Astrophysics

We analyse two analytic fitting profiles to model the ram pressure exerted over satellites galaxies for different environments and epochs, using hydrodynamical resimulations of groups and clusters of galaxies to measure the ram pressure from the gas particle distribution. First, we compare the predictions given by a known β -profile model with the simulation measurements, finding that the profile is not in agreement with the expected behaviour by missing the dependence with the halo mass and halocentric distance. It features a systematic underestimation of the predicted ram pressure at high redshifts ( z>1.5 ), which increases towards the central regions of the halos and it is independent of halo mass, reaching differences larger than two decades for satellites at r<0.4 R vir . This behaviour reverses as redshift decreases, featuring an increasing over-estimation with halocentric distance at z=0 . As an alternative, we introduce a new universal analytic model for the profiles consisting in a damped power law, and we provide a new set of fitted parameters which can recover the ram pressure dependence on halo mass and redshifts. Finally, we analyse the impact of these analytic profiles in the galaxy properties by applying a semi-analytic model of galaxy formation and evolution on top of the simulations. We find the number of galaxies experiencing large amounts of accumulated ram pressure stripping have low stellar mass ( M ????10 9 M ??), and their specific star formation rates depend significantly on the ram pressure modelling, particularly at high redshifts ( z>1.5 ).

The study of strong-lensing systems conventionally involves constructing a mass distribution that can reproduce the observed multiply-imaging properties. Such mass reconstructions are generically non-unique. Here, we present an alternative strategy: instead of modelling the mass distribution, we search cosmological galaxy-formation simulations for plausible matches. In this paper we test the idea on seven well-studied lenses from the SLACS survey. For each of these, we first pre-select a few hundred galaxies from the EAGLE simulations, using the expected Einstein radius as an initial criterion. Then, for each of these pre-selected galaxies, we fit for the source light distribution, while using MCMC for the placement and orientation of the lensing galaxy, so as to reproduce the multiple images and arcs. The results indicate that the strategy is feasible, and even yields relative posterior probabilities of two different galaxy-formation scenarios, though these are not statistically significant yet. Extensions to other observables, such as kinematics and colours of the stellar population in the lensing galaxy, is straightforward in principle, though we have not attempted it yet. Scaling to arbitrarily large numbers of lenses also appears feasible. This will be especially relevant for upcoming wide-field surveys, through which the number of galaxy lenses will rise possibly a hundredfold, which will overwhelm conventional modelling methods.

We report the discovery of a bright, compact ultraviolet source at a projected separation of 1.1~kpc from the known active galactic nucleus (AGN) in Mrk~766 based on Astrosat/UVIT observations. We perform radial profile analysis and derive the UV flux almost free from the nearby contaminating sources. The new source is about 2.5 and 5.6 times fainter than the AGN in the far and near UV bands. The two sources appear as a pair of nuclei in Mrk~766. We investigate the nature of the new source based on the UV flux ratio, X-ray and optical emission. The new source is highly unlikely to be another accreting supermassive black hole in Mrk~766 as it lacks X-ray emission. We find that the UV/Optical flux of the new source measured at four different bands closely follow the shape of the template spectrum of starburst galaxies. This strongly suggests that the new source is a compact star-forming region.

To derive the properties of the dense cores in the galactic star-forming complex NGC6357 and to investigate the effects of an intense far-UV radiation field on their properties, we mapped the region at 450 and 850 micron, and in the CO(3-2) line with the JCMT. We also made use of the Herschel Hi-GAL data at 70 and 160 micron. We used Gaussclumps to retrieve 686 compact cores embedded in the diffuse sub-mm emission and constructed their SED from 70 to 850 micron, from which we derived mass and temperature. The estimated mass completeness limit is ~5Mo. We divided the observed area in an 'active' region, exposed to the far-UV radiation from the more massive members of three star clusters (411 cores), and a 'quiescent' region, less affected by far-UV radiation (275 cores). We also attempted to select a sample of pre-stellar cores based on cross-correlation with 70 micron emission and red WISE point sources. Most of the cores above the mass completeness limit are likely to be gravitationally bound. The fraction of gas in dense cores is very low, 1.4%. We found a mass-size relation log(M/Mo) ~ (2.0-2.4) x log (D/arcsec), depending on the precise selection of the sample. The temperature distributions in the two sub-regions are clearly different, peaking at ~25K in the quiescent region and at ~35K in the active region. The core mass functions are different as well, at a 2sigma level, consistent with a Salpeter IMF in the quiescent region and flatter than that in the active region. The dense cores lying close to the HII regions are consistent with pre-existing cores being gradually engulfed by a PDR and photoevaporating. We attribute the different global properties of dense cores in the two sub-regions to the influence of the far-UV radiation field.

Strong line metallicity calibrations are widely used to determine the gas phase metallicities of individual HII regions and entire galaxies. Over a decade ago, based on the Sloan Digital Sky Survey Data Release 4 (SDSS DR4), Kewley \& Ellison published the coefficients of third-order polynomials that can be used to convert between different strong line metallicity calibrations for global galaxy spectra. Here, we update the work of Kewley \& Ellison in three ways. First, by using a newer data release (DR7), we approximately double the number of galaxies used in polynomial fits, providing statistically improved polynomial coefficients. Second, we include in the calibration suite five additional metallicity diagnostics that have been proposed in the last decade and were not included by Kewley \& Ellison. Finally, we develop a new machine learning approach for converting between metallicity calibrations. The random forest algorithm is non-parametric and therefore more flexible than polynomial conversions, due to its ability to capture non-linear behaviour in the data. The random forest method yields the same accuracy as the (updated) polynomial conversions, but has the significant advantage that a single model can be applied over a wide range of metallicities, without the need to distinguish upper and lower branches in R 23 calibrations. The trained random forest is made publicly available for use in the community.

