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We present X-ray and radio observations of what may be the closest type Iax supernova (SN) to date, SN 2014dt (d=12.3-19.3 Mpc) and provide tight constraints on the radio and X-ray emission. We infer a specific radio luminosity of < (1.0-2.4)E25 erg/s/Hz at a frequency of 7.5 GHz and a X-ray luminosity < 1.4E38 erg/s (0.3-10 keV) at ~38-48 days post-explosion. We interpret these limits in the context of Inverse Compton (IC) emission and synchrotron emission from a population of electrons accelerated at the forward shock of the explosion in a power-law distribution $N_e(\gamma_e)\propto \gamma_e^{-p}$ with p=3. Our analysis constrains the progenitor system mass-loss rate to be smaller than 5E-6 solar masses per year at distances where r <= 1E16 cm for an assumed wind velocity v=100 km/s, and a fraction of post-shock energy into magnetic fields and relativistic electrons of epsilon_B=0.01 and epsilon_e=0.1, respectively. This result rules out some of the parameter space of symbiotic giant star companions, and it is consistent with the low mass-loss rates expected from He-star companions. Our calculations also show that the improved sensitivity of the next generation Very Large Array (ngVLA) is needed to probe the very low-density media characteristic of He stars that are the leading model for binary stellar companions of white dwarfs giving origin to type Iax SNe., Comment: Submitted to MNRAS, 9 pages and 7 figures

Deep learning is a subset of a broader family of machine learning methods based on learning data representations. These models are inspired by human biological nervous systems, even if there are various differences pertaining to the structural and functional properties of biological brains. The elementary constituents of deep learning models are neurons, which can be considered as functions that receive inputs and produce an output that is a weighted sum of the inputs fed through an activation function. Several models of neurons were proposed in the course of the years that are all based on learnable parameters called weights. In this paper we present a new type of artificial neuron, the double-weight neuron,characterized by additional learnable weights that lead to a more complex and accurate system. We tested a feed-forward and convolutional neural network consisting of double-weight neurons on the MNIST dataset, and we tested a convolution network on the CIFAR-10 dataset. For MNIST we find a $\approx 4\%$ and $\approx 1\%$ improved classification accuracy, respectively, when compared to a standard feed-forward and convolutional neural network built with the same sets of hyperparameters. For CIFAR-10 we find a $\approx 12\%$ improved classification accuracy. We thus conclude that this novel artificial neuron can be considered as a valuable alternative to common ones.

The Large Synoptic Survey Telescope (LSST) will open a discovery frontier for faint and fast transients with its ability to detect variable flux components down to $\sim$24.5 mag in a $\sim$30 second exposure. Spectroscopic follow-up of such phenomena - which are necessary for understanding the physics of stellar explosions - can require a rapid response and several hours with a 8-10m telescope, making it both expensive and difficult to acquire. The future Extremely Large Telescopes (ELTs) would be able to provide not only spectroscopy but capabilities such as spectropolarimetry and high-resolution diffraction-limited imaging that would contribute to future advances in our physical understanding of stellar explosions. In this white paper we focus on several specific scientific impacts in the field of explosive transient astrophysics that will be generated by the combination of LSST's discovery abilities and ELTs' follow-up capacities. First, we map the uncharted frontier of discovery phase-space in terms of intrinsic luminosity and timescales for explosive transients, where we expect the unexpected. We then focus on six areas with open science questions for known transients: the progenitors of thermonuclear supernovae (SNe), mass loss prior to core collapse, asymmetry in stellar explosions, light echoes, high-$z$ transients, and strongly lensed SNe. We conclude with a brief discussion of the practical aspects of ELT & LSST synergy., Comment: 8 pages, 4 figures, submitted to Astro2020 call for Science White Papers

The field of time-domain astrophysics has entered the era of Multi-messenger Astronomy (MMA). One key science goal for the next decade (and beyond) will be to characterize gravitational wave (GW) and neutrino sources using the next generation of Extremely Large Telescopes (ELTs). These studies will have a broad impact across astrophysics, informing our knowledge of the production and enrichment history of the heaviest chemical elements, constrain the dense matter equation of state, provide independent constraints on cosmology, increase our understanding of particle acceleration in shocks and jets, and study the lives of black holes in the universe. Future GW detectors will greatly improve their sensitivity during the coming decade, as will near-infrared telescopes capable of independently finding kilonovae from neutron star mergers. However, the electromagnetic counterparts to high-frequency (LIGO/Virgo band) GW sources will be distant and faint and thus demand ELT capabilities for characterization. ELTs will be important and necessary contributors to an advanced and complete multi-messenger network., Comment: White paper submitted to the Astro2020 Decadal Survey

The degradation of spatial resolution in star-forming regions observed at large distances ($d\gtrsim1$ kpc) with Herschel,can lead to estimates of the physical parameters of the detected compact sources (clumps) which do not necessarily mirror the properties of the original population of cores. This paper aims at quantifying the bias introduced in the estimation of these parameters by the distance effect. To do so, we consider Herschel maps of nearby star-forming regions taken from the Herschel-Gould-Belt survey, and simulate the effect of increased distance to understand what amount of information is lost when a distant star-forming region is observed with Herschel resolution. In the maps displaced to different distances we extract compact sources, and we derive their physical parameters as if they were original Hi-GAL maps of the extracted source samples. In this way, we are able to discuss how the main physical properties change with distance. In particular, we discuss the ability of clumps to form massive stars: we estimate the fraction of distant sources that are classified as high-mass stars-forming objects due to their position in the mass vs radius diagram, that are only "false positives". We give also a threshold for high-mass star-formation $M>1282 \ \left(\frac{r}{[\mathrm{pc}]}\right)^{1.42} M_{\odot}$. In conclusion, this paper provides the astronomer dealing with Herschel maps of distant star-forming regions with a set of prescriptions to partially recover the character of the core population in unresolved clumps., Comment: 32 pages, 42 figures, accepted for publication in MNRAS