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Register a new userMilky Way Satellite Census. I. The Observational Selection Function for Milky Way Satellites in DES Y3 and Pan-STARRS DR1
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We report the results of a systematic search for ultra-faint Milky Way satellite galaxies using data from the Dark Energy Survey (DES) and Pan-STARRS1 (PS1). Together, DES and PS1 provide multi-band photometry in optical/near-infrared wavelengths over ~80% of the sky. Our search for satellite galaxies targets ~25,000 deg$^2$ of the high-Galactic-latitude sky reaching a 10$sigma$ point-source depth of $gtrsim$ 22.5 mag in the $g$ and $r$ bands. While satellite galaxy searches have been performed independently on DES and PS1 before, this is the first time that a self-consistent search is performed across both data sets. We do not detect any new high-significance satellite galaxy candidates, while recovering the majority of satellites previously detected in surveys of comparable depth. We characterize the sensitivity of our search using a large set of simulated satellites injected into the survey data. We use these simulations to derive both analytic and machine-learning models that accurately predict the detectability of Milky Way satellites as a function of their distance, size, luminosity, and location on the sky. To demonstrate the utility of this observational selection function, we calculate the luminosity function of Milky Way satellite galaxies, assuming that the known population of satellite galaxies is representative of the underlying distribution. We provide access to our observational selection function to facilitate comparisons with cosmological models of galaxy formation and evolution.
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We combine a series of high-resolution simulations with semi-analytic galaxy formation models to follow the evolution of a system resembling the Milky Way and its satellites. The semi-analytic model is based on that developed for the Millennium Simulation, and successfully reproduces the properties of galaxies on large scales, as well as those of the Milky Way. In this model, we are able to reproduce the luminosity function of the satellites around the Milky Way by preventing cooling in haloes with Vvir < 16.7 km/s (i.e. the atomic hydrogen cooling limit) and including the impact of the reionization of the Universe. The physical properties of our model satellites (e.g. mean metallicities, ages, half-light radii and mass-to-light ratios) are in good agreement with the latest observational measurements. We do not find a strong dependence upon the particular implementation of supernova feedback, but a scheme which is more efficient in galaxies embedded in smaller haloes, i.e. shallower potential wells, gives better agreement with the properties of the ultra-faint satellites. Our model predicts that the brightest satellites are associated with the most massive subhaloes, are accreted later (z $lta$ 1), and have extended star formation histories, with only 1 per cent of their stars made by the end of the reionization. On the other hand, the faintest satellites were accreted early, are dominated by stars with age > 10 Gyr, and a few of them formed most of their stars before the reionization was complete. Objects with luminosities comparable to those of the classical MW satellites are associated with dark matter subhaloes with a peak circular velocity $gta$ 10 km/s, in agreement with the latest constraints.
Recent studies suggest that only three of the twelve brightest satellites of the Milky Way (MW) inhabit dark matter halos with maximum circular velocity, V_max, exceeding 30km/s. This is in apparent contradiction with the LCDM simulations of the Aquarius Project, which suggest that MW-sized halos should have at least 8 subhalos with V_max>30km/s. The absence of luminous satellites in such massive subhalos is thus puzzling and may present a challenge to the LCDM paradigm. We note, however, that the number of massive subhalos depends sensitively on the (poorly-known) virial mass of the Milky Way, and that their scarcity makes estimates of their abundance from a small simulation set like Aquarius uncertain. We use the Millennium Simulation series and the invariance of the scaled subhalo velocity function (i.e., the number of subhalos as a function of u, the ratio of subhalo V_max to host halo virial velocity, V_200) to secure improved estimates of the abundance of rare massive subsystems. In the range 0.1< u<0.5, N_sub(> u) is approximately Poisson-distributed about an average given by <N_sub>=10.2x( u/0.15)^(-3.11). This is slightly lower than in Aquarius halos, but consistent with recent results from the Phoenix Project. The probability that a LCDM halo has 3 or fewer subhalos with V_max above some threshold value, V_th, is then straightforward to compute. It decreases steeply both with decreasing V_th and with increasing halo mass. For V_th=30km/s, ~40% of M_halo=10^12 M_sun halos pass the test; fewer than 5% do so for M_halo>= 2x10^12 M_sun; and the probability effectively vanishes for M_halo>= 3x 10^12 M_sun. Rather than a failure of LCDM, the absence of massive subhalos might simply indicate that the Milky Way is less massive than is commonly thought.
