In the field of gravitational-wave (GW) interferometers, the most severe limitation to the detection of transient signals from astrophysical sources comes from transient noise artefacts, known as glitches, that happens at a rate around 1 per minute. Because glitches reduce the amount of scientific data available, there is a need for better modelling and inclusion of glitches in large-scale studies, such as stress testing the search pipelines and increasing the confidence of detection. In this work, we employ a Generative Adversarial Network (GAN) to produce a particular class of glitches (blip) in the time domain. We share the trained network through a user-friendly open-source software package called <monospace>gengli</monospace> and provide practical examples of its usage.

The Pan-STARRS telescopes, located on Haleakala, Maui, Hawaii, are conducting a long-term search of the night sky for Near-Earth Objects. The Pan-STARRS survey is now one of the leading NEO surveys, accounting for more than 40% of all new NEO discoveries, and over 50% of discoveries of larger NEOs. Pan-STARRS is also a prolific comet discovery telescope, and discovered the first interstellar object, ‘Oumuamua.

We proposed a machine learning approach to identify and distinguish dusty stellar sources employing supervised methods and categorizing point sources, mainly evolved stars, using photometric and spectroscopic data collected over the IR sky. Spectroscopic data is typically used to identify specific infrared sources. However, our goal is to determine how well these sources can be identified using multiwavelength data. Consequently, we developed a robust training set of spectra of confirmed sources from the Large and Small Magellanic Clouds derived from SAGE-Spec Spitzer Legacy and SMC-Spec Spitzer Infrared Spectrograph (IRS) spectral catalogs. Subsequently, we applied various learning classifiers to distinguish stellar subcategories comprising young stellar objects (YSOs), C-rich asymptotic giant branch (CAGB), O-rich AGB stars (OAGB), Red supergiant (RSG), and post-AGB stars. We have classified around 700 counts of these sources. It should be highlighted that despite utilizing the limited spectroscopic data we trained, the accuracy and models’ learning curve provided outstanding results for some of the models. Therefore, the Support Vector Classifier (SVC) is the most accurate classifier for this limited dataset.

We present two studies where astrophysics and computational techniques are applied to astroparticle physics and medical image processing. The first one is the use of software as Corsika, Gammapy, Astropy and ARTI to identify potential sites to study astroparticles physics in Ecuador. We determine the secondary particle fluence and M1 signal at different locations, and we calculated the effective area of a prototype Water Cherenkov Detector (WCD) in Quito. The second work is the manipulation of medical images using python and PyDICOM libraries.Aiming to explore the Computer Aided Detection (CAD) systems for the automated detection of numerous lung diseases. We develop a tool for extracting information from DICOM files more efficiently. A second tool consists of a lung segmentation technique for computed tomography images.These works shows the versatility of the computational tools encouraging young researchers to explore and further develop the use of such tools in other areas.

Comets and asteroids collision with Earth and other planets is part of the continued planetary formation, the other part is the solar wind delivers water and gasses back to the Kuiper Belt from the planets, together they form the solar hydrologic cycle. The new theory of solar hydrologic cycle provides that solar wind stripping water and gasses from the inner planets, while having diminished effect on outer planets, is the cause for outer planets becoming gas giants. Jupiter, having the smallest orbit among outer planets, is destined to be the predominant planet and plays a critical role in complex life evolution on Earth. Jupiter grows mass by locking up comet mass, thus reducing the number of comet collisions to Earth. Reduced hydrogen infusion from comets enabled Earth’s atmosphere transitioning from hydrogen to oxygen rich. The transition trajectories of Jupiter mass gain and Earth water and gasses mass fluctuation are calibrated using known geological events. Earth’s trajectory can be divided into three periods, Hydrogen, Carbon Dioxide, and Oxygen, named after predominant gas in Earth’s atmosphere for the period. Complex life only flourishes in the Oxygen Period, when aerobic metabolism is possible. Mass extinction can be caused by cometary hydrogen infusion that incinerates atmospheric oxygen. Probability of such astronomical hazards is declining as outer planets have locked up most comets and will continue to absorb more comets. Earth is safer than ever, and will become even safer, as dictated by the solar water cycle. The physics of the solar hydrologic cycle is universally true, life should be universal phenomena.

