I worked out (thanks Zechmeister) that the wobble post-processing is a self-calibration; this has data-combining value beyond wobble itself • I completed my proof that binary convolution + Gaussian fit is equivalent to x-corr with a synthetic template. What I learned: • There are hybrid methods (physics + data-driven) that would be easy to implement now. • The implicit “divide by tellurics” in wobble is technically slightly wrong (thanks Sharon X Wang)! What I want to know more about: • Are there observing strategy modifications that could improve our robustness to tellurics? Are these in conflict with other desiderata?
Automated Planet Finder (APF) data, used variable RV option to extract good-looking telluric model What I learned • How wobble works • A ton about the current understanding of tellurics: how they are modeled, how individual instruments merit different approaches, etc. What I still want to learn: • How telluric contamination affects measurement of H-alpha. Can hopefully quantify this with wobble-modeled APF data
get started with wobble • Made various bug fixes to wobble, especially to the RV uncertainties (although these may still need work) What I learned: • Every instrument has its own data challenges! (I keep relearning this...) • Even if we have perfect knowledge of the tellurics, removing them is a mathematical challenge (cf. Sharon Wang’s work) What I want to know more about: • Which external measurements (local environment monitoring, national-level weather service predictions, etc) are most feasible & most useful for predicting telluric variability behavior?
did: What I learned: What I still want to learn: - Felt less alone in the battle against tellurics - Gathered strategies from other instrument teams for pipeline dev - Spend more time working with Chad on TERRASPEC - Implement automated (believable) telluric correction into HPF, NEID, KPF pipelines - Variety of mathematical tools we might still explore - What are going to be our common metrics to compare success at telluric correction - -
pull out the code from the SPIRou pipeline and try it on other data sets (as a proof of concept) • Tested with SPIRou and CARMENES data • Progress on github: https://github.com/njcuk9999/tellu_pca What I learned: • Many having the same problems and wanting to use data-drive methods to correct tellurics (i.e. WOBBLE) • Removing OH lines is important for work with Helium, HITRAN is used everywhere, dividing by tellurics is bad but everyone does it anyway, technical details about CARMENES, HPF etc Result: • Have the code working but some problems → will need more work → extend to more instruments Worked with: Evangelos Nagel, Solène Ulmer-Moll Work this week (with spirou data) SPIRou pipeline (with same spirou data) Test on CARMENES
PSG What I learned • Got a much deeper understanding of the issues around dealing with telluric line removals What I want to know more about • How to combine stellar models and telluric models for grid searching HIP 89584 - O6 Arcturus - K2
Planetary Spectrum Generator to compute telluric model. • Fit the telluric model, component by component, as a linear model to IGRINS observations • What’s next: • Improve IGRINS spectrum flattening. • Regularize the wavelength solution. • Solve instrument line profile to replace Gaussian. • Test robustness of using a single model. What I learned: • Telluric modeling has the potential to negate the need for A0s(!) Joe Llama (Lowell) --- IGRINS data --- PSG telluric model --- Optimized telluric fit With help from Greg Mace, Brian Thorsbro, Dan Foreman-Mackey
developing non-linear dimension reduction of Telluric line variations using Diffusion maps. • Worked on running HPF data in wobble. What I learned • How close forward modelling techniques are to fitting Telluric lines as well as sky emission lines. • How various groups are doing Telluric correction. • The inverse transform from non-linear space of Diffusion maps is not trivial. What I want to know more about • Computational methods to do inverse transform of Diffusion maps.
• Apply PCA approach to CARMENES NIR data (Neil) is not trivial What I learned • How other teams deal with telluric lines (especially Spirou => PCA approach developed by Etienne A. & Neil) • That molecfit works with the outdated HITRAN version of 2008 => use 2016 to solve badly corrected oxygen bands • How Kyle models the OH sky emission lines (forward model with three parameters) • Many things about TAPAS & HITRAN • Using a hybrid method is the way to go in the future • Many technicals details about Spirou & HPF
• Comparing Wobble vs. Serval RVs • Optimizing, how Wobble handles CARMENES spectra What I learned and would like to follow up on: • The RVs are comparable in general • Orders behave differently (d_RV(order); some fail) -> a) robust way of combining them for final RVs b) finding the reason for that i) quality of spectra? (but no dependency on SNR or airmass) ii) amount of tellurics and position wrt. the stellar spectrum (d_RVs show clear systematics with BERVs)
wobble working on simulated data - not giving better RVs than CCF or forward modeling... still looking into it • Improved forward modeling algorithm for fitting tellurics What I learned • Make sure to use HITRAN 2016, which would make a difference for especially water and oxygen lines • I’m feeling that the most optimal way forward is to combine ground-up telluric modeling with the data driven method What I want to know more about • Can we pin point to places, one by one, where the synthetic model couldn’t match the observations, and why.