We present the results from an observing campaign to confirm the peculiar motion of the supermassive black hole (SMBH) in J0437+2456 first reported in Pesce et al. (2018). Deep observations with the Arecibo Observatory have yielded a detection of neutral hydrogen (HI) emission, from which we measure a recession velocity of 4910 km s ?? for the galaxy as a whole. We have also obtained near-infrared integral field spectroscopic observations of the galactic nucleus with the Gemini North telescope, yielding spatially resolved stellar and gas kinematics with a central velocity at the innermost radii ( 0.1 ?��???4 pc) of 4860 km s ?? . Both measurements differ significantly from the ??4810 km s ?? H 2 O megamaser velocity of the SMBH, supporting the prior indications of a velocity offset between the SMBH and its host galaxy. However, the two measurements also differ significantly from one another, and the galaxy as a whole exhibits a complex velocity structure that implies the system has recently been dynamically disturbed. These results make it clear that the SMBH is not at rest with respect to the systemic velocity of the galaxy, though the specific nature of the mobile SMBH -- i.e., whether it traces an ongoing galaxy merger, a binary black hole system, or a gravitational wave recoil event -- remains unclear.

The evidence for benzonitrile (C 6 H 5 CN}) in the starless cloud core TMC-1 makes high-resolution studies of other aromatic nitriles and their ring-chain derivatives especially timely. One such species is phenylpropiolonitrile (3-phenyl-2-propynenitrile, C 6 H 5 C 3 N), whose spectroscopic characterization is reported here for the first time. The low resolution (0.5 cm ?? ) vibrational spectrum of C 6 H 5 C 3 N} has been recorded at far- and mid-infrared wavelengths (50 - 3500 cm ?? ) using a Fourier Transform interferometer, allowing for the assignment of band centers of 14 fundamental vibrational bands. The pure rotational spectrum of the species has been investigated using a chirped-pulse Fourier transform microwave (FTMW) spectrometer (6 - 18 GHz), a cavity enhanced FTMW instrument (6 - 20 GHz), and a millimeter-wave one (75 - 100 GHz, 140 - 214 GHz). Through the assignment of more than 6200 lines, accurate ground state spectroscopic constants (rotational, centrifugal distortion up to octics, and nuclear quadrupole hyperfine constants) have been derived from our measurements, with a plausible prediction of the weaker bands through calculations. Interstellar searches for this highly polar species can now be undertaken with confidence since the astronomically most interesting radio lines have either been measured or can be calculated to very high accuracy below 300 GHz.

In the upcoming decades large facilities, such as the SKA, will provide resolved observations of the kinematics of millions of galaxies. In order to assist in the timely exploitation of these vast datasets we explore the use of a self-supervised, physics aware neural network capable of Bayesian kinematic modelling of galaxies. We demonstrate the network's ability to model the kinematics of cold gas in galaxies with an emphasis on recovering physical parameters and accompanying modelling errors. The model is able to recover rotation curves, inclinations and disc scale lengths for both CO and HI data which match well with those found in the literature. The model is also able to provide modelling errors over learned parameters thanks to the application of quasi-Bayesian Monte-Carlo dropout. This work shows the promising use of machine learning, and in particular self-supervised neural networks, in the context of kinematically modelling galaxies. This work represents the first steps in applying such models for kinematic fitting and we propose that variants of our model would seem especially suitable for enabling emission-line science from upcoming surveys with e.g. the SKA, allowing fast exploitation of these large datasets.

We introduce a simple entropy-based formalism to characterize the role of mixing in pressure-balanced multiphase clouds, and demonstrate example applications using Enzo-E (magneto)hydrodynamic simulations. Under this formalism, the high-dimensional description of the system's state at a given time is simplified to the joint distribution of mass over pressure ( P ) and entropy ( K=P/ ? γ ). As a result, this approach provides a way for (empirically and analytically) quantifying the impact of different initial conditions and sets of physics on the system evolution. We find that mixing predominantly alters the distribution along the K direction and illustrate how the formalism can be used to model mixing and cooling for fluid elements originating in the cloud. We further confirm and generalize a previously suggested criterion for cloud growth in the presence of radiative cooling, and demonstrate that the shape of the cooling curve, particularly at the low temperature end, can play an important role in controlling condensation. Moreover, we discuss the capacity of our approach to generalize such a criterion to apply to additional sets of physics, and to build intuition for the impact of subtle higher order effects not directly addressed by the criterion.

Sparse regression algorithms have been proposed as the appropriate framework to model the governing equations of a system from data, without needing prior knowledge of the underlying physics. In this work, we use sparse regression to build an accurate and explainable model of the stellar mass of central galaxies given properties of their host dark matter (DM) halo. Our data set comprises 9,521 central galaxies from the EAGLE hydrodynamic simulation. By matching the host halos to a DM-only simulation, we collect the halo mass and specific angular momentum at present time and for their main progenitors in 10 redshift bins from z=0 to z=4 . The principal component of our governing equation is a third-order polynomial of the host halo mass, which models the stellar-mass halo-mass relation. The scatter about this relation is driven by the halo mass evolution and is captured by second and third-order correlations of the halo mass evolution with the present halo mass. An advantage of sparse regression approaches is that unnecessary terms are removed. Although we include information on halo specific angular momentum, these parameters are discarded by our methodology. This suggests that halo angular momentum has little connection to galaxy formation efficiency. Our model has a root mean square error (RMSE) of 0.167 log 10 ( M ??/ M ??) , and accurately reproduces both the stellar mass function and central galaxy correlation function of EAGLE. The methodology appears to be an encouraging approach for populating the halos of DM-only simulations with galaxies, and we discuss the next steps that are required.