Applying Machine Learning to Catalogue Matching in Astrophysics

We present the results of applying automated machine learning techniques to the problem of matching different object catalogues in astrophysics. In this study we take two partially matched catalogues where one of the two catalogues has a large positional uncertainty. The two catalogues we used here were taken from the HI Parkes All Sky Survey (HIPASS), and SuperCOSMOS optical survey. Previous work had matched 44% (1887 objects) of HIPASS to the SuperCOSMOS catalogue. A supervised learning algorithm was then applied to construct a model of the matched portion of our catalogue. Validation of the model shows that we achieved a good classification performance (99.12% correct). Applying this model, to the unmatched portion of the catalogue found 1209 new matches. This increases the catalogue size from 1887 matched objects to 3096. The combination of these procedures yields a catalogue that is 72% matched.Comment: 8 Pages, 5 Figure

Wide Field Imaging. I. Applications of Neural Networks to object detection and star/galaxy classification

[Abriged] Astronomical Wide Field Imaging performed with new large format CCD detectors poses data reduction problems of unprecedented scale which are difficult to deal with traditional interactive tools. We present here NExt (Neural Extractor): a new Neural Network (NN) based package capable to detect objects and to perform both deblending and star/galaxy classification in an automatic way. Traditionally, in astronomical images, objects are first discriminated from the noisy background by searching for sets of connected pixels having brightnesses above a given threshold and then they are classified as stars or as galaxies through diagnostic diagrams having variables choosen accordingly to the astronomer's taste and experience. In the extraction step, assuming that images are well sampled, NExt requires only the simplest a priori definition of "what an object is" (id est, it keeps all structures composed by more than one pixels) and performs the detection via an unsupervised NN approaching detection as a clustering problem which has been thoroughly studied in the artificial intelligence literature. In order to obtain an objective and reliable classification, instead of using an arbitrarily defined set of features, we use a NN to select the most significant features among the large number of measured ones, and then we use their selected features to perform the classification task. In order to optimise the performances of the system we implemented and tested several different models of NN. The comparison of the NExt performances with those of the best detection and classification package known to the authors (SExtractor) shows that NExt is at least as effective as the best traditional packages.Comment: MNRAS, in press. Paper with higher resolution images is available at http://www.na.astro.it/~andreon/listapub.htm

Infrared Emission from the Composite Grains: Effects of Inclusions and Porosities on the 10 and 18 μm\mu m Features

In this paper we study the effects of inclusions and porosities on the emission properties of silicate grains and compare the model curves with the observed infrared emission from circumstellar dust. We calculate the absorption efficiency of the composite grain, made up of a host silicate oblate spheroid and inclusions of ice/graphite/or voids, in the spectral region 5.0-25.0$\mu m$. The absorption efficiencies of the composite spheroidal oblate grains for three axial ratios are computed using the discrete dipole approximation (DDA). We study the absorption as a function of the volume fraction of the inclusions and porosity. In particular, we study the variation in the $10\mu m$ and $18\mu m$ emission features with the volume fraction of the inclusions and porosities. We then calculate the infrared fluxes for these composite grains at several dust temperatures (T=200-350K) and compare the model curves with the average observed IRAS-LRS curve, obtained for circumstellar dust shells around oxygen rich M-type stars. The model curves are also compared with two other individual stars. The results on the composite grains clearly indicate that the silicate feature at 10$\mu m$ shifts with the volume fraction of graphite inclusions. The feature does not shift with the porosity. Both the features do not show any broadening with the inclusions or porosity. The absorption efficiencies of the composite grains calculated using DDA and Effective Medium Approximation (EMA) do not agree. The composite grain models presented in this study need to be compared with the observed IR emission from the circumstellar dust around a few more stars.Comment: 12 pages, 12 figures, 7 tables; To appear in A & A, 201

Revealing the cold dust in low-metallicity environments: I. Photometry analysis of the Dwarf Galaxy Survey with Herschel

Context. We present new photometric data from our Herschel Guaranteed Time Key Programme, the Dwarf Galaxy Survey (DGS), dedicated to the observation of the gas and dust in low-metallicity environments. A total of 48 dwarf galaxies were observed with the PACS and SPIRE instruments onboard the Herschel Space Observatory at 70, 100, 160, 250, 350, and 500 µm. Aims. The goal of this paper is to provide reliable far infrared (FIR) photometry for the DGS sample and to analyse the FIR/submillimetre (submm) behaviour of the DGS galaxies. We focus on a systematic comparison of the derived FIR properties (FIR luminosity, LFIR, dust mass, Mdust , dust temperature, T, emissivity index, β) with more metal-rich galaxies and investigate the detection of a potential submm excess. Methods. The data reduction method is adapted for each galaxy in order to derive the most reliable photometry from the final maps. The derived PACS flux densities are compared with the Spitzer MIPS 70 and 160 µm bands. We use colour-colour diagrams to analyse the FIR/submm behaviour of the DGS galaxies and modified blackbody fitting procedures to determine their dust properties. To study the variation in these dust properties with metallicity, we also include galaxies from the Herschel KINGFISH sample, which contains more metal-rich environments, totalling 109 galaxies. Results. The location of the DGS galaxies on Herschel colour-colour diagrams highlights the differences in dust grain properties and/or global environments of low-metallicity dwarf galaxies. The dust in DGS galaxies is generally warmer than in KINGFISH galaxies (TDGS ∼ 32 K and TKINGFIS H ∼ 23 K). The emissivity index, β, is ∼ 1.7 in the DGS, however metallicity does not make a strong effect on β. The proportion of dust mass relative to stellar mass is lower in low-metallicity galaxies: Mdust /Mstar ∼ 0.02% for the DGS versus 0.1% for KINGFISH. However, per unit dust mass, dwarf galaxies emit about six times more in the FIR/submm than higher metallicity galaxies. Out of the 22 DGS galaxies detected at 500 µm, about 41% present an excess in the submm beyond the explanation of our dust SED model, and this excess can go up to 150% above the prediction from the model. The excess mainly appears in lower metallicity galaxies (12+log(O/H) ;S 8.3), and the strongest excesses are detected in the most metal-poor galaxies. However, we so stress the need for observations longwards of the Herschel wavelengths to detect any submm excess appearing beyond 500 .Norwegian Lis

From crisis to capacity: Lessons learned from youth e-mentoring during the COVID-19 pandemic

The COVID-19 pandemic and associated need for social isolation left in-person youth mentoring programs scrambling to keep mentees and mentors connected, and many programs turned to e-mentoring. To better understand the transition period and to inform e-mentoring practice in a post-COVID world, this study explored the experience of mentoring programs shifting to e-mentoring during the first year of the pandemic. Seven remote focus group discussions were conducted with twenty-three staff members from twenty U.S. youth mentoring organizations that used the iCouldBe e-mentoring platform during Spring/summer 2020 or Fall/Winter 2020–2021. Thematic content analysis was used to uncover insights from the data. E-mentoring was successful overall for keeping mentees and mentors in touch, especially for matches with a strong connection before the pandemic. Zoom and text messaging were the most used virtual communication methods. Programs faced many challenges but also experienced unexpected positives, including a strong interest in future e-mentoring implementation. Participants recommended that programs interested in e-mentoring start small and with intention; they also requested a central website with e-mentoring support and ways to connect with other programs and mentors. Although the literature on e-mentoring remains limited, this study contributes a picture of e-mentoring success even during a global crisis