Recent Talks

David Calle, youtuber, fundador del portal educativo unicoos nos contará como surgió la idea y los retos a los que tuvo que enfrentarse. Y cómo el uso del vídeo está modificando la forma en la que el profesorado y alumnado interactúa en el proceso de enseñanza-aprendizaje (flipped classroom).

Esta charla servirá, también, como punto de partida de una futura colaboración conjunta para la creación de contenidos divulgativos y educativos (enseñanza reglada) que comenzaría con la selección de un equipo de astrofísicas/os, que tengan alguna relación con el IAC-ULL con el objetivo de desarrollar vídeos soporte para:

• las optativas de Astrofísica del Grado de Físicas
• las asignaturas del Máster de Astrofísica.

Since first light in 2004 the 2.0m Liverpool Telescope has been the world’s largest
fully robotic telescope. It specialises in time domain astrophysics and has
a dedicated instrument suite giving imagining, spectroscopic and
polarimetric capabilities. In this seminar I will describe how the robotic
operation of the telescope works and give examples of the science
accomplished in areas such as gamma ray burst follow-up and supernova
classification. I will also present our plans to develop a new 4.0m robotic telescope
in collaboration with colleagues at IAC which will deliver faster reaction and
increased sensitivity.

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.

The Faculty of Mathematics, Physics and Informatics of Comenius University in Bratislava, Slovakia (FMPI CU) operates its own Astronomical and Geophysical Observatory in Modra, Slovakia (AGO). AGO consists of several optical systems, from which some were developed by FMPI. One of the mentioned systems is a 70-cm Newton telescope (AGO70) with primary focus on the space debris research, space surveillance and tracking (SST) to support the European attempts for autonomous SST operations.

AGO70 has several parallel scientific programs with primary focus on space debris characterization. In the last two years we created our own space debris light curve catalogue which is available for scientific community. The light curve catalogue is further used for the BVRI photometry where the shape of the phase-diagram and the synodic rotation period define the strategy for the data acquisition and processing once acquired with multi-band filters. Astrometric measurements are used for three goals. To validate and calibrate the AGO70 system’s data, to support the cataloguing efforts which requires orbit determination and improvement, and to improve the tracking efficiency of Satellite Laser Ranging stations.

Part of the improvement of AGO70 system is also hardware and software modifications. There have been efforts given to the improvement of the image processing software responsible for the real-time processing of acquired FITS frames. This so-called Image Processing Elements (IPE) pipeline is based on the modular design to make it more flexible for modifications and implementation to other systems. Currently, there are nine IPEs in total responsible for many different tasks like image segmentation, astrometric reduction, tracklet building or object correlation.

In our work we will present the AGO70 system’s technical characteristics and observation programs. We will introduce the overall design of the system and its functionalities. The planning, acquisition and processing of light curves, BVRI photometric data, and astrometric measurements will be discussed in detail. We will present the image processing pipeline which improves the obtained data’s quality and latency.