Context. We introduce the Dwarf Galaxy Survey with Amateur Telescopes (DGSAT) project and report the discovery of eleven low surface brightness (LSB) galaxies in the fields of the nearby galaxies NGC 2683, NGC 3628, NGC 4594 (M 104), NGC 4631, NGC 5457 (M 101), and NGC 7814. Aims. The DGSAT project aims to use the potential of small-sized telescopes to probe LSB features around large galaxies and to increase the sample size of the dwarf satellite galaxies in the Local Volume. Methods. Using long exposure images, fields of the target spiral galaxies are explored for extended LSB objects. After identifying dwarf galaxy candidates, their observed properties are extracted by fitting models to their light profiles. Results. We find three, one, three, one, one, and two new LSB galaxies in the fields of NGC 2683, 3628, 4594, 4631, 5457, and 7814, respectively. In addition to the newly found galaxies, we analyse the structural properties of nine already known galaxies. All of these 20 dwarf galaxy candidates have effective surface brightnesses in the range 25.3 < ∼ µ e < ∼ 28.8 mag arcsec −2 and are fit with Sersic profiles with indices n < ∼ 1. Assuming that they are in the vicinity of the above mentioned massive galaxies, their r-band absolute magnitudes, their effective radii, and their luminosities are in the ranges −15.6 < ∼ M r < ∼ −7.8, 160 pc < ∼ R e < ∼ 4.1 kpc, and 0.1 × 10 6 < ∼ L L r < ∼ 127 × 10 6 , respectively. To determine whether these LSB galaxies are indeed satellites of the above mentioned massive galaxies, their distances need to be determined via further observations. Conclusions. Using small telescopes, we are readily able to detect LSB galaxies with similar properties to the known dwarf galaxies of the Local Group.

We present a method to test the isotropy of the magnitude-redshift relation of Type Ia Supernovae (SNe Ia) and single out the most discrepant direction (in terms of the signal-to-noise ratio) with respect to the all-sky data. Our technique accounts for possible directional variations of the corrections for SNe Ia and yields all-sky maps of the best-fit cosmological parameters with arbitrary angular resolution. To show its potential, we apply our method to the recent Union2.1 compilation, building maps with three different angular resolutions. We use a Monte Carlo method to estimate the statistical significance with which we could reject the null hypothesis that the magnitude-redshift relation is isotropic based on the properties of the observed most discrepant directions. We find that, based on pure signal-tonoise arguments, the null hypothesis cannot be rejected at any meaningful confidence level. However, if we also consider that the strongest deviations in the Union2.1 sample closely align with the dipole temperature anisotropy of the cosmic microwave background, we find that the null hypothesis should be rejected at the 95 − 99 per cent confidence level, slightly depending on the angular resolution of the study. If this result is not due to a statistical fluke, it might either indicate that the SN data have not been cleaned from all possible systematics or even point towards new physics. We finally discuss future perspectives in the field for achieving larger and more uniform data sets that will vastly improve the quality of the results and optimally exploit our method.

The majority of monogenic disorders cause craniofacial abnormalities with characteristic facial morphology. These disorders can be diagnosed more e ciently with the support of computer-aided nextgeneration phenotyping tools, such as DeepGestalt. These tools have learned to associate facial phenotypes with the underlying syndrome through training on thousands of patient photographs. However, this "supervised" approach means that diagnoses are only possible if they were part of the training set. To improve recognition of ultra-rare diseases, we created GestaltMatcher, which uses a deep convolutional neural network based on the DeepGestalt framework. We used photographs of 21,836 patients with 1,362 rare disorders to de ne a "Clinical Face Phenotype Space". Distance between cases in the phenotype space de nes syndromic similarity, allowing test patients to be matched to a molecular diagnosis even when the disorder was not included in the training set. Similarities among patients with previously unknown disease genes can also be detected. Therefore, in concert with mutation data, GestaltMatcher could accelerate the clinical diagnosis of patients with ultra-rare disorders and facial dysmorphism.