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Aims.We investigate the spectroscopic characteristics of intermediate-luminosity Red Transients (ILRTs), a class of elusive objects with peak luminosity between that of classical novae and standard supernovae. Our goal is to provide a stepping stone in the path to unveiling the physical origin of these events based on the analysis of the collected datasets. Methods.We present the extensive optical and near-infrared (NIR) spectroscopic monitoring of four ILRTs, namely NGC 300 2008OT-1, AT 2019abn, AT 2019ahd and AT 2019udc. First we focus on the evolution of the most prominent spectral features observed in the low-resolution spectra. We then present a more detailed description of the high-resolution spectrum collected for NGC 300 2008OT-1 with the Very Large Telescope equipped with UVES. Finally, we describe our analysis of late-time spectra of NGC 300 2008OT-1 and AT 2019ahd through comparisons with both synthetic and observed spectra. Results.Balmer and Ca lines dominate the optical spectra, revealing the presence of slowly moving circumstellar medium (CSM) around the objects. The line luminosity of Hα, Hβ, and Ca IINIR triplet presents a double peaked evolution with time, possibly indicative of interaction between fast ejecta and the slow CSM. The high-resolution spectrum of NGC 300 2008OT-1 reveals a complex circumstellar environment, with the transient being surrounded by a slow (∼30 km s⁻¹) progenitor wind. At late epochs, optical spectra of NGC 300 2008OT-1 and AT 2019ahd show broad (∼2500 km s⁻¹) emission features at ∼6170 Å and ∼7000 Å which are unprecedented for ILRTs. We find that these lines originate most likely from the blending of several narrow lines, possibly of iron-peak elements. 

The authors consider a bistatic configuration with a stationary transmitter transmitting unknown waveforms of opportunity and a single moving receiver and present a deep learning (DL) framework for passive synthetic aperture radar (SAR) imaging. They approach DL from an optimisation based perspective and formulate image reconstruction as a machine learning task. By unfolding the iterations of a proximal gradient descent algorithm, they construct a deep recurrent neural network (RNN) that is parameterised by the transmitted waveforms. They cascade the RNN structure with a decoder stage to form a recurrent auto-encoder architecture. They then use backpropagation to learn transmitted waveforms by training the network in an unsupervised manner using SAR measurements. The highly non-convex problem of backpropagation is guided to a feasible solution over the parameter space by initialising the network with the known components of the SAR forward model. Moreover, prior information regarding the waveform structure is incorporated during initialisation and backpropagation. They demonstrate the effectiveness of the DL-based approach through numerical simulations that show focused, high contrast imagery using a single receiver antenna at realistic signal-to-noise-ratio levels. 

In this paper, we discuss the outcomes of the follow-up campaign of SN 2018ijp, discovered as part of the Zwicky Transient Facility survey for optical transients. Its first spectrum shows similarities to broad-lined Type Ic supernovae around maximum light, whereas later spectra display strong signatures of interaction between rapidly expanding ejecta and a dense H-rich circumstellar medium, coinciding with a second peak in the photometric evolution of the transient. This evolution, along with the results of modeling of the first light-curve peak, suggests a scenario where a stripped star exploded within a dense circumstellar medium. The two main phases in the evolution of the transient could be interpreted as a first phase dominated by radioactive decays, and a later interaction-dominated phase where the ejecta collide with a pre-existing shell. We therefore discuss SN 2018jp within the context of a massive star depleted of its outer layers exploding within a dense H-rich circumstellar medium.