The LSST Data Mining Research Agenda

We describe features of the LSST science database that are amenable to scientific data mining, object classification, outlier identification, anomaly detection, image quality assurance, and survey science validation. The data mining research agenda includes: scalability (at petabytes scales) of existing machine learning and data mining algorithms; development of grid-enabled parallel data mining algorithms; designing a robust system for brokering classifications from the LSST event pipeline (which may produce 10,000 or more event alerts per night); multi-resolution methods for exploration of petascale databases; indexing of multi-attribute multi-dimensional astronomical databases (beyond spatial indexing) for rapid querying of petabyte databases; and more.Comment: 5 pages, Presented at the "Classification and Discovery in Large Astronomical Surveys" meeting, Ringberg Castle, 14-17 October, 200

New stellar encounters discovered in the second Gaia data release

Passing stars may play an important role in the evolution of our solar system. We search for close stellar encounters to the Sun among all 7.2 million stars in Gaia-DR2 that have six-dimensional phase space data. We characterize encounters by integrating their orbits through a Galactic potential and propagating the correlated uncertainties via a Monte Carlo resampling. After filtering to remove spurious data, we find 694 stars that have median (over uncertainties) closest encounter distances within 5 pc, all occurring within 15 Myr from now. 26 of these have at least a 50% chance of coming closer than 1 pc (and 7 within 0.5 pc), all but one of which are newly discovered here. We further confirm some and refute several other previously-identified encounters, confirming suspicions about their data. The closest encounter in the sample is Gl 710, which has a 95% probability of coming closer than 0.08 pc (17 000 AU). Taking mass estimates from Gaia astrometry and multiband photometry for essentially all encounters, we find that Gl 710 also has the largest impulse on the Oort cloud. Using a Galaxy model, we compute the completeness of the Gaia-DR2 encountering sample as a function of perihelion time and distance. Only 15% of encounters within 5 pc occurring within +/- 5 Myr of now have been identified, mostly due to the lack of radial velocities for faint and/or cool stars. Accounting for the incompleteness, we infer the present rate of encounters within 1 pc to be 19.7 +/- 2.2 per Myr, a quantity expected to scale quadratically with the encounter distance out to at least several pc. Spuriously large parallaxes in our sample from imperfect filtering would tend to inflate both the number of encounters found and this inferred rate. The magnitude of this effect is hard to quantify.Comment: 12 pages. Accepted to A&A. Added to this version: section 3.2 and Fig. 8 (CMD) with discussion of astrometric quality metrics; full versions of tables 2 and 3 as ancillary dat

Inferring the three-dimensional distribution of dust in the Galaxy with a non-parametric method: Preparing for Gaia

We present a non-parametric model for inferring the three-dimensional (3D) distribution of dust density in the Milky Way. Our approach uses the extinction measured towards stars at different locations in the Galaxy at approximately known distances. Each extinction measurement is proportional to the integrated dust density along its line-of-sight. Making simple assumptions about the spatial correlation of the dust density, we can infer the most probable 3D distribution of dust across the entire observed region, including along sight lines which were not observed. This is possible because our model employs a Gaussian Process to connect all lines-of-sight. We demonstrate the capability of our model to capture detailed dust density variations using mock data as well as simulated data from the Gaia Universe Model Snapshot. We then apply our method to a sample of giant stars observed by APOGEE and Kepler to construct a 3D dust map over a small region of the Galaxy. Due to our smoothness constraint and its isotropy, we provide one of the first maps which does not show the "fingers of god" effect.Comment: Minor changes applied. Final version accepted for publication in A&A. 15 pages, 17 figure

New approaches to object classification in synoptic sky surveys

Digital synoptic sky surveys pose several new object classification challenges. In surveys where real-time detection and classification of transient events is a science driver, there is a need for an effective elimination of instrument-related artifacts which can masquerade as transient sources in the detection pipeline, e.g., unremoved large cosmic rays, saturation trails, reflections, crosstalk artifacts, etc. We have implemented such an Artifact Filter, using a supervised neural network, for the real-time processing pipeline in the Palomar-Quest (PQ) survey. After the training phase, for each object it takes as input a set of measured morphological parameters and returns the probability of it being a real object. Despite the relatively low number of training cases for many kinds of artifacts, the overall artifact classification rate is around 90%, with no genuine transients misclassified during our real-time scans. Another question is how to assign an optimal star-galaxy classification in a multi-pass survey, where seeing and other conditions change between different epochs, potentially producing inconsistent classifications for the same object. We have implemented a star/galaxy multipass classifier that makes use of external and a priori knowledge to find the optimal classification from the individually derived ones. Both these techniques can be applied to other, similar surveys and data sets

