.IM↗

arXiv2021

Completeness of the Gaia-verse IV: The Astrometry Spread Function of Gaia DR2

Gaia DR2 published positions, parallaxes and proper motions for an unprecedented 1,331,909,727 sources, revolutionising the field of Galactic dynamics. We complement this data with the Astrometry Spread Function (ASF), the expected uncertainty in the measured positions, proper motions and parallax for a non-accelerating point source. The ASF is a Gaussian function for which we construct the 5D astrometric covariance matrix as a function of position on the sky and apparent magnitude using the Gaia DR2 scanning law and demonstrate excellent agreement with the observed data. This can be used to answer the question `What astrometric covariance would Gaia have published if my star was a non-accelerating point source?'. The ASF will enable characterisation of binary systems, exoplanet orbits, astrometric microlensing events and extended sources which add an excess astrometric noise to the expected astrometry uncertainty. By using the ASF to estimate the unit weight error (UWE) of Gaia DR2 sources, we demonstrate that the ASF indeed provides a direct probe of the excess source noise. We use the ASF to estimate the contribution to the selection function of the Gaia astrometric sample from a cut on astrometric_sigma5d_max showing high completeness for $G<20$ dropping to $<1\%$ in underscanned regions of the sky for $G=21$. We have added an ASF module to the Python package SCANNINGLAW (https://github.com/gaiaverse/scanninglaw) through which users can access the ASF.

astro-ph.GA↗

arXiv2020

Completeness of the Gaia-verse III: using hidden states to infer gaps, detection efficiencies and the scanning law from the DR2 light curves

The completeness of the Gaia catalogues heavily depends on the status of that space telescope through time. Stars are only published with each of the astrometric, photometric and spectroscopic data products if they are detected a minimum number of times. If there is a gap in scientific operations, a drop in the detection efficiency or Gaia deviates from the commanded scanning law, then stars will miss out on potential detections and thus be less likely to make it into the Gaia catalogues. We lay the groundwork to retrospectively ascertain the status of Gaia throughout the mission from the tens of individual measurements of the billions of stars, by developing novel methodologies to infer both the orientation and angular velocity of Gaia through time and gaps and efficiency drops in the detections. We have applied these methodologies to the Gaia DR2 variable star epoch photometry -- which are the only publicly available Gaia time-series at the present time -- and make the results publicly available. We accompany these results with a new Python package scanninglaw (https://github.com/gaiaverse/scanninglaw) that you can use to easily predict Gaia observation times and detection probabilities for arbitrary locations on the sky.

astro-ph.GA↗

arXiv2020

Predictions of Gaia's prize microlensing events are flawed

Precision astrometry from the second Gaia data release has allowed astronomers to predict 5,787 microlensing events, with 528 of these having maximums within the extended Gaia mission (J2014.5 - J2026.5). Future analysis of the Gaia time-series astrometry of these events will, in some cases, lead to precise gravitational mass measurements of the lens. We find that 61% of events predicted during the extended Gaia mission with sources brighter than G = 18 are likely to be spurious, with the background source in these cases commonly being either a duplicate detection or a binary companion of the lens. We present quality cuts to identify these spurious events and a revised list of microlensing event candidates. Our findings imply that half of the predictable astrometric microlensing events during the Gaia mission have yet to be identified.

astro-ph.SR↗

arXiv2020

Completeness of the Gaia-verse II: what are the odds that a star is missing from Gaia DR2?

The second data release of the Gaia mission contained astrometry and photometry for an incredible 1,692,919,135 sources, but how many sources did Gaia miss and where do they lie on the sky? The answer to this question will be crucial for any astronomer attempting to map the Milky Way with Gaia DR2. We infer the completeness of Gaia DR2 by exploiting the fact that it only contains sources with at least five astrometric detections. The odds that a source achieves those five detections depends on both the number of observations and the probability that an observation of that source results in a detection. We predict the number of times that each source was observed by Gaia and assume that the probability of detection is either a function of magnitude or a distribution as a function of magnitude. We fit both these models to the 1.7 billion stars of Gaia DR2, and thus are able to robustly predict the completeness of Gaia across the sky as a function of magnitude. We extend our selection function to account for crowding in dense regions of the sky, and show that this is vitally important, particularly in the Galactic bulge and the Large and Small Magellanic Clouds. We find that the magnitude limit at which Gaia is still 99% complete varies over the sky from $G=18.9$ to $21.3$. We have created a new Python package selectionfunctions (https://github.com/gaiaverse/selectionfunctions) which provides easy access to our selection functions.