Since the last 1990s, Doppler spectroscopy has been one of the most prolific methods of detecting and characterizing exoplanets (Fischer et al. 2016). The latest generation of stabilized spectrographs can achieve impressive levels of precision and stability, approaching that needed to detect the motion of a Sun-like star due to the gravity of an Earth-mass planet in its habitable zone (Crass et al. 2021). However, the exoplanet detection power of modern radial velocity (RV) exoplanet surveys is typically limited by the spectral variability of the target star. Machine learning (ML) has the potential to significantly improve the ability of RV exoplanet surveys to distinguish planets for stellar variability. Astronomers have begun making applying a wide variety of ML techniques, from principal component analysis and multilinear regression to convolutional neural networks. This paper reviews the state of the field for mitigating stellar variability in RV exoplanet surveys from a ML perspective. Early results show that relatively simple ML techniques paired with well-engineered features often perform comparable to much more complex ML models, while providing improved interpretability and explainability. These are likely to be critical factors for establishing the credibility and robustness of any future detections of potentially Earth-like planets.

One of the best and most direct ways to study planet formation processes is to observe young planets while they are forming within their birth protoplanetary disks. As they form, planets tidally interact with their parental disk and produce observable signatures. Recent observations have demonstrated that kinematic planetary signatures (KPS), the perturbed velocity fields of the gas in the protoplanetary disk in the vicinity of the planet, can be observed with the Atacama Large Millimeter/submillimeter Array (ALMA). Here, I introduce a machine learning-based tool KPSFinder (Kinematic Planetary Signature Finder), which aims to find KPS robustly and efficiently.

Migration of bodies under the gravitational influence of almost formed planets was studied, and probabilities of their collisions with the Earth and other terrestrial planets were calculated. Based on the probabilities, several conclusions on the accumulation of the terrestrial planets have been made. The outer layers of the Earth and Venus could accumulate similar planetesimals from different regions of the feeding zone of the terrestrial planets. The probabilities of collisions of bodies during their dynamical lifetimes with the Earth could be up to 0.001-0.01 for some initial semi-major axes between 3.2 and 3.6 AU, whereas such probabilities did not exceed 10−5 at initial semi-major axes between 12 and 40 AU. The total mass of water delivered to the Earth from beyond Jupiter’s orbit could exceed the mass of the Earth’s oceans. The zone of the outer asteroid belt could be one of the sources of the late-heavy bombardment. The bodies that came from the zone of Jupiter and Saturn typically collided with the Earth and the Moon with velocities from 23 to 26 km/s and from 20 to 23 km/s, respectively.

In this work we present a Bayesian approach to obtain the non-rotating stellar spectra from an observed spectrum of a rotating star. This is our first attempt to solve an inverse problem expressed in terms of a Fredholm integral. Our preliminary results with synthetic spectra are promising. More studies are required to compare our Bayesian approach with the standard method for real spectra.

We show that the U-Net neural network architecture provides an efficient and effective way of locating sources in SKA Data Challenge datasets. The improved performance relative to PyBDSF is quantified and U-Net is proposed as an efficient source finder for real radio surveys.

In this paper, we present a convolutional neural network (CNN)-based architecture trained on a dataset of meteorites and terrestrial rocks and another dataset trained on meteors and light sources. For meteorites, the dataset comprises augmented images from the meteorite collection at the Sharjah Academy for Astronomy, Space Sciences, and Technology (SAASST). For meteors, the images are taken from the United Arab Emirates (UAE) Meteor Monitoring Network (MMN). Such a project’s significance is to expand machine learning applications in astronomy to include the solar system’s small bodies upon contact with the Earth’s atmosphere. This, in return, acts as deep learning research, which examines a computer’s ability to mimic a human’s brain in recognizing meteorites from rocks, and meteors from airplanes and other noise sources. When testing the CNN models, results have shown that both the meteorite and meteor models reached an accuracy of above 80%.

The Gaia mission DR3 provides accurate data of around two billion stars in the Galaxy, including a classification based on astronomical classes of objects. In this work we present a web visualization tool to analyze one of the products published in the DR3, the Outlier Analysis Self-Organizing Map†.