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Register a new userA Machine Learning Approach to Measuring the Quenched Fraction of Low-Mass Satellites Beyond the Local Group
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Observations suggest that satellite quenching plays a major role in the build-up of passive, low-mass galaxies at late cosmic times. Studies of low-mass satellites, however, are limited by the ability to robustly characterize the local environment and star-formation activity of faint systems. In an effort to overcome the limitations of existing data sets, we utilize deep photometry in Stripe 82 of the Sloan Digital Sky Survey, in conjunction with a neural network classification scheme, to study the suppression of star formation in low-mass satellite galaxies in the local Universe. Using a statistically-driven approach, we are able to push beyond the limits of existing spectroscopic data sets, measuring the satellite quenched fraction down to satellite stellar masses of ${sim}10^7~{rm M}_{odot}$ in group environments (${M}_{rm{halo}} = 10^{13-14}~h^{-1}~{rm M}_{odot}$). At high satellite stellar masses ($gtrsim 10^{10}~{rm M}_{odot}$), our analysis successfully reproduces existing measurements of the quenched fraction based on spectroscopic samples. Pushing to lower masses, we find that the fraction of passive satellites increases, potentially signaling a change in the dominant quenching mechanism at ${M}_{star} sim 10^{9}~{rm M}_{odot}$. Similar to the results of previous studies of the Local Group, this increase in the quenched fraction at low satellite masses may correspond to an increase in the efficacy of ram-pressure stripping as a quenching mechanism in groups.
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Recent observations of the fields surrounding a few Milky-Way-like galaxies in the local Universe have become deep enough to enable investigations of the predictions of the standard LCDM cosmological model down to small scales outside the Local Group. Motivated by an observed correlation between the number of dwarf satellites (N_sat) and the bulge-to-total baryonic mass ratios (B/T) of the three main galaxies in the Local Group, i.e. the Milky Way, Andromeda, and Triangulum (M33), we use published data of three well-studied galaxies outside the Local Group, namely M81, Centaurus A, and M101, and their confirmed satellites, and we find a strong and significant correlation between N_sat and B/T. This presents itself in contradiction with the hitherto published results from cosmological simulations reporting an absence of a correlation between N_sat and B/T in the LCDM model. We conclude that, based on the current data, the N_sat vs. B/T correlation is no longer a property confined to only the Local Group.
We demonstrate that highly accurate joint redshift-stellar mass probability distribution functions (PDFs) can be obtained using the Random Forest (RF) machine learning (ML) algorithm, even with few photometric bands available. As an example, we use the Dark Energy Survey (DES), combined with the COSMOS2015 catalogue for redshifts and stellar masses. We build two ML models: one containing deep photometry in the $griz$ bands, and the second reflecting the photometric scatter present in the main DES survey, with carefully constructed representative training data in each case. We validate our joint PDFs for $10,699$ test galaxies by utilizing the copula probability integral transform and the Kendall distribution function, and their univariate counterparts to validate the marginals. Benchmarked against a basic set-up of the template-fitting code BAGPIPES, our ML-based method outperforms template fitting on all of our predefined performance metrics. In addition to accuracy, the RF is extremely fast, able to compute joint PDFs for a million galaxies in just under $6$ min with consumer computer hardware. Such speed enables PDFs to be derived in real time within analysis codes, solving potential storage issues. As part of this work we have developed GALPRO, a highly intuitive and efficient Python package to rapidly generate multivariate PDFs on-the-fly. GALPRO is documented and available for researchers to use in their cosmology and galaxy evolution studies.
Recent observations of galaxies at $z gtrsim 7$, along with the low value of the electron scattering optical depth measured by the Planck mission, make galaxies plausible as dominant sources of ionizing photons during the epoch of reionization. However, scenarios of galaxy-driven reionization hinge on the assumption that the average escape fraction of ionizing photons is significantly higher for galaxies in the reionization epoch than in the local Universe. The NIRSpec instrument on the James Webb Space Telescope (JWST) will enable spectroscopic observations of large samples of reionization-epoch galaxies. While the leakage of ionizing photons will not be directly measurable from these spectra, the leakage is predicted to have an indirect effect on the spectral slope and the strength of nebular emission lines in the rest-frame ultraviolet and optical. Here, we apply a machine learning technique known as lasso regression on mock JWST/NIRSpec observations of simulated $z=7$ galaxies in order to obtain a model that can predict the escape fraction from JWST/NIRSpec data. Barring systematic biases in the simulated spectra, our method is able to retrieve the escape fraction with a mean absolute error of $Delta f_{mathrm{esc}} approx 0.12$ for spectra with $S/Napprox 5$ at a rest-frame wavelength of 1500 {AA} for our fiducial simulation. This prediction accuracy represents a significant improvement over previous similar approaches.
We describe spectroscopic observations of 21 low-mass (<0.45 M_sun) white dwarfs (WDs) from the Palomar-Green Survey obtained over four years. We use both radial velocities and infrared photometry to identify binary systems, and find that the fraction of single, low-mass WDs is <30%. We discuss the potential formation channels for these single stars including binary mergers of lower-mass objects. However, binary mergers are not likely to explain the observed number of single low-mass WDs. Thus additional formation channels, such as enhanced mass loss due to winds or interactions with substellar companions, are likely.
We search for vast planes of satellites (VPoS) in a high resolution simulation of the Local Group performed by the CLUES project, which improves significantly the resolution of former similar studies. We use a simple method for detecting planar configurations of satellites, and validate it on the known plane of M31. We implement a range of prescriptions for modelling the satellite populations, roughly reproducing the variety of recipes used in the literature, and investigate the occurence and properties of planar structures in these populations. The structure of the simulated satellite systems is strongly non-random and contains planes of satellites, predominantly co-rotating, with, in some cases, sizes comparable to the plane observed in M31 by Ibata et al.. However the latter is slightly richer in satellites, slightly thinner and has stronger co-rotation, which makes it stand out as overall more exceptional than the simulated planes, when compared to a random population. Although the simulated planes we find are generally dominated by one real structure, forming its backbone, they are also partly fortuitous and are thus not kinematically coherent structures as a whole. Provided that the simulated and observed planes of satellites are indeed of the same nature, our results suggest that the VPoS of M31 is not a coherent disc and that one third to one half of its satellites must have large proper motions perpendicular to the plane.
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