Photometric identification of blue horizontal branch stars

We investigate the performance of some common machine learning techniques in identifying BHB stars from photometric data. To train the machine learning algorithms, we use previously published spectroscopic identifications of BHB stars from SDSS data. We investigate the performance of three different techniques, namely k nearest neighbour classification, kernel density estimation and a support vector machine (SVM). We discuss the performance of the methods in terms of both completeness and contamination. We discuss the prospect of trading off these values, achieving lower contamination at the expense of lower completeness, by adjusting probability thresholds for the classification. We also discuss the role of prior probabilities in the classification performance, and we assess via simulations the reliability of the dataset used for training. Overall it seems that no-prior gives the best completeness, but adopting a prior lowers the contamination. We find that the SVM generally delivers the lowest contamination for a given level of completeness, and so is our method of choice. Finally, we classify a large sample of SDSS DR7 photometry using the SVM trained on the spectroscopic sample. We identify 27,074 probable BHB stars out of a sample of 294,652 stars. We derive photometric parallaxes and demonstrate that our results are reasonable by comparing to known distances for a selection of globular clusters. We attach our classifications, including probabilities, as an electronic table, so that they can be used either directly as a BHB star catalogue, or as priors to a spectroscopic or other classification method. We also provide our final models so that they can be directly applied to new data.Comment: To appear in A&A. 19 pages, 22 figures. Tables 7, A3 and A4 available electronically onlin

Polarisation of very-low-mass stars and brown dwarfs

Ultra-cool dwarfs of the L spectral type (Teff=1400-2200K) are known to have dusty atmospheres. Asymmetries of the dwarf surface may arise from rotationally-induced flattening and dust-cloud coverage, and may result in non-zero linear polarisation through dust scattering. We aim to study the heterogeneity of ultra-cool dwarfs' atmospheres and the grain-size effects on the polarisation degree in a sample of nine late M, L and early T dwarfs. We obtain linear polarimetric imaging measurements using FORS1 at the Very Large Telescope, in the Bessel I filter, and for a subset in the Bessel R and the Gunn z filters. We measure a polarisation degree of (0.31+/-0.06)% for LHS102BC. We fail to detect linear polarisation in the rest of our sample, with upper-limits on the polarisation degree of each object of 0.09% to 0.76% (95% CL). For those targets we do not find evidence of large-scale cloud horizontal structure in our data. Together with previous surveys, our results set the fraction of ultra-cool dwarfs with detected linear polarisation to (30+10-6)% (1-sigma). For three brown dwarfs, our observations indicate polarisation degrees different (at the 3-sigma level) than previously reported, giving hints of possible variations. Our results fail to correlate with the current model predictions for ultra-cool dwarf polarisation for a flattening-induced polarisation, or with the variability studies for a polarisation induced by an hetereneous cloud cover. This stresses the intricacy of each of those tasks, but may as well proceed from complex and dynamic atmospheric processes.Comment: 8 pages, 2 figures, accepted by A&A. Reference problem and a few typos corrected; improved error treatment of Zapatero Osorio et al (2005) data, leading to minor differences in the result

Plausible home stars of the interstellar object 'Oumuamua found in Gaia DR2

The first detected interstellar object 'Oumuamua that passed within 0.25au of the Sun on 2017 September 9 was presumably ejected from a stellar system. We use its newly determined non-Keplerian trajectory together with the reconstructed Galactic orbits of 7 million stars from Gaia DR2 to identify past close encounters. Such an "encounter" could reveal the home system from which 'Oumuamua was ejected. The closest encounter, at 0.60pc (0.53-0.67pc, 90% confidence interval), was with the M2.5 dwarf HIP 3757 at a relative velocity of 24.7km/s, 1Myr ago. A more distant encounter (1.6pc) but with a lower encounter (ejection) velocity of 10.7km/s was with the G5 dwarf HD 292249, 3.8Myr ago. Two more stars have encounter distances and velocities intermediate to these. The encounter parameters are similar across six different non-gravitational trajectories for 'Oumuamua. Ejection of 'Oumuamua by scattering from a giant planet in one of the systems is plausible, but requires a rather unlikely configuration to achieve the high velocities found. A binary star system is more likely to produce the observed velocities. None of the four home candidates have published exoplanets or are known to be binaries. Given that the 7 million stars in Gaia DR2 with 6D phase space information is just a small fraction of all stars for which we can eventually reconstruct orbits, it is a priori unlikely that our current search would find 'Oumuamua's home star system. As 'Oumuamua is expected to pass within 1pc of about 20 stars and brown dwarfs every Myr, the plausibility of a home system depends also on an appropriate (low) encounter velocity.Comment: Accepted to The Astronomical Journa

Towards real-time classification of astronomical transients

Exploration of time domain is now a vibrant area of research in astronomy, driven by the advent of digital synoptic sky surveys. While panoramic surveys can detect variable or transient events, typically some follow-up observations are needed; for short-lived phenomena, a rapid response is essential. Ability to automatically classify and prioritize transient events for follow-up studies becomes critical as the data rates increase. We have been developing such methods using the data streams from the Palomar-Quest survey, the Catalina Sky Survey and others, using the VOEventNet framework. The goal is to automatically classify transient events, using the new measurements, combined with archival data (previous and multi-wavelength measurements), and contextual information (e.g., Galactic or ecliptic latitude, presence of a possible host galaxy nearby, etc.); and to iterate them dynamically as the follow-up data come in (e.g., light curves or colors). We have been investigating Bayesian methodologies for classification, as well as discriminated follow-up to optimize the use of available resources, including Naive Bayesian approach, and the non-parametric Gaussian process regression. We will also be deploying variants of the traditional machine learning techniques such as Neural Nets and Support Vector Machines on datasets of reliably classified transients as they build up