We present the MUSE Hubble Ultra Deep Survey, a mosaic of nine MUSE fields covering 90% of the entire HUDF region with a 10-h deep exposure time, plus a deeper 31-h exposure in a single 1.15 arcmin 2 field. The improved observing strategy and advanced data reduction results in datacubes with sub-arcsecond spatial resolution (0 . 65 at 7000 Å) and accurate astrometry (0 . 07 rms). We compare the broadband photometric properties of the datacubes to HST photometry, finding a good agreement in zeropoint up to m AB = 28 but with an increasing scatter for faint objects. We have investigated the noise properties and developed an empirical way to account for the impact of the correlation introduced by the 3D drizzle interpolation. The achieved 3σ emission line detection limit for a point source is 1.5 and 3.1 × 10 −19 erg s −1 cm −2 for the single ultra-deep datacube and the mosaic, respectively. We extracted 6288 sources using an optimal extraction scheme that takes the published HST source locations as prior. In parallel, we performed a blind search of emission line galaxies using an original method based on advanced test statistics and filter matching. The blind search results in 1251 emission line galaxy candidates in the mosaic and 306 in the ultradeep datacube, including 72 sources without HST counterparts (m AB > 31). In addition 88 sources missed in the HST catalog but with clear HST counterparts were identified. This data set is the deepest spectroscopic survey ever performed. In just over 100 h of integration time, it provides nearly an order of magnitude more spectroscopic redshifts compared to the data that has been accumulated on the UDF over the past decade. The depth and high quality of these datacubes enables new and detailed studies of the physical properties of the galaxy population and their environments over a large redshift range.

Context. Recent years have been seeing huge developments of radio telescopes and a tremendous increase in their capabilities (sensitivity, angular and spectral resolution, field of view, etc.). Such systems make designing more sophisticated techniques mandatory not only for transporting, storing, and processing this new generation of radio interferometric data, but also for restoring the astrophysical information contained in such data. Aims. In this paper we present a new radio deconvolution algorithm named MORESANE and its application to fully realistic simulated data of MeerKAT, one of the SKA precursors. This method has been designed for the difficult case of restoring diffuse astronomical sources that are faint in brightness, complex in morphology, and possibly buried in the dirty beam's side lobes of bright radio sources in the field. Methods. MORESANE is a greedy algorithm that combines complementary types of sparse recovery methods in order to reconstruct the most appropriate sky model from observed radio visibilities. A synthesis approach is used for reconstructing images, in which the synthesis atoms representing the unknown sources are learned using analysis priors. We applied this new deconvolution method to fully realistic simulations of the radio observations of a galaxy cluster and of an HII region in M 31. Results. We show that MORESANE is able to efficiently reconstruct images composed of a wide variety of sources (compact pointlike objects, extended tailed radio galaxies, low-surface brightness emission) from radio interferometric data. Comparisons with the state of the art algorithms indicate that MORESANE provides competitive results in terms of both the total flux/surface brightness conservation and fidelity of the reconstructed model. MORESANE seems particularly well suited to recovering diffuse and extended sources, as well as bright and compact radio sources known to be hosted in galaxy clusters.

We report the discovery of diffuse extended Lyα emission from redshift 3.1 to 4.5, tracing cosmic web filaments on scales of 2.5−4 cMpc. These structures have been observed in overdensities of Lyα emitters in the MUSE Extremely Deep Field, a 140 h deep MUSE observation located in the Hubble Ultra-Deep Field. Among the 22 overdense regions identified, five are likely to harbor very extended Lyα emission at high significance with an average surface brightness of 5 × 10−20 erg s−1 cm−2 arcsec−2. Remarkably, 70% of the total Lyα luminosity from these filaments comes from beyond the circumgalactic medium of any identified Lyα emitter. Fluorescent Lyα emission powered by the cosmic UV background can only account for less than 34% of this emission at z ≈ 3 and for not more than 10% at higher redshift. We find that the bulk of this diffuse emission can be reproduced by the unresolved Lyα emission of a large population of ultra low-luminosity Lyα emitters (< 1040 erg s−1), provided that the faint end of the Lyα luminosity function is steep (α ⪅ −1.8), it extends down to luminosities lower than 1038 − 1037 erg s−1, and the clustering of these Lyα emitters is significant (filling factor < 1/6). If these Lyα emitters are powered by star formation, then this implies their luminosity function needs to extend down to star formation rates < 10−4 M⊙ yr−1. These observations provide the first detection of the cosmic web in Lyα emission in typical filamentary environments and the first observational clue indicating the existence of a large population of ultra low-luminosity Lyα emitters at high redshift.