We perform a comprehensive study of Milky Way (MW) satellite galaxies to constrain the fundamental properties of dark matter (DM). This analysis fully incorporates inhomogeneities in the spatial distribution and detectability of MW satellites and marginalizes over uncertainties in the mapping between galaxies and DM halos, the properties of the MW system, and the disruption of subhalos by the MW disk. Our results are consistent with the cold, collisionless DM paradigm and yield the strongest cosmological constraints to date on particle models of warm, interacting, and fuzzy dark matter. At $95%$ confidence, we report limits on (i) the mass of thermal relic warm DM, $m_{rm WDM} > 6.5 mathrm{keV}$ (free-streaming length, $lambda_{rm{fs}} lesssim 10,h^{-1} mathrm{kpc}$), (ii) the velocity-independent DM-proton scattering cross section, $sigma_{0} < 8.8times 10^{-29} mathrm{cm}^{2}$ for a $100 mathrm{MeV}$ DM particle mass (DM-proton coupling, $c_p lesssim (0.3 mathrm{GeV})^{-2}$), and (iii) the mass of fuzzy DM, $m_{phi}> 2.9 times 10^{-21} mathrm{eV}$ (de Broglie wavelength, $lambda_{rm{dB}} lesssim 0.5 mathrm{kpc}$). These constraints are complementary to other observational and laboratory constraints on DM properties.
We present MWFitting, a method to fit the stellar components of the Galaxy by comparing Hess Diagrams (HDs) from TRILEGAL models to real data. We apply MWFitting to photometric data from the first three years of the Dark Energy Survey (DES). After removing regions containing known resolved stellar systems such as globular clusters, dwarf galaxies, nearby galaxies, the Large Magellanic Cloud and the Sagittarius Stream, our main sample spans a total area of $sim$2,300 deg$^2$ distributed across the DES footprint. We further explore a smaller subset ($sim$ 1,300 deg$^2$) that excludes all regions with known stellar streams and stellar overdensities. Validation tests on synthetic data possessing similar properties to the DES data show that the method is able to recover input parameters with a precision better than 3%. Based on the best-fit models, we create simulated stellar catalogues covering the whole DES footprint down to $g = 24$ magnitude. Comparisons of data and simulations provide evidence for a break in the power law index describing the stellar density of the Milky Way (MW) halo. Several previously discovered stellar over-densities are recovered in the residual stellar density map, showing the reliability of MWFitting in determining the Galactic components. Simulations made with the best-fitting parameters are a promising way to predict MW star counts for surveys such as LSST and Euclid.
White dwarf stars are a well-established tool for studying Galactic stellar populations. Two white dwarfs in a tight binary system offer us an additional messenger - gravitational waves - for exploring the Milky Way and its immediate surroundings. Gravitational waves produced by double white dwarf (DWD) binaries can be detected by the future Laser Interferometer Space Antenna (LISA). Numerous and widespread DWDs have the potential to probe shapes, masses and formation histories of the stellar populations in the Galactic neighbourhood. In this work we outline a method for estimating the total stellar mass of Milky Way satellite galaxies based on the number of DWDs detected by LISA. To constrain the mass we perform a Bayesian inference using binary population synthesis models and considering the number of detected DWDs associated with the satellite and the measured distance to the satellite as the only inputs. Using a fiducial binary population synthesis model we find that for large satellites the stellar masses can be recovered to within 1) a factor two if the star formation history is known and 2) an order of magnitude when marginalising over different star formation history models. For smaller satellites we can place upper limits on their stellar mass. Gravitational wave observations can provide mass measurements for large satellites that are comparable, and in some cases more precise, than standard electromagnetic observations.
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