Data Science for Science

The Coma Cluster contains more than 1,000 galaxies. Scientists have long been frustrated by large uncertainties in its mass. Credit: PSC  A galaxy, as you may know, is a huge collection of stars that has a certain overall shape and which moves as a unit across the cosmos. Even larger groupings such as galaxy clusters  exist. The individual galaxies in a cluster move about relative to each other and revolve around their common center of mass. Their velocities relative to our observation point can be gleaned from measurements of their red-shifts or blue-shifts.  Estimation of the mass of galaxy clusters is still fraught with uncertainties. It is not yet clear how to determine the three-dimensional structure of galaxy clusters which reduces further the confidence of estimating the mass.  But why estimate the mass? It is now known that stars in a galaxy revolve around the center of mass of the galaxy. But their velocities exceed the calculated velocity based on the detectable mass of the

    (Photo credit: BBC) Are We Alone?  It is a question that humans have had ever since we realized there are other worlds, other planets out there in the cosmos. The implications of the answer to this question are staggering no matter what that answer is. In pursuit of an answer scientists have been searching for planets that could potentially harbor life. Planets that are outside of our solar system are termed Exoplanets. The exploratory pursuit is to find "earth-like" exoplanets because these would have the greatest chance of harboring life as we know it. Determining the habitability of exoplanets is not an easy task. Existing ML methods such as Metric-based quantification (using metrics like surface temperature) or Supervised Learning (based mainly on estimated surface temperature) are not reliable enough.  Astronomers from the Indian Institute of Astrophysics along with astronomers from BITS Pilani and Goa campus designed an anomaly detection method to identify potential

Hubble Deep Field. Courtesy: Encyclopedia Britannica Space is vast. The number of objects in the Universe is literally astronomical. The first step toward exploring these objects is to identify them aka classify them. Objects could be stars, galaxies, quasars, supernovae etc. Due to the sheer number and complexity of objects the task is very challenging to do manually. The further away a source of light is, the harder it is to distinguish its features and thus classify it. Astronomers have now sought the aid of AI in this task. A machine learning pipeline called SHEEP has been developed. SHEEP first extracts the photometric redshift of celestial objects and then uses this as one of the data features fed into an ML model for training. SHEEP combines several ML algorithms: XGBoost , LightGBM , and Catboost to obtain better classification performance. SHEEP contains two distinct classification methodologies: (i) Multi-class and (ii) one versus all with correction by a meta-learner.

Namaskaram! Today we have an application of AI in Astronomy. The Universe is vast. So mind-boggling is its vastness that astronomers need the use of technology to find and classify astronomical objects. The Subaru Telescope, located in Hawaii, USA, had taken numerous images of galaxies from Earth's vantage point. Thanks to its high sensitivity, as many as 560,000 galaxies have been detected in the images. It would be extremely difficult to visually process this large number of galaxies one by one with human eyes for morphological (shape) classification. The AI technique enabled astronomers of the NAOJ (National Astronomical Observatory of Japan) to process these galaxies without human intervention. Deep Learning based image classification techniques have been used to classify images based on their pixel-data. You might have heard of the "dog" and "cat" classifier. Well, it turns out Deep Learning is also good at distinguishing galaxies "with spiral patterns

Courtesy NASA/JPL-Caltech Hello, Today we have an entry in the field of Astronomy. It is specifically about planetary systems around other stars and their orbits. Astronomers detect planets around other stars and collect data. There are enough data points to confirm that they have indeed detected a planet but not enough to determine the exact orbit of that planet around its host star. Throw in multiple planets around a single host star and you have a planetary system for which you do not know the stable orbital configuration. Knowing the stable orbital configuration allows astronomers to predict planetary positions and movements which would in turn be useful for making observations (such as atmospheric composition) and help bolster or weaken theories of exoplanets. How can ML help? Previously, orbital configurations would have to be simulated over many billions of orbits using brute-force techniques in order to find stable configurations. These would take many hours even on modern supe