The LIGO/Virgo detections of gravitational waves from merging black holes of ≃ 30 solar mass suggest progenitor stars of low metallicity (Z/Z⊙ ≲ 0.3). In this talk I will provide constrains on where the progenitors of GW150914 and GW170104 may have formed, based on advanced models of galaxy formation and evolution combined with binary population synthesis models. First I will combine estimates of galaxy properties (star-forming gas metallicity, star formation rate and merger rate) across cosmic time to predict the low redshift BBH merger rate as a function of present day host galaxy mass, formation redshift of the progenitor system and different progenitor metallicities. I will show that the signal is dominated by binaries formed at the peak of star formation in massive galaxies with and binaries formed recently in dwarf galaxies. Then, I will present what very high resolution hydrodynamic simulations of different galaxy types can learn us about their black hole populations.

The upcoming radio interferometer Square Kilometre Array is expected to directly detect the redshifted 21-cm signal from the Cosmic Dawn for the first time. In this era temperature fluctuations from X-ray heating of the neutral intergalactic medium can impact this signal dramatically. Previously, in Ross et al. (2017), we presented the first large-volume, 244 h-1 Mpc=349 Mpc a side, fully numerical radiative transfer simulations of X-ray heating. This work is a follow-up where we now also consider QSO-like sources in addition to high mass X-ray binaries. Images of the two cases are clearly distinguishable at SKA1-LOW resolution and have RMS fluctuations above the expected noise. The inclusion of QSOs leads to a dramatic increase in non-Gaussianity of the signal, as measured by the skewness and kurtosis of the 21-cm signal. We conclude that this increased non-Gaussianity is a promising signature of early QSOs.

We present the results of prompt optical follow-up of the electromagnetic counterpart of GW170817 by the Transient Optical Robotic Observatory of the South Collaboration (TOROS). We detected highly significant dimming in the light curves of the counterpart over the course of only 80 minutes of observations obtained ~35 hr after the trigger with the T80-South telescope. A second epoch of observations, obtained ~59 hr after the event with the EABA 1.5m telescope, confirms the fast fading nature of the transient. The observed colors of the counterpart suggest that this event was a “blue kilonova” relatively free of lanthanides.

21-cm cosmology is a powerful new probe of the intergalactic medium at redshifts 20 ≳ z ≳ 6 corresponding to the Cosmic Dawn and Epoch of Reionization. Current observations of the highly-redshifted 21-cm transition are limited by the dynamic range they can achieve against foreground sources of low-frequency (<200 MHz) of radio emission. We used the Owens Valley Radio Observatory Long Wavelength Array (OVRO-LWA) to generate a series of new modern high-fidelity sky maps that capture emission on angular scales ranging from tens of degrees to ∼15 arcmin, and frequencies between 36 and 73 MHz. These sky maps were generated from the application of Tikhonov-regularized m-mode analysis imaging, which is a new interferometric imaging technique that is uniquely suited for low-frequency, wide-field, drift-scanning interferometers.

With current efforts inching closer to detecting the 21-cm signal from the Epoch of Reionization (EoR), proper preparation will require publicly available simulated models of the various forms the signal could take. In this work we present a database of such models, available at 21ssd.obspm.fr. The models are created with a fully-coupled radiative hydrodynamic simulation (LICORICE), and are created at high resolution (10243). We also begin to analyse and explore the possible 21-cm EoR signals (with Power Spectra and Pixel Distribution Functions), and study the effects of thermal noise on our ability to recover the signal out to high redshifts. Finally, we begin to explore the concepts of ‘distance’ between different models, which represents a crucial step towards optimising parameter space sampling, training neural networks, and finally extracting parameter values from observations.

Investigating the distant extragalactic Universe requires a subtraction of the Galactic foreground. One of the major difficulties deriving the fine structure of the galactic foreground is the embedded foreground and background point sources appearing in the given fields. It is especially so in the infrared. We report our study subtracting point sources from Herschel images with Kriging, an interpolation method where the interpolated values are modelled by a Gaussian process governed by prior covariances. Using the Kriging method on Herschel multi-wavelength observations the structure of the Galactic foreground can be studied with much higher resolution than previously, leading to a better foreground subtraction at the end.

A short status update on the LOFAR Epoch of Reionization (EoR) Key Science Project (KSP) is given, regarding data acquisition, data processing and analysis, and current power-spectrum limits on the redshifted 21-cm signal of neutral hydrogen at redshifts z = 8 − 10. With caution, we present a preliminary astrophysical analysis of ∼60 hr of processed LOFAR data and their resulting power spectrum, showing that potentially already interesting limits on X-ray heating during the Cosmic Dawn can already be gained. This is by no means the final analysis of this sub-set of data, but illustrates the future potential when all nearly 3000 hr of data in hand on two EoR windows will have been processed.

We review an improved statistical model of extra-galactic point-source foregrounds first introduced in Murray et al. (2017), in the context of the Epoch of Reionization. This model extends the instrumentally-convolved foreground covariance used in inverse-covariance foreground mitigation schemes, by considering the cosmological clustering of the sources. In this short work, we show that over scales of k ∼ (0.6, 40.)hMpc−1, ignoring source clustering is a valid approximation. This is in contrast to Murray et al. (2017), who found a possibility of false detection if the clustering was ignored. The dominant cause for this change is the introduction of a Galactic synchrotron component which shadows the clustering of sources.