Aims. We search for photometric variability in chemically peculiar A type stars in the northern hemisphere. Methods. High-speed photometric observations of Ap and Am star candidates have been carried out from ARIES (Manora Peak, Nainital) using a three-channel fast photometer attached to the ARIES 104-cm Sampurnanand telescope.Results. This paper presents three new variables: HD 113878, HD 118660 and HD 207561. During the time span of the survey (1999 December to 2004 January) pulsations of the δ Sct type were also found for the two evolved Am stars HD 102480 and HD 98851, as reported in Joshi et al. (2002Joshi et al. ( , 2003. Additionally, we present 140 null results of the survey for this time span. Conclusions. The star HD 113878 pulsates with a period of 2.31 h, which is typical of δ Sct stars. HD 118660 exhibits multi-periodic variability with a prominent period of nearly 1 h. These periods need to be investigated and make HD 118660 a particularly interesting target for further observations. For HD 207561, a star classified as Am, a probable pulsation with a period of 6 min was found in the light curves obtained on two consecutive nights. Both HD 102480 and HD 98851 exhibit unusual alternating high and low amplitude maxima, with a period ratio of 2:1. The analysis of the null results confirms the photometric quality of the Nainital site.

In the lead-up to the Square Kilometre Array (SKA) project, several next-generation radio telescopes and upgrades are already being built around the world. These include APERTIF (The Netherlands), ASKAP (Australia), e-MERLIN

When the noise affecting time series is colored with unknown statistics, a difficulty for sinusoid detection is to control the true significance level of the test outcome. This paper investigates the possibility of using training data sets of the noise to improve this control. Specifically, we analyze the performances of various detectors applied to periodograms standardized using training data sets. Emphasis is put on sparse detection in the Fourier domain and on the limitation posed by the necessarily finite size of the training sets available in practice. We study the resulting false alarm and detection rates and show that standardization leads in some cases to powerful constant false alarm rate tests. The study is both analytical and numerical. Although analytical results are derived in an asymptotic regime, numerical results show that theory accurately describes the tests' behaviour for moderately large sample sizes. Throughout the paper, an application of the considered periodogram standardization is presented for exoplanet detection in radial velocity data.

This paper addresses the problem of blind and fully constrained unmixing of hyperspectral images. Unmixing is performed without the use of any dictionary, and assumes that the number of constituent materials in the scene and their spectral signatures are unknown. The estimated abundances satisfy the desired sum-to-one and nonnegativity constraints. Two models with increasing complexity are developed to achieve this challenging task, depending on how noise interacts with hyperspectral data. The first one leads to a convex optimization problem, and is solved with the Alternating Direction Method of Multipliers. The second one accounts for signal-dependent noise, and is addressed with a Reweighted Least Squares algorithm. Experiments on synthetic and real data demonstrate the effectiveness of our approach.

Context. One of the major science cases of the MUSE (Multi Unit Spectroscopic Explorer) integral field spectrograph is the detection of Lyman-alpha emitters at high redshifts. The on-going and planned deep fields observations will allow for one large sample of these sources. An efficient tool to perform blind detection of faint emitters in MUSE datacubes is a prerequisite of such an endeavor. Aims. Several line detection algorithms exist but their performance during the deepest MUSE exposures is hard to quantify, in particular with respect to their actual false detection rate, or purity. The aim of this work is to design and validate an algorithm that efficiently detects faint spatial-spectral emission signatures, while allowing for a stable false detection rate over the data cube and providing in the same time an automated and reliable estimation of the purity. Methods. The algorithm implements i) a nuisance removal part based on a continuum subtraction combining a discrete cosine transform and an iterative principal component analysis, ii) a detection part based on the local maxima of generalized likelihood ratio test statistics obtained for a set of spatial-spectral profiles of emission line emitters and iii) a purity estimation part, where the proportion of true emission lines is estimated from the data itself: the distribution of the local maxima in the "noise only" configuration is estimated from that of the local minima. Results. Results on simulated data cubes providing ground truth show that the method reaches its aims in terms of purity and completeness. When applied to the deep 30-hour exposure MUSE datacube in the Hubble Ultra Deep Field, the algorithms allows for the confirmed detection of 133 intermediate redshifts galaxies and 248 Lyα emitters, including 86 sources with no HST (Hubble Space Telescope) counterpart. Conclusions. The algorithm fulfills its aims in terms of detection power and reliability. It is consequently implemented as a Python package whose code and documentation are available on GitHub and readthedocs.