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Register a new userA new method to quantify differentiate collapse models of star formation
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Continuum emissions from dust grains are used as a general probe to constrain the initial physical conditions of molecular dense cores where new stars may born. To get as much information as possible from dust emissions, we have developed a tool, named as $COREGA$, which is capable of identifying positions of dense cores, optimizing a three-dimensional model for the dense cores with well characterized uncertainties. $COREGA$ can also estimate the physical properties of dense cores, such as density, temperature, and dust emissivity, through analyzing multi-wavelength dust continuum data sets. In the numerical tests on $COREGA$, the results of fitting simulated data are consistent with initial built-in parameters. We also demonstrate $COREGA$ by adding random gaussian noises with Monte Carlo methods and show that the results are stable against varying observational noise intensities within certain levels. A beam size $<$ 3 arcsec and rms $<$ 0.2mJy/pixel (1 pixel = 0.1) is needed for ALMA to distinguish different collapse models, such as power law and Bonner-Ebert sphere, during continuum observations of massive dense cores in Orion molecular cloud. Based on its advanced algorithm, $COREGA$ is capable of giving a quick and deep analysis on dust cores.
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We present a new observational method to evaluate the star formation law as formulated by Schmidt: the power-law expression assumed to relate the rate of star formation in a volume of space to the local total gas volume density. Volume densities in the clouds surrounding an OB association are determined with a simple model which considers atomic hydrogen as a photodissociation product on cloud surfaces. The photodissociating flux incident on the cloud is computed from the far-UV luminosity of the OB association and the geometry. We have applied this PDR Method to a sample of star-forming regions in M33 using VLA 21-cm data for the HI and GALEX imagery in the far-UV. It provides an estimate of the total volume density of hydrogen (atomic + molecular) in the gas clouds surrounding the young star cluster. A logarithmic graph of the cluster UV luminosity versus the surrounding gas density is a direct measure of the star formation law. However, this plot is severely affected by observational selection, rendering large areas of the diagram inaccessible to the data. An ordinary least-squares regression fit therefore gives a strongly biased result. Its slope primarily reflects the boundary defined when the 21-cm line becomes optically thick, no longer reliably measuring the HI column density. We use a maximum-likelihood statistical approach which can deal with truncated and skewed data, taking into account the large uncertainties in the derived total gas densities. The exponent we obtain for the Schmidt law in M33 is 1.4 pm 0.2.
The star formation rate (SFR) is one of the most fundamental parameters of galaxies, but nearly all of the standard SFR diagnostics are difficult to measure in active galaxies because of contamination from the active galactic nucleus (AGN). Being less sensitive to dust extinction, the mid-infrared fine-structure lines of [NeII] 12.81 micron and [NeIII] 15.56 micron effectively trace the SFR in star-forming galaxies. These lines also have the potential to serve as a reliable SFR indicator in active galaxies, provided that their contribution from the AGN narrow-line region can be removed. We use a new set of photoionization calculations with realistic AGN spectral energy distributions and input assumptions to constrain the magnitude of [NeII] and [NeIII] produced by the narrow-line region for a given strength of [NeV] 14.32 micron. We demonstrate that AGNs emit a relatively restricted range of [NeII]/[NeV] and [NeIII]/[NeV] ratios. Hence, once [NeV] is measured, the AGN contribution to the low-ionization Ne lines can be estimated, and the SFR can be determined from the strength of [NeII] and [NeIII]. We find that AGN host galaxies have similar properties as compact extragalactic HII regions, which indicates that the star formation in AGN hosts is spatially concentrated. This suggests a close relationship between black hole accretion and nuclear star formation. We update the calibration of [NeII] and [NeIII] strength as a SFR indicator, explicitly considering the effects of metallicity, finding very good relations between Ne fractional abundances and the [NeIII]/[NeII] ratio for different metallicities, ionization parameters, and starburst ages. Comparison of neon-based SFRs with independent SFRs for active and star-forming galaxies shows excellent consistency with small scatter ($sim0.18$ dex).
