To learn more about their projects, what they learned this summer, the biggest challenges they have faced and the accomplishments they have achieved, see their interviews, below: Sabrina Côté Maldonado, Frédéric Genest, Laurent Jacob, Olivia Lim, Merrin Peterson, Steven Rogowski, Laurence Marcotte.
The creation of a homogenous spectral library to analyse the spectrum of the exoplanet 51 Eri b.
It was really interesting to study this planet, one of the few known that is similar in mass and distance to its host star to the giant planets in our own solar system.
According to the comparison with my spectral library, 51 Eri b has a spectral of T7, which would correspond to a temperature of 825 K. This is hotter than what was previously found by Macintosh in 2015, but it could be explained by the fact the relationship I used to determined physical parameters using the spectral type was calibrated for older objects. The homogenous spectral library that I have made will be useful for the analysis of other objects. As more young and low gravity objects are added to it, it will be more relevant to analyse objects like 51 Eri b.
I definitely improved my programming skills, and I am now much better to find information in various file formats. I also learned how to analyse spectra comparing them with reference ones. In general, I feel that I am now more autonomous to solve problems.
The many programming errors which blocked my progression. Google became my best friend, and the others summer interns and other members of iREx brought precious help. It was great to see how everyone were getting along and helped each other. I would like to thank everybody for their contribution to the success of my project!
To obtain results after many weeks working on my code. It is such a great joy when you get results and they make sense. The regular progress I made all along the summer encouraged me to persevere through my internship. The analysis of the results at the end of the summer was also a very fun part, since I got to really understand thoroughly the astrophysics behind the programming.
M starts are great targets to observe and characterize Earth-size exoplanets, since the low luminosity and small size of these stars make the transits of planets much easier to measure. However, these stars are cold enough to contain water in their atmosphere and their startspots contain a little more of it, being colder. When a planet transit in front of an M star without occulting any starspot, there is a water absorption feature that appears that can be mistaken for the detection of water in the atmosphere of the planet. My project’s goal was to determine if we can identify the origin of the water absorption feature (from the starspots or from the planet’s atmosphere) using the fact that the planet has a varying radial velocity due to its orbital motion around the star. We were hoping this variation would cause a shift of the absorption feature (Doppler effect) that can be detected using high resolution spectroscopy (with SPIRou, for example).
The scientific community is really interested in M stars right now, because they are good targets to find earth-like planets that we will be able to characterize in a near future. This project was meant to pave the way for the analysis that will be made with new instruments that will allow these discoveries. What made the project interesting to me was that it addressed a problem that was, to my knowledge, not studied thoroughly before, but that will probably have an important effect for the search of exoplanets.
I have obtained two interesting results. The first one is that it will be possible to distinguish water in the atmosphere of the star from that in the atmosphere of a planet. The variation of the radial velocity of the planet will induce a shift that will be measurable with SPIRou. However, it will be necessary to compare the observations with models that are extracted from thousands of different atmospheres. The second result, just as important, is that depending on the atmosphere of the planet, it will sometimes be possible to conclude on the presence of water in it without using any complex analysis method.
I have learned a lot! I had to learn about transit spectroscopy and exoplanet’s atmospheres. I also learned to code much better (in Python) and to build and use various models. I learned about M stars and their properties. I also learned a lot of general knowledge in astronomy.
The beginning of the project, when I had to learn how to code and the various astrophysical knowledge I needed during the summer. It can be challenging to read many scientific papers to know how the project will be carried out. Fortunately, this period didn’t last long and I was soon able to code simple models and put to practice the knowledge I acquired.
I liked the feeling to be working on a useful, relevant and interesting project. I really liked the work atmosphere: I was quite autonomous, and I never felt too stressed. It was also a pleasure to work with the other interns.
My internship was about trying to detect the refraction of light in the atmosphere of a planet right before and after it transits.
This is very interesting because it provides a unique way of looking for atmospheres in certain exoplanets. Depending on the result, you can either confirm the current models or improve them. It can also give insight into how thick the atmosphere is and whether the atmospheres absorbs a lot of light or not.
