Milky Way globular cluster metallicity and low-mass X-ray binaries: The red giant influence

Galactic and extragalactic studies have shown that metal-rich globular clusters (GCs) are approximately three times more likely to host bright low-mass X-ray binaries (LMXBs) than metal-poor GCs. There is no satisfactory explanation for this metallicity effect. We tested the hypothesis that the number density of red giant branch (RGB) stars is larger in metal-rich GCs, and thus potentially the cause of the metallicity effect. Using Hubble Space Telescope photometry for 109 unique Milky Way GCs, we investigated whether RGB star density was correlated with GC metallicity. Isochrone fitting was used to calculate the number of RGB stars, which were normalized by the GC mass and fraction of observed GC luminosity, and determined density using the volume at the half-light radius (rh). The RGB star number density was weakly correlated with metallicity [Fe/H], giving Spearman and Kendall Rank test p-values of 0.000 16 and 0.000 21 and coefficients rs = 0.35 and τ = 0.24, respectively. This correlation may be biased by a possible dependence of rh on [Fe/H], although studies have shown that rh is correlated with Galactocentric distance and independent of [Fe/H]. The dynamical origin of the rh-metallicity correlation (tidal stripping) suggests that metal-rich GCs may have had more active dynamical histories, which would promote LMXB formation. No correlation between the RGB star number density and metallicity was found when using only the GCs that hosted quiescent LMXBs. A complete census of quiescent LMXBs in our Galaxy is needed to further probe the metallicity effect, which will be possible with the upcoming launch of eROSITA

X-rays beware: The deepest Chandra catalogue of point sources in M31

This study represents the most sensitive Chandra X-ray point source catalogue of M31. Using 133 publicly available Chandra ACIS-I/S observations totalling ~1 Ms, we detected 795 X-ray sources in the bulge, north-east, and south-west fields of M31, covering an area of ≈0.6 deg2, to a limiting unabsorbed 0.5-8.0 keV luminosity of ~1034 erg s-1. In the inner bulge, where exposure is approximately constant, X-ray fluxes represent average values because they were determined from many observations over a long period of time. Similarly, our catalogue is more complete in the bulge fields since monitoring allowedmore transient sources to be detected. The catalogue was cross-correlated with a previous XMM-Newton catalogue of M31\u27s D25 isophote consisting of 1948 X-ray sources, with only 979 within the field of view of our survey. We found 387 (49 per cent) of our Chandra sources (352 or 44 per cent unique sources) matched to within 5 arcsec of 352 XMM-Newton sources. Combining this result with matching done to previous Chandra X-ray sources we detected 259. new sources in our catalogue. We created X-ray luminosity functions (XLFs) in the soft (0.5-2.0 keV) and hard (2.0-8.0 keV) bands that are the most sensitive for any large galaxy based on our detection limits. Completeness-corrected XLFs show a break around ≈1.3 × 1037 erg s-1, consistent with previous work. As in past surveys, we find that the bulge XLFs are flatter than the disc, indicating a lack of bright high-mass X-ray binaries in the disc and an aging population of low-mass X-ray binaries in the bulge

Identifying new X-ray binary candidates in M31 using random forest classification

Identifying X-ray binary (XRB) candidates in nearby galaxies requires distinguishing them from possible contaminants including foreground stars and background active galactic nuclei. This work investigates the use of supervised machine learning algorithms to identify highprobability XRB candidates. Using a catalogue of 943 Chandra X-ray sources in the Andromeda galaxy, we trained and tested several classification algorithms using the X-ray properties of 163 sources with previously known types. Amongst the algorithms tested, we find that random forest classifiers give the best performance and work better in a binary classification (XRB/non-XRB) context compared to the use of multiple classes. Evaluating our method by comparingwith classifications from visible-light and hardX-ray observations as part of the Panchromatic Hubble Andromeda Treasury, we find compatibility at the 90 per cent level, although we caution that the number of source in common is rather small. The estimated probability that an object is an XRB agrees well between the random forest binary and multiclass approaches and we find that the classifications with the highest confidence are in the XRB class. Themost discriminating X-ray bands for classification are the 1.7-2.8, 0.5-1.0, 2.0-4.0, and 2.0-7.0 keV photon flux ratios. Of the 780 unclassified sources in the Andromeda catalogue, we identify 16 new high-probability XRB candidates and tabulate their properties for follow-up

