Recent Talks

Series: XXX Canary Islands Winter School of Astrophysics: Big Data in Astronomy
Topic: Deep learning
Lecture 1

Series: XXX Canary Islands Winter School of Astrophysics: Big Data in Astronomy
Topic: Machine learning methods for non-supervised classification and dimension reduction techniques
Lecture 1

No sound available

BRITE-Constellation (BRight Target Explorer) consists of six nano-satellites aiming to study of variability of the brightest stars in the sky. Austria, Poland, and Canada contribute two spacecraft each all launched into low earth orbits. The satellites have the same structure: they are 20 cm cubes, 7kg mass, with a CCD photometer fed by 3 cm aperture telescopes. The main difference between pairs of satellites is the instrument passband which set to blue (400-450nm) or red (550-700nm). The core scientific objective is to obtain high precision two color photometry, with a time base of up to 180 days, of stars brighter than 4.5 mag in order to study stellar pulsations, spots, and granulation, eclipsing binaries, search for planets and more.
Since the launch of the first two BRITE satellites in February 2013 more than 5 and a half years of experiences in space have been gathered to run the mission and a summary of lessons learned will be presented.  By now more than 20 peer-reviewed scientific articles have been published based on data collected by BRITE-Constellation satellites in space and most results presented therein benefitted greatly from supplementary spectroscopy by meter size telescopes obtained on ground.

Series: XXX Canary Islands Winter School of Astrophysics: Big Data in Astronomy
Topic: Supervised learning: classification and regression
Lecture 2

Series: XXX Canary Islands Winter School of Astrophysics: Big Data in Astronomy
Topic: Supervised learning: classification and regression
Lecture 1

Series: XXX Canary Islands Winter School of Astrophysics: Big Data in Astronomy
Topic: Data challenges and solutions in forthcoming surveys
Lecture 1

Series: XXX Canary Islands Winter School of Astrophysics: Big Data in Astronomy

Topic: General overview on the use of machine learning techniques in astronomy

Lecture 1

Two exotic elements have been introduced into the standard
cosmological model: non-baryonic dark matter and dark energy. The success
in converting a hypothesis into a solid theory depends strongly on whether
we are able to solve the problems in explaining observations with these
dark elements and whether the solutions of these problems are unique within
the standard paradigm without recourse to alternative scenarios. We have
not achieved that success yet because of numerous inconsistencies, mainly
on galactic scales, the non-detection so far of candidate particles for
dark matter, and the existence of many alternative hypotheses that might
substitute the standard picture to explain the cosmological observations. A
review of some ideas and facts is given here.
(arXiv:1808.09823)

The cutting off of gas supply in galaxies, whether it is exhausted internally or removed forcibly, can have dramatic consequences and are thought to build up clusters dominated by an early-type galaxy population, as observed in the local Universe. In recent years it has become more and more obvious that this transformation starts in moderate overdensities and filaments surrounding and feeding galaxy clusters. Keeping in mind that the environment of a galaxy cluster is the result of continuous hierarchical assembly boasting a wide range of substructures of locally dense environments, we therefore need to ask: Are the observed galaxy (scaling-) relations due to the timing and physics of cosmological structure formation or due to baryonic physics?

To answer this question we need to look beyond the cluster core.
We therefore investigated members (down to 10^8.5 M_sol) of a massive galaxy cluster at z=0.44 out to 3 virial radii. Our observations probe the nearby infall region, central to the topical question of “pre-processing”. The smoking gun of this “pre-processing” are transitional objects, like passive disk galaxies, whose stellar populations and morphologies indicate a recent change in star formation and/or dynamical history. We find a large number of this virialized population at distinct locations. In our exploration of the galaxy stellar mass-size relation (Kuchner+17), we were able to show that the manifestation of the size distribution of the cluster galaxies is due to an outer disk-fading and possible bulge growth that accompanies the varying fraction of star-forming and quiescent galaxies.

Continuing this quest, the Wiliam Herschel Telescope’s new multi-object survey spectrograph WEAVE will run a dedicated survey that focuses on this question of “pre-processing”. WEAVE will map 16 nearby galaxy clusters and their filamentary structure of infalling galaxies out to 5 viral radii. This will allow us to characterize the onset and continuation of galaxy transformation during their infall process toward the cluster center.

The precipitable water vapour (PWV) is the main absorber in the Earth's
atmosphere at infrared (IR) and microwave wavelengths. In the last years,
the IAC Sky team has been providing real-time PWV data from a monitor based
on the GNSS (GPS) technique (GNSS PWV Monitors; GPM). Among other things,
the PWV values help in the scheduling of the telescopes with IR
instrumentation. The GPM have undergone a continuous process of upgrading.
More recently, we have undertaken the PWV forecasting. We will present in
the talk a brief summary of the monitors and details of ForO ("Forecasting
the Observatories"), the forecasting system for PWV at the Observatories.
ForO is based on a mesoscale Numerical Weather Prediction (NWP) model. The
ForO system has been validated and calibrated with PWV data from the GPM
and will deliver accurate PWV daily predictions for ORM and OT on a 24, 48
and 72 hours windows. This is a definitive improvement to optimize the
flexible scheduling for IR observations, in particular for CanaryCAM and
EMIR at the GTC.