We discovered that the atmospheres of the observed exoplanets have a much smaller refraction signal than expected with our current models. This confirms us that the models presented so far are inaccurate in representing the actual refraction effect. There is also a possibility that the atmospheres are very cloudy.
I learned how to use mathematical tools in python in order to analyze big datasets. I learned how to go through scientific papers and understand the essence of the content. I also learned how to ask the right questions and how to tackle a series of problems more efficiently.
The biggest challenge was trying to catch up to the literature on the subject. Most of the papers were difficult to go through at first due to the degree of knowledge required to understand them correctly. However, I’m glad I had to go through a lot of papers as it is an essential skill when doing research.
I really liked the fact that research is a team effort. It is comforting to be able to ask questions to experts about subjects you are just beginning in. The project itself was very interesting and I’m glad we were able to get a significant result out of it.
My first project was on measuring the small-scale magnetic field of M dwarfs. We used high resolution intensity spectra from ESPaDOnS that we analyzed with a radiative transfer code.
My second project was on confirming et refuting binary and multiple systems made of at least one M dwarf using astrometric data from the GAIA and HIPPARCOS missions.
Magnetic fields in M dwarfs can hinder the detection of exoplanets by the radial velocity method. With the imminent delivery of SPIRou, whose primary mission is to detect and characterize telluric planets in orbit around low-mass stars, it is necessary to better understand the impact of magnetic fields on the data that will be collected by the instrument.
We know that approximately 50% of solar-type stars are part of a binary system. However, this ratio is not as accurately known for M-type stars. Yet, it is crucial to study binary and multiple systems for this spectral type since these systems should be avoided in the context of searching for exoplanets. Therefore it is important to have an updated and accurate catalog of binaries and multiples with M dwarfs.
The radiative transfer code used to measure the magnetic fields has certain limitations that must be quantified. This code uses parameters such as the temperature of the star and its gravity that have to be either adjusted, which requires more time, or fixed, which requires a good knowledge of the value of the parameters. In the second case, if a wrong value is given to the code, then the measured magnetic field can vary.
BD+74 456a is probably a giant! This star was classified in a triple system that was made of a K-type star, an M dwarf, and the giant star itself, but by analyzing its absolute magnitude and by comparing its parallax and proper motion to those of the other components in the system, we came to the conclusion that BD+74 456a is probably a background giant star that is not bound to the system.
I learned the different steps to reduce spectra: find the radial velocity of a star by cross-correlation with standards, correct a spectrum using this radial velocity, subtract telluric lines from a spectrum, etc.
I learned how to use astrometric data such as parallax, proper motion, separation and apparent magnitude to deduce the relative position of a pair of stars.
By comparing our results of measured magnetic fields to those obtained by other researchers, we could not see any correlation. However, we must be cautious with the conclusions that we draw from this: inconsistent results do not imply that one of the methods is completely incorrect. We have to make sure the methods really measure the same quantity and we must consider the errors in each method that could explain a divergence.
The catalogs used in the project on binaries and multiples was incomplete for the individual components of the systems. It was therefore impossible to directly compare astrometric measurements for these stars. We had to find alternative ways to deduce the relative position of the components of these systems to tell whether they are bound or not their system.
My favorite part of the project on magnetic fields was the fact that we could compare our results to those obtained by international researchers. This allowed us to put our method to the test, and we could also see that other people are also enthusiast about this subject and determined to find answers.
As for the project on binaries and multiples, I really enjoyed learning how to used astrometric data to compare the position of stars. The amount of information that can be extracted from only a few measurements has blown me away.
In terms of instrumentation, I had the opportunity to learn, at the Observatoire du Mont-Mégantic as well as at the Canada-France-Hawaii Telescope, how the data, the raw data actually, used by astronomers are collected! In my opinion, it is an aspect of astronomy that is different but just as interesting and complex as data analysis.
The topic of my project was detecting water in the atmosphere of a specific exoplanet (the first ever discovered around a sun-like start in 1995, 51 Pegasi b) in archival spectra from HIRES, the spectrograph at KECK observatory. Water has been detected previously on the planet, using almost the same methods as ours but with data from a different spectrograph. We are trying to model the star’s light.
