Dr Fiorenzo Stoppa

Abstract:

The main science aim of the BlackGEM array is to detect optical counterparts to gravitational wave mergers. Additionally, the array will perform a set of synoptic surveys to detect Local Universe transients and short timescale variability in stars and binaries, as well as a six-filter all-sky survey down to ∼22nd mag. The BlackGEM Phase-I array consists of three optical wide-field unit telescopes. Each unit uses an f/5.5 modified Dall-Kirkham (Harmer-Wynne) design with a triplet corrector lens, and a 65 cm primary mirror, coupled with a 110Mpix CCD detector, that provides an instantaneous field-of-view of 2.7 square degrees, sampled at 0.″564 pixel⁻¹. The total field-of-view for the array is 8.2 square degrees. Each telescope is equipped with a six-slot filter wheel containing an optimised Sloan set (BG-u, BG-g, BG-r, BG-i, BG-z) and a wider-band 440-720 nm (BG-q) filter. Each unit telescope is independent from the others. Cloud-based data processing is done in real time, and includes a transient-detection routine as well as a full-source optimal-photometry module. BlackGEM has been installed at the ESO La Silla observatory as of 2019 October. After a prolonged COVID-19 hiatus, science operations started on 2023 April 1 and will run for five years. Aside from its core scientific program, BlackGEM will give rise to a multitude of additional science cases in multi-colour time-domain astronomy, to the benefit of a variety of topics in astrophysics, such as infant supernovae, luminous red novae, asteroseismology of post-main-sequence objects, (ultracompact) binary stars, and the relation between gravitational wave counterparts and other classes of transients.

Abstract:

Massive stars are predominantly born in stellar associations or clusters¹. Their radiation fields, stellar winds and supernovae strongly impact their local environment. In the first few million years of a cluster's life, massive stars are dynamically ejected and run away from the cluster at high speed². However, the production rate of dynamically ejected runaways is poorly constrained. Here we report on a sample of 55 massive runaway stars ejected from the young cluster R136 in the Large Magellanic Cloud. An astrometric analysis of Gaia data^3-5 reveals two channels of dynamically ejected runaways. The first channel ejects massive stars in all directions and is consistent with dynamical interactions during and after the birth of R136. The second channel launches stars in a preferred direction and may be related to a cluster interaction. We found that 23-33% of the most luminous stars initially born in R136 are runaways. Model predictions^2,6,7 have significantly underestimated the dynamical escape fraction of massive stars. Consequently, their role in shaping and heating the interstellar and galactic media and their role in driving galactic outflows are far more important than previously thought^8,9.

Abstract:

The upcoming Cherenkov Telescope Array (CTA) will dramatically improve the point-source sensitivity compared to the current Imaging Atmospheric Cherenkov Telescopes (IACTs). One of the key science projects of CTA will be a survey of the whole Galactic plane (GPS) using both southern and northern observatories, specifically focusing on the inner galactic region. We extend a deep learning-based image segmentation software pipeline (autosource-id) developed on Fermi-LAT data to detect and classify extended sources for the simulated CTA GPS. Using updated instrument response functions for CTA (Prod5), we test this pipeline on simulated gamma-ray sources lying in the inner galactic region (specifically 0^◦ < l < 20^◦, |b| < 3^◦) for energies ranging from 30 GeV to 100 TeV. Dividing the source extensions ranging from 0.03^◦ to 1^◦ in three different classes, we find that using a simple and light convolutional neural network it is possible to achieve a 97% global accuracy in separating the extended sources from the point-like sources. The neural net architecture including other data pre-processing codes is available online.

Abstract:

Context. Optimal frequency identification in astronomical datasets is crucial for variable star studies, exoplanet detection, and astero-seismology. Traditional period-finding methods often rely on specific parametric assumptions, employ binning procedures, or overlook the regression nature of the problem, limiting their applicability and precision. Aims. We introduce a universal- nonparametric kernel regression method for optimal frequency determination that is generalizable, efficient, and robust across various astronomical data types. Methods. FINKER uses nonparametric kernel regression on folded datasets at different frequencies, selecting the optimal frequency by minimising squared residuals. This technique inherently incorporates a weighting system that accounts for measurement uncertainties and facilitates multi-band data analysis. We evaluated our method’s performance across a range of frequencies pertinent to diverse data types and compared it with an established period-finding algorithm, conditional entropy. Results. The method demonstrates superior performance in accuracy and robustness compared to existing algorithms, requiring fewer observations to reliably identify significant frequencies. It exhibits resilience against noise and adapts well to datasets with varying complexity.