-
Solar Flare Physics.
Over the solar visible surface occur a great number of events with explosive characteristics due to their short duration, variety of manifestations and energy released. Solar flares are the most energetic events we can observe over the Sunwith great detail. While they do not pose a great risk to life on Earth, they disturb the space where our satellites live.
My main interest in solar flares is to understand how, and why they occur. I use radio observations from a few GHz from, e.g., the Owens Valley Solar Array, to few hundred of GHz of our Solar Submillimiter Telescope, and more recently the Atacama Large Millimeter Array (ALMA). I also use X-rays (XR) data taken by satellites like RHESSI or the GOES series from NOAA; Ultraviolet (UV) images from EIT and TRACE are also very helpful and complemented with ground base telescopes with Hα filters, magnetograms, etc.
-
Radio Astronomical
Instrumentation.
I work with single dish telescopes that have many receivers in the focal plane (focal array) which allow us to deduce the center of the emitting source as well as its size. This technique was named Multibeam. The movie in this page was made projecting the six SST horns, (the six white circles), over the sun disc here represented by a map obtained at the frequency of 212 GHz and artificially colored. (On that day the sun did not have active regions.) When the Earth daily moves respect to the sun the horns change their relative position.
In the last years I'm very actively collaborating with the Large Latin American Millimeter Array (LLAMA) a multipurpose telescope similar to the Atacama Pathfinder Experiment (APEX), to be installed at 4825 m above sea level and ro obeserve in the range from 45 to 700 GHz.
-
Time Series.
Most of our observations are time series, i.e. values registered along some time. We want to find periods, or periodicities in the data. We use classic techniques such as Fourier Analysis and new techniques like wavelets.
Furthermore, every observation is contaminated with interferences that we call noise. The determination of the noise is a key element in the time series analysis, for that we use classic techniques and new ones like Bayesian Probabilities.