astro-ph.GA↗

arXiv2020

Completeness of the Gaia-verse I: when and where were Gaia's eyes on the sky during DR2?

The Gaia space mission is crafting revolutionary astrometric, photometric and spectroscopic catalogues that will allow us to map our Galaxy, but only if we know the completeness of this Gaia-verse of catalogues: what stars does it contain and what stars is it missing? We argue that the completeness is driven by Gaia's spinning-and-precessing scanning law and will apply this principle to the Gaia-verse over this series. We take a first step by identifying the periods in time that did not contribute any measurements to Gaia DR2; these gaps create ribbons of incompleteness across the sky that will bias any study that ignores them, although some of these gaps may be filled in future data releases. Our first approach was to use the variable star photometry to identify the 94 gaps longer than 1% of a day. Our second approach was to predict the number of observations of every point on the sky, which in comparison to the reported number of detections revealed additional gaps in the astrometry and spectroscopy. Making these predictions required us to make the most precise, publicly-available determination of the Gaia scanning law. Using this scanning law, we further identified that most stars fainter than $G=22$ in DR2 have spurious magnitudes due to a miscalibration resulting from a thunderstorm over Madrid. Our list of gaps and precision scanning law will allow astronomers to know when Gaia's eye was truly on their binary star, exoplanet or microlensing event during the time period of the second data release.

astro-ph.IM↗

arXiv2020

Unresolved stellar companions with Gaia DR2 astrometry

For stars with unresolved companions, motions of the centre of light and that of mass decouple, causing a single-source astrometric model to perform poorly. We show that such stars can be easily detected with the reduced chi2 statistic, or RUWE, provided as part of Gaia DR2. We convert RUWE into the amplitude of the image centroid wobble, which, if scaled by the source distance, is proportional to the physical separation between companions (for periods up to several years). We test this idea on a sample of known spectroscopic binaries and demonstrate that the amplitude of the centroid perturbation scales with the binary period and the mass ratio as expected. We apply this technique to the Gaia DR2 data and show how the binary fraction evolves across the Hertzsprung--Russell diagram. The observed incidence of unresolved companions is high for massive young stars and drops steadily with stellar mass, reaching its lowest levels for white dwarfs. We highlight the elevated binary fraction for the nearby Blue Stragglers and Blue Horizontal Branch stars. We also illustrate how unresolved hierarchical triples inflate the relative velocity signal in wide binaries. Finally, we point out a hint of evidence for the existence of additional companions to the hosts of extrasolar hot jupiters.

astro-ph.SR↗

arXiv2019

seestar: Selection functions for spectroscopic surveys of the Milky Way

Selection functions are vital for understanding the observational biases of spectroscopic surveys. With the wide variety of multi-object spectrographs currently in operation and becoming available soon, we require easily generalisable methods for determining the selection functions of these surveys. Previous work, however, has largely been focused on generating individual, tailored selection functions for every data release of each survey. Moreover, no methods for combining these selection functions to be used for joint catalogues have been developed. We have developed a Poisson likelihood estimation method for calculating selection functions in a Bayesian framework, which can be generalised to any multi-object spectrograph. We include a robust treatment of overlapping fields within a survey as well as selection functions for combined samples with overlapping footprints. We also provide a method for transforming the selection function that depends on the sky positions, colour, and apparent magnitude of a star to one that depends on the galactic location, metallicity, mass, and age of a star. This `intrinsic' selection function is invaluable for chemodynamical models of the Milky Way. We demonstrate that our method is successful at recreating synthetic spectroscopic samples selected from a mock galaxy catalogue.

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