Foreground contamination is one of the most important limiting factors in detecting the neutral hydrogen in the epoch of reionisation. These foregrounds can be roughly split into galactic and extragalactic foregrounds. In these proceedings we highlight information that can be gleaned from multi-wavelength extragalactic surveys in order to overcome this issue. We discuss how clustering information from the lower-redshift, foreground galaxies, can be used as additional information in accounting for the noise associated with the foregrounds. We then go on to highlight the expected contribution of future optical and near-infrared surveys for detecting the galaxies responsible for ionising the Universe. We suggest that these galaxies can also be used to reduce the systematics in the 21-cm epoch of reionisation signal through cross-correlations if enough common area is surveyed.

AGILE is a space mission of the Italian Space Agency dedicated to γ-ray astrophysics, launched in 2007. AGILE performed dedicated real-time searches for possible γ-ray counterparts of gravitational wave (GW) events detected by the LIGO-Virgo scientific Collaboration (LVC) during the O2 observation run. We present a review of AGILE observations of GW events, starting with the first, GW150914, which was a test case for future searches. We focus here on the main characteristics of the observations of the most important GW events detected in 2017, i.e. GW170104 and GW170817. In particular, for the former event we published γ-ray upper limits (ULs) in the 50 MeV – 10 GeV energy band together with a detailed analysis of a candidate precursor event in the Mini-Calorimeter data. As for GW170817, we published a set of constraining γ-ray ULs obtained for integrations preceding and following the event time. These results allow us to establish important constraints on the γ-ray emission from a possible magnetar-like remnant in the first ~1000 s following T0. AGILE is a major player in the search of electromagnetic counterparts of GW events, and its enhanced detection capabilities in hard X-ray/MeV/GeV ranges will play a crucial role in the future O3 observing run.

In the last decade, it has become clear that the dust-enshrouded star formation contributes significantly to early galaxy evolution. Detection of dust is therefore essential in determining the properties of galaxies in the high-redshift universe. This requires observations at the (sub-)millimeter wavelengths. Unfortunately, sensitivity and background confusion of single dish observations on the one hand, and mapping efficiency of interferometers on the other hand, pose unique challenges to observers. One promising route to overcome these difficulties is intensity mapping of fluctuations which exploits the confusion-limited regime and measures the collective light emission from all sources, including unresolved faint galaxies. We discuss in this contribution how 2D and 3D intensity mapping can measure the dusty star formation at high redshift, through the Cosmic Infrared Background (2D) and [CII] fine structure transition (3D) anisotropies.

The cross power spectrum of the 21 cm signal and Lyman-α emitters (LAEs) is a probe of the Epoch of Reionization. Astrophysical foregrounds do not correlate with the LAE distribution, though the foregrounds contribute to the error. To study the impact of foregrounds on the measurement, we assume realistic observation by the Murchison Widefield Array using a catalogue of radio galaxies, a LAE survey by the Subaru Hyper Supreme-Cam and the redshift of LAEs is determined by the Prime Focus Spectrograph. The HI distribution is estimated from a radiative transfer simulation with models based on results of radiation hydrodynamics simulation. Using these models, we found that the error of cross power spectrum is dominated by foreground terms. Furthermore, we estimate the effects of foreground removal, and find 99% of the foreground removal is required to detect the 21 cm-LAE signal at k ∼ 0.4 h Mpc−1.

Next-generation 21cm observations will enable imaging of reionization on very large scales. These images will contain more astrophysical and cosmological information than the power spectrum, and hence providing an alternative way to constrain the contribution of different reionizing sources populations to cosmic reionization. Using Convolutional Neural Networks, we present a simple network architecture that is sufficient to discriminate between Galaxy-dominated versus AGN-dominated models, even in the presence of simulated noise from different experiments such as the HERA and SKA.

The author - with his collaborators - already in years 1995-96 have shown - purely from the analyses of the observations - that the gamma-ray bursts (GRBs) can be till redshift 20. Since that time several other statistical studies of the spatial distribution of GRBs were provided. Remarkable conclusions concerning the star-formation rate and the validity of the cosmological principle were obtained about the regions of the cosmic dawn. In this contribution these efforts are surveyed.

The 21cm signal at epoch of reionization (EoR) should be observed within next decade. We expect that cosmic 21cm signal at the EoR provides us both cosmological and astrophysical information. In order to extract fruitful information from observation data, we need to develop inversion method. For such a method, we introduce artificial neural network (ANN) which is one of the machine learning techniques. We apply the ANN to inversion problem to constrain astrophysical parameters from 21cm power spectrum. We train the architecture of the neural network with 70 training datasets and apply it to 54 test datasets with different value of parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameter sets at a given redshift and also find that the accuracy of reconstruction is improved by increasing the number of given redshifts. We conclude that the ANN is viable inversion method whose main strength is that they require a sparse extrapolation of the parameter space and thus should be usable with full simulation.