The cloud-scale physics of star formation and feedback represent the main uncertainty in galaxy formation studies. Progress is hampered by the limited empirical constraints outside the restricted environment of the Local Group. In particular, the poorly-quantified time evolution of the molecular cloud lifecycle, star formation, and feedback obstructs robust predictions on the scales smaller than the disc scale height that are resolved in modern galaxy formation simulations. We present a new statistical method to derive the evolutionary timeline of molecular clouds and star-forming regions. By quantifying the excess or deficit of the gas-to-stellar flux ratio around peaks of gas or star formation tracer emission, we directly measure the relative rarity of these peaks, which allows us to derive their lifetimes. We present a step-by-step, quantitative description of the method and demonstrate its practical application. The methods accuracy is tested in nearly 300 experiments using simulated galaxy maps, showing that it is capable of constraining the molecular cloud lifetime and feedback time-scale to $<0.1$ dex precision. Access to the evolutionary timeline provides a variety of additional physical quantities, such as the cloud-scale star formation efficiency, the feedback outflow velocity, the mass loading factor, and the feedback energy or momentum coupling efficiencies to the ambient medium. We show that the results are robust for a wide variety of gas and star formation tracers, spatial resolutions, galaxy inclinations, and galaxy sizes. Finally, we demonstrate that our method can be applied out to high redshift ($zlesssim4$) with a feasible time investment on current large-scale observatories. This is a major shift from previous studies that constrained the physics of star formation and feedback in the immediate vicinity of the Sun.
We present CO(1-0) maps of 12 warm H$_2$-selected Hickson Compact Groups (HCGs), covering 14 individually imaged warm H$_2$ bright galaxies, with CARMA. We found a variety of molecular gas distributions within the HCGs, including regularly rotating disks, bars, rings, tidal tails, and possibly nuclear outflows, though the molecular gas morphologies are more consistent with spirals and early-type galaxies than mergers and interacting systems. Our CO-imaged HCG galaxies show star formation suppression of $langle$S$rangle$=10$pm$5, distributed bimodally, with five objects exhibiting suppressions of S$gtrsim$10 and depletion timescales $gtrsim$10Gyr. This star formation inefficiency is also seen in the efficiency per freefall time. We investigate the gas-to-dust ratios of these galaxies to determine if an incorrect conversion caused the apparent suppression and find that HCGs have normal ratios. It is likely that the cause of the suppression in these objects is associated with shocks injecting turbulence into the molecular gas. Galaxies with high star formation suppression (S$gtrsim$10) also appear to be those in the most advanced stages of transition across optical and infrared color space. This supports the idea that some galaxies in HCGs are transitioning objects, where a disruption of the existing molecular gas in the system suppresses star formation by inhibiting the molecular gas from collapsing and forming stars efficiently. These observations, combined with recent work on poststarburst galaxies with molecular reservoirs, indicates that galaxies do not need to expel their molecular reservoirs prior to quenching star formation and transitioning from blue spirals to red early-type galaxies. This may imply that star formation quenching can occur without the need to starve a galaxy of cold gas first.
While gravitational waves have been detected from mergers of binary black holes and binary neutron stars, signals from core collapse supernovae, the most energetic explosions in the modern Universe, have not been detected yet. Here we present a new method to analyse the data of the LIGO, Virgo and KAGRA network to enhance the detection efficiency of this category of signals. The method takes advantage of a peculiarity of the gravitational wave signal emitted in the core collapse supernova and it is based on a classification procedure of the time-frequency images of the network data performed by a convolutional neural network trained to perform the task to recognize the signal. We validate the method using phenomenological waveforms injected in Gaussian noise whose spectral properties are those of the LIGO and Virgo advanced detectors and we conclude that this method can identify the signal better than the present algorithm devoted to select gravitational wave transient signal.
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