Specialising Word Vectors for Lexical Entailment

We present LEAR (Lexical Entailment Attract-Repel), a novel post-processing method that transforms any input word vector space to emphasise the asymmetric relation of lexical entailment (LE), also known as the IS-A or hyponymy-hypernymy relation. By injecting external linguistic constraints (e.g., WordNet links) into the initial vector space, the LE specialisation procedure brings true hyponymy-hypernymy pairs closer together in the transformed Euclidean space. The proposed asymmetric distance measure adjusts the norms of word vectors to reflect the actual WordNet-style hierarchy of concepts. Simultaneously, a joint objective enforces semantic similarity using the symmetric cosine distance, yielding a vector space specialised for both lexical relations at once. LEAR specialisation achieves state-of-the-art performance in the tasks of hypernymy directionality, hypernymy detection, and graded lexical entailment, demonstrating the effectiveness and robustness of the proposed asymmetric specialisation model

Surface modification of graphitic carbon nitride with copper nanoparticles

Two different synthetic routes were employed to modify surface of graphitic carbon nitride (gCN) with copper nanoparticles (CuNPs). Structure, morphology and CuNPs distribution on presynthesized g-CN surface are characterized by FT-IR, XRD and TEM. Results suggested that the simpler method based on mixing of precursors in inert atmosphere and room temperature, resulted in better CuNPs distribution compared to method which used refluxing as a step in synthesis

Morph-fitting: Fine-tuning word vector spaces with simple language-specific rules

Morphologically rich languages accentuate two properties of distributional vector space models: 1) the difficulty of inducing accurate representations for low-frequency word forms; and 2) insensitivity to distinct lexical relations that have similar distributional signatures. These effects are detrimental for language understanding systems, which may infer that inexpensive is a rephrasing for expensive or may not associate acquire with acquires. In this work, we propose a novel morph-fitting procedure which moves past the use of curated semantic lexicons for improving distributional vector spaces. Instead, our method injects morphological constraints generated using simple language-specific rules, pulling inflectional forms of the same word close together and pushing derivational antonyms far apart. In intrinsic evaluation over four languages, we show that our approach: 1) improves low-frequency word estimates; and 2) boosts the semantic quality of the entire word vector collection. Finally, we show that morph-fitted vectors yield large gains in the downstream task of dialogue state tracking, highlighting the importance of morphology for tackling long-tail phenomena in language understanding tasks

Astro 2020 Science White Paper: Time Domain Studies of Neutron Star and Black Hole Populations: X-ray Identification of Compact Object Types

What are the most important conditions and processes governing the growth of stellar-origin compact objects? The identification of compact object type as either black hole (BH) or neutron star (NS) is fundamental to understanding their formation and evolution. To date, time-domain determination of compact object type remains a relatively untapped tool. Measurement of orbital periods, pulsations, and bursts will lead to a revolution in the study of the demographics of NS and BH populations, linking source phenomena to accretion and galaxy parameters (e.g., star formation, metallicity). To perform these measurements over sufficient parameter space, a combination of a wide-field (>5000 deg^2) transient X-ray monitor over a dynamic energy range (~1-100 keV) and an X-ray telescope for deep surveys with <5 arcsec PSF half-energy width (HEW) angular resolution are required. Synergy with multiwavelength data for characterizing the underlying stellar population will transform our understanding of the time domain properties of transient sources, helping to explain details of supernova explosions and gravitational wave event rates.Comment: 9 pages, 2 figures. Submitted to the Astro2020 Decadal Surve