The Effect of Corotation on the Radial Gradient of Metallicity of Spiral Galaxies

The corotation radius in a spiral galaxy is the radius where the spiral pattern speed has the same velocity of the rotation curve. By compiling results from the literature for 20 spiral galaxies we verified a strong correlation between the radius of the minima or inflections of the metallicity distribution and the corotation radius.Comment: 3 pages, 1 figur

First optical images of circumstellar dust surrounding the debris disk candidate HD 32297

Near-infrared imaging with the Hubble Space Telescope recently revealed a circumstellar dust disk around the A star HD 32297. Dust scattered light is detected as far as 400 AU radius and the linear morphology is consistent with a disk ~10 degrees away from an edge-on orientation. Here we present the first optical images that show the dust scattered light morphology from 560 to 1680 AU radius. The position angle of the putative disk midplane diverges by 31 degrees and the color of dust scattering is most likely blue. We associate HD 32297 with a wall of interstellar gas and the enigmatic region south of the Taurus molecular cloud. We propose that the extreme asymmetries and blue disk color originate from a collision with a clump of interstellar material as HD 32297 moves southward, and discuss evidence consistent with an age of 30 Myr or younger.Comment: 5 pages; Accepted for publication in ApJ Letter

Revised metallicity classes for low-mass stars: dwarfs (dM), subdwarfs (sdM), extreme subdwarfs (esdM), and ultra subdwarfs (usdM)

The current classification system of M stars on the main sequence distinguishes three metallicity classes (dwarfs - dM, subdwarfs - sdM, and extreme subdwarfs - esdM). The spectroscopic definition of these classes is based on the relative strength of prominent CaH and TiO molecular absorption bands near 7000A, as quantified by three spectroscopic indices (CaH2, CaH3, and TiO5). We re-examine this classification system in light of our ongoing spectroscopic survey of stars with proper motion \mu > 0.45 "/yr, which has increased the census of spectroscopically identified metal-poor M stars to over 400 objects. Kinematic separation of disk dwarfs and halo subdwarfs suggest deficiencies in the current classification system. Observations of common proper motion doubles indicates that the current dM/sdM and sdM/esdM boundaries in the [TiO5,CaH2+CaH3] index plane do not follow iso-metallicity contours, leaving some binaries inappropriately classified as dM+sdM or sdM+esdM. We propose a revision of the classification system based on an empirical calibration of the TiO/CaH ratio for stars of near solar metallicity. We introduce the parameter \zeta_{TiO/CaH} which quantifies the weakening of the TiO bandstrength due to metallicity effect, with values ranging from \zeta_{TiO/CaH}=1 for stars of near-solar metallicity to \zeta_{TiO/CaH}~0 for the most metal-poor (and TiO depleted) subdwarfs. We redefine the metallicity classes based on the value of the parameter \zeta_{TiO/CaH}; and refine the scheme by introducing an additional class of ultra subdwarfs (usdM). We introduce sequences of sdM, esdM, and usdM stars to be used as formal classification standards.Comment: 15 pages, accepted for publication in the Astrophysical Journa

Photoionization microscopy on magnesium atom and comparison with hydrogenic theory

International audienc

A Catalog of Cool Dwarf Targets for the Transiting Exoplanet Survey Satellite

We present a catalog of cool dwarf targets ($V-J>2.7$, $T_{\rm eff} \lesssim 4000 K$) and their stellar properties for the upcoming Transiting Exoplanet Survey Satellite (TESS), for the purpose of determining which cool dwarfs should be observed using two-minute observations. TESS has the opportunity to search tens of thousands of nearby, cool, late K and M-type dwarfs for transiting exoplanets, an order of magnitude more than current or previous transiting exoplanet surveys, such as {\it Kepler}, K2 and ground-based programs. This necessitates a new approach to choosing cool dwarf targets. Cool dwarfs were chosen by collating parallax and proper motion catalogs from the literature and subjecting them to a variety of selection criteria. We calculate stellar parameters and TESS magnitudes using the best possible relations from the literature while maintaining uniformity of methods for the sake of reproducibility. We estimate the expected planet yield from TESS observations using statistical results from the Kepler Mission, and use these results to choose the best targets for two-minute observations, optimizing for small planets for which masses can conceivably be measured using follow up Doppler spectroscopy by current and future Doppler spectrometers. The catalog is incorporated into the TESS Input Catalog and TESS Candidate Target List until a more complete and accurate cool dwarf catalog identified by ESA's Gaia Mission can be incorporated.Comment: Accepted to The Astronomical Journal. For the full catalog, please contact the corresponding autho

Percolating magmas in three dimensions

The classical models of volcanic eruptions assume that they originate as a consequence of critical stresses or critical strain rates being exceeded in the magma followed by catastrophic fragmentation. In a recent paper (Gaonac'h et al., 2003) we proposed an additional mechanism based on the properties of complex networks of overlapping bubbles; that extreme multibubble coalescence could lead to catastrophic changes in the magma rheology at a critical vesicularity. This is possible because at a critical vesicularity <i>P<sub>c</sub></i> (the percolation threshold), even in the absence of external stresses the magma fragments. By considering 2-D percolation with the (observed) extreme power law bubble distributions, we showed numerically that <i>P<sub>2c</sub></i> had the apparently realistic value ≈0.7. <br><br> The properties of percolating systems are, however, significantly different in 2-D and 3-D. In this paper, we discuss various new features relevant to 3-D percolation and compare the model predictions with empirical data on explosive volcanism. The most important points are a) bubbles and magma have different 3-D critical percolation points; we show numerically that with power law bubble distributions that the important magma percolation threshold <i>P<sub>3c,m</sub></i> has the high value ≈0.97±0.01, b) a generic result of 3-D percolation is that the resulting primary fragments will have power law distributions with exponent <i>B<sub>3f</sub></i>≈1.186±0.002, near the empirical value (for pumice) ≈1.1±0.1; c) we review the relevant percolation literature and point out that the elastic properties may have lower – possibly more realistic – critical vesicularities relevant to magmas; d) we explore the implications of long range correlations (power law bubble distributions) and discuss this in combination with bubble anisotropy; e) we propose a new kind of intermediate "elliptical" dimensional percolation involving differentially elongated bubbles and show that it can lead to somewhat lower critical thresholds. <br><br> These percolation mechanisms for catastrophically weakening magma would presumably operate in conjunction with the classical critical stress and critical strain mechanisms. We conclude that percolation theory provides an attractive theoretical framework for understanding highly vesicular magma

An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data.

Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by specially designed instrumented vehicles, which requires time and money and is only able to cover a limited proportion of the road network. In light of the ubiquitous use of smartphones, this paper proposes an automatic pothole detection system utilizing the built-in vibration sensors and global positioning system receivers in smartphones. We collected road condition data in a city using dedicated vehicles and smartphones with a purpose-built mobile application designed for this study. A series of processing methods were applied to the collected data, and features from different frequency domains were extracted, along with various machine-learning classifiers. The results indicated that features from the time and frequency domains outperformed other features for identifying potholes. Among the classifiers tested, the Random Forest method exhibited the best classification performance for potholes, with a precision of 88.5% and recall of 75%. Finally, we validated the proposed method using datasets generated from different road types and examined its universality and robustness