Zoë de Beurs (she/hers)

@ginadesic aww, thank you gina!! 😊

Congratulations to Zoe de Beurs (@Astro_Zozo) for her new paper measuring the mass of the planet K2-167 b and introducing a new framework for removing the effects of stellar variability from radial velocity observations! arxiv.org/abs/2401.12276

This week, @amvanderburg and I are actually observing dozens of stars to measure the masses of orbiting exoplanets at the TNG in La Palma, Spain! Some of these targets may be included in our analyses in the future…

In addition to K2-167 b, we have applied our CALM pipeline to four other stars (Zhao, L.L. et al 2022). Next, we are excited to apply our pipeline to many more stars that span a variety of temperatures and luminosities!

We’re excited to share our new paper on CALM, a new stellar variability model! Our goal is to “calm” active stars and reveal hidden exoplanets and their masses using the radial velocity method, which detects the gravitational tug of a planet on its star. arxiv.org/abs/2401.12276

Check out this great new paper from Gabrielle Ross, a freshman @PU_Astro undergrad who has been working with me and @Astro_Zozo on "killing planets" (identifying false positives) with her custom software called DEATHSTAR! arxiv.org/abs/2312.08373

I would just pay to have such RV time series on real data. Interesting paper today on arxiv by Quang Tran, Mega Bedell, @rodluger and @exoplaneteer on RV and photometric time series: arxiv.org/abs/2305.00988

@drmattashby Thank you so much Matt!!

@Astro_Zozo @amvanderburg Congratulations Zoe!

It's paper day! Very excited to share that our paper on using deep learning for removing stellar activity from RVs has been accepted to ApJ! Big thank you to @amvanderburg for all his help and an amazing new mug to celebrate my first first-author paper!! arxiv.org/abs/2011.00003

@iamcloudib Thank you so much Jessica! You are to kind!

@Astro_Zozo @amvanderburg Congratulations Zoe! You are a great person and a great scientist!

@astrojoeg @NSF Congratulations Joseph!! You're amazing✨🤩 ✨So proud of you!! 🥳🥳🥳

Thrilled to share I won an @NSF Graduate Research Fellowship. I’m so fortunate to be surrounded by patient and selfless friends, mentors, and colleagues who have showered me with endless support and guidance. Wouldn’t be here without them. This is a dream come true.

All three methods achieve high accuracy, and provide a probability distribution for each source to help quantify uncertainty. We hope that this work will lead to more exploration of ML methods for efficient and reliable XRB classification! 4/4 arxiv.org/abs/2204.00346

In our paper, we compare three ML methods (Bayesian Gaussian Processes, K-Nearest Neighbors, and Support Vector Machines). We train our methods to use spatial patterns in 3D Color-Color-Intensity diagrams so that new, previously unseen XRB sources can then be classified. 3/4

Excited to share our newly accepted paper on using machine learning to classify X-ray binaries (XRBs)! We train 3 ML methods and find that they are highly accurate for most sources, paving the way towards more robust XRB classification. (Image:Chandra) arxiv.org/abs/2204.00346 1/4

On arxiv today, @Astro_Zozo reports her SAO REU project on how to use ML to classify X-ray binary stars - congrats Zoe! arxiv.org/abs/2204.00346