List of all the talks in the archive, sorted by date.
The dust component of active galactic nuclei (AGN) produces a broad infrared spectral energy distribution (SED), whose power and shape depends on the fraction of the source absorbed, and the geometry of the absorber respectively. This emitting region is expected to be concentrated within the inner ∼5 pc of the AGN which makes almost impossible to image it with the current instruments. The study of the infrared SED by comparison between infrared AGN spectra and predicted models is one of the few ways to infer the properties of the AGN dust. We explore a set of six dusty models of AGN with available SEDs, namely Fritz et al. (2006), Nenkova et al. (2008B), Hoenig & Kishimoto (2010), Siebenmorgen et al. (2015), Stalevski et al. (2016), and Hoenig & Kishimoto (2017). They cover a wide range of morphologies, dust distributions, and compositions.
We explore the discrimination among models and parameter restriction using synthetic spectra (Gonzalez-Martin et al. 2019A), and perform spectral fitting of a sample of 110 AGN with Spitzer/IRS drawn from the Swift/BAT survey (Gonzalez-Martin et al. 2019B). Our conclusion is that most of these models can be discriminated using only mid-infrared spectroscopy as long as the host galaxy contribution is less than 50%. The best model describing the sample is the clumpy disk-wind model by Hoenig & Kishimoto (2017). However, large residuals are shown irrespective of the model used, indicating that AGN dust is more complex than models. We found that the parameter space covered by models is not completely adequate. This talk will give tips for observers and modelers to actually answer the question: how is the dust arrange in AGN? This question will be one of the main subjects of future research using JWST in the AGN field.
In this talk, I present a new technique that explores the hypothesis that
the structure producing the continuum emission at mid-IR and the reflection
component at X-ray are the same. If this is the case, they can be used
together to better constrain the physical parameters of the torus. Our
technique consists on a simultaneous fitting of Spitzer and NuSTAR spectra
using mid-IR and X-ray models available. During this talk, I will also show
the first results obtained when applying our technique to the nearby type-2
active nucleus IC 5063. Finally, I will talk about the work that we are
currently developing using this technique.
Until the advent in the late 1990’s of sensitive submillimetre arrays such as SCUBA, it was generally thought that the main sources for the interstellar dust found in galaxies were the dusty outflows from evolved AGB stars and M supergiants, although a dust contribution from supernovae had long been predicted on theoretical grounds. The detection at submillimetre wavelengths of very large dust masses in some high redshift galaxies emitting less than a billion years after the Big Bang led to a more serious consideration of core-collapse supernovae (CCSNe) from massive stars as major dust contributors. KAO and Spitzer mid-infrared observations confirmed that CCSN ejecta could form dust but it was not until the Herschel mission and subsequent ALMA observations that direct evidence has been obtained for the presence of significantly large masses of cold dust in young CCSN remnants. As well as using infrared spectral energy distributions to measure the amounts of dust forming in CCSN ejecta, dust masses can also be quantified from the analysis of red-blue asymmetries in their late-time optical emission line profiles. I will describe current results from these methods for estimating ejecta dust masses, and their implications.
The existence of apparently isolated massive stars has been recognized for some time, and various explanations have been proposed to explain these ranging from isolated star formation to variouscluster ejection mechanisms. In this talk I will present recent results from Gaia and Hubble on stellar dynamics within the Tarantula Nebula/30 Doradus region of the Large Magellanic Cloud. I will discuss how these complementary datasets have improved our knowledge of this nearby mini-starburst. The first results indicate the existence of a few stars in the region with masses ~100 solar masses that have been ejected from the central dense cluster R136. Ejection velocities appear torange from a few 10s of km/s to ~100 km/s. Given the extreme youth of R136 it is therefore likely that the mechanism of ejection was via the dynamical interaction channel rather than the binary supernova ejection scenario.
Nature Astronomy, launched in January 2017, is a new research journal published by Springer Nature. Sitting alongside our sister journal Nature, we aim to publish high impact research in the fields of astronomy, astrophysics and planetary science. In this talk I will cover the motivation and scope of the journal, the types of manuscripts we publish, the editorial process and what we look for in papers. I will also cover common pitfalls of writing and submitting papers and I will share hints and tips on how to maximize the impact of your paper, from writing an engaging but informative title and a properly contextualized but concise abstract, to structuring your paper in a way that your results are communicated succinctly.
We propose to use convolutional neural networks to detect contaminants in astronomical images. Once trained, our networks are able to detect various contaminants such as cosmic rays, hot and bad pixels, persistence effects, satellite or plane trails, residual fringe patterns, nebulosity, saturated pixels, diffraction spikes and tracking errors in images, encompassing a broad range of ambient conditions (seeing), PSF sampling, detectors, optics and stellar density. MaxiMask is performing semantic segmentation: it can output a probability map for each contaminant, assigning to each pixel its probability to belong to the given contaminant class, except for tracking errors where another convolutional neural network can assign the probability that the entire focal plane is affected. Training and testing data have been gathered from real data originating from various modern CCD and near-infrared cameras or simulated data. We show that MaxiMask achieves good performance on test data and propose a prior modification technique based on Bayesian statistics to optimize its behaviour to any expected class proportion in real data.
- Arch Filament Systems and their evolution through the layers of the solar atmosphereDr. Sergio Javier Gonzalez ManriqueTuesday October 15, 2019 - 12:30 (Aula)
- TBDDr. Nancy LevensonWednesday October 16, 2019 - 10:30 (Aula)