The project is interesting because, until I worked on it this Summer, we didn’t know whether it would be possible! The data I am working with was taken to get information about the star’s speed, and the planet’s light has not been resolved in it before; if we succeeded, the old data would be useful for a totally new purpose.
We were able to discover that 51 Pegasi is too bright to resolve the planet’s light this way, but that the method could work for dimmer stars in the archive. Now that we know this, my supervisors might use the method, and my code, to investigate these systems.
This summer, I learned lots of information specific to my project, but I learned a lot more about astronomy in general and about how people on Earth get information about other planets! I now know how science is done in astronomy: that is, where and how researchers get data and what they do with it. I also got to work with data from a real observatory (KECK) and helped make observations at Observatoire du Mont-Mégantic.
My biggest challenge this Summer was working with time constraints. We picked an ambitious project for four months, and I couldn’t do the whole thing perfectly. It was difficult to decide what information we most wanted and how to get that in four months, even if it meant I had less time to improve code.
What I liked most about my internship is that it introduced me to research in the world of astronomy. I also liked that it was independent; although I spent a great deal of time questioning my supervisors, I feel like I was given a project, a data set, a group of experts to ask questions, and set loose! It was a really fun challenge and a great project and I wish I could have spent another four months on it.
This summer I worked with Hubble Space Telescope data using the transit spectrum extraction code ExoTEP and atmospheric modelling code SCARLET (both developed by my supervisor Björn Benneke). I spent a lot of time optimizing and adding new atmospheric physics to the SCARLET code in particular. Specifically, we were looking at data for an exoplanet similar to Saturn in mass from a recent HST survey of 16 exoplanets spanning a range of masses and equilibrium temperatures. The objective will eventually be to constrain the atmospheric composition (particularly the metallicity) of this planet and determine to what extent clouds and/or hazes are present in its atmosphere. Ultimately, the aim is to develop a broader understanding of the wider exoplanet population in preparation for new, more powerful observatories like JWST coming online in the next few years.
Studying exoplanets in this way is particularly exciting because I have the chance to compare actual observational data to the results of detailed atmospheric models. This gave me the chance to gain experience on both the observational and computational side of things. Going forward with my MSc at UdeM, I will continue to work in both of these regimes.
I started my project in June. Now, in August, we have preliminary results which are promising but most certainly not final. I will be continuing this project for my master thesis, and we aim to submit our results to the AAS journals by the end of this year (2017).
The hands on experience working with a detailed code such as SCARLET provided me the unique opportunity to dramatically improve my coding skills. Working with such a well-optimized and concisely written code probably taught me as much about programming as multiple computer science courses did.
Trying to understand and improve upon someone else’s code, especially coming from a background of having done no truly object-oriented-programming, was the biggest challenge. However, this also turned out to be one of the most important things I learned and will no doubt help me tremendously in the years to come.
Just getting the chance work on exoplanet research was pretty much a dream come true. I’ve wanted to end up in this area of astronomy since I decided I would major in astrophysics back in high school.
My internship was on the exoplanet Kepler-10b. The objective was to make a simple model to determine its rotation period and albedo.
What I personally found interesting was to learn to create a model to better understand a planet taking into account all the different physical concepts involved.
I did not find with certainty the rotation period of Kepler-10b, but I was able to determine a few values that are plausible with what we know of the planet. It could have a rotation period equal to its orbital period, which would allow to determine that the same portion of the planet is always facing the star (« tidally locked »). Another possibility is that the planet is rotating quite fast in a direction that is opposite to its orbital movement. I was able to determine the albedo of the planet should be between 0.1 and 0.2.
I learned to code in Python, which will be really useful during me undergraduate studies. I also learned more about exoplanets and what physical processes are relevant to their study.
My greatest challenge was to start to program my model. Since it was my first programming experience, I was not sure where to start and what to do first.
The people and the environment. I really like to do this project, but I was also pleasantly surprised by the people working with me. They were always there to help and we discovered many things together (for example, new python functions). There were also many outreach and social activities that contributed to the very good atmosphere.