after telluric correction (Y, J, H) With CARMENES spectrum corrected with Molecfit > pb: synthetic data scaled to SNR 100 in J band https://github.com/jason-neal/eniric (Neal 2018, Figueira et al. 2016) • Ran wobble on HARPS data of HD41248 > need to find best parameters for the regularisation What I learned: • New techniques to correct tellurics: PCA with Neil Cook Wobble with Megan Bedell • HITRAN 2016 should improve water & oxygen lines modelling What I want to know more about: • Quantify the gain of correcting for the tellurics • Test out hitran 2016 • Impact of the wind on the telluric lines Solène Ulmer-Moll IA - Porto
- plan to see how it behaves with HPF data • Using different tools (TelFit/Molecfit/terraspec) on HPF data What I learned • The latest HITRAN is preferred (but AER 3.6 ≃ HITRAN2016) • About different data driven approaches to telluric correction What I want to know more about • How do data driven models compare with forward modelling • Metric to compare these methods of telluric correction
differential illumination variations for fiber-fed spectrometer -- gauge feasibility of ‘vanilla’ sky subtraction in the NIR. • Thought experiments for estimating the current limits of micro-telluric contamination on optical PRV measurements. What I learned: • Framework of wobble • HITRAN 2016 • Active interest in full, 2D RVs from multiple directions. What I want to know more about: • How can we robustly estimate the current limits to ground-based radial velocity measurements, even in the most ‘clean’ spectral windows? -- tie into PRV white paper? • Can we reduce observing overheads by in-situ telluric correction (reduce frequency of hot star observations as telluric standards?)
with Kyle Kaplan clarify some spectroscopy questions with Iouli Gordon (HITRAN) (not relevant to RV exoplanets, but relevant to Climate CO2 monitoring from space) The usefullness of a co-ordinated network of Hi-res spectometers HZ exoplanets hunters •make exhaustive inventory of EPHZ around stars nearby sun (increasing distance, to prepare space observations) • Earth longitude distribution to measure host star oscillation regime, to determine age of system •distribution of target stars between various observatories ! otherwise, everybody look at the same star… •each star to be monitored by at least 2 or 3 spectrometers. •a bulletin or Newsletter: fast exchange of informations ? •The network needs not to be formalized by international agreements, or bindings agreements •Examples: TCCON network, NDACC network, etc… a volunteer to initiate and manage this network?
with Terraspec. Looked into the differences b/n atmospheric models isolating changes in HITRAN parameters & different atmospheric models. Saw that these can produce errors in telluric modeling on a similar order of magnitude (at least b/n HIT2008 & AER 3.6 which is ~HIT2016 for water) What I learned: About the many great tools being developed for telluric modeling/removal & what RV folks worry about in removing tellurics (e.g. mismatch in line shape) Remaining questions: Wobble sees success w/ 3 principle components - can do PCA on telluric models for diff atm models to physically inform these components + PCA on extracted telluric data from solar data (right).
did: What I learned: What I still want to learn: - Listened to presentations - Discussed with many different people - Thought about applications to CARMENES data - Test modeling of OH lines, in particular near He 101830 lines - Data-driven approaches should work well for CARMENES data - PCA should be ok although problem is non-linear - OH airglow spectrum can be described by a rather small number of parameters - Whether site (altitude, PWV) has a strong influence on RV precision - Whether there is a rationale to go to space for RV measurements - -
Schlegel “spectro-perfectionism” algorithm on simple fake data. • Worked on code to characterize PSF over PaRVI array from LFC exposures What we learned: • Proof of concept • Constructing the calibration matrix A will be the most difficult part. • Should run this on SIG data, not yet lab data Rose Gibson / Ricky Nilsson from AMNH+Columbia U / Caltech What we want to know more about: • Hope to continue conversations about this extraction method with others firstname.lastname@example.org email@example.com
What I did: What I learned: What I still want to learn: - Ran wobble on sample datasets - Packaging my data into proper format: took time! Should have done it earlier if possible! - Worked on group project: (non-)linearity of underlying telluric manifold [unfinished!] - Running wobble on actual IRD data - Using the wobble as a black-box tool is easy; understanding its output is hard! - A lot of existing tools! - More intuitive feel on data-driven paradigm on modelling tellurics - -
how to use it • How to do non-linear dimension reduction • How to implement a variational autoencoder (VAE) with PyTorch to subtract tellurics (thanks to Miles Cranmer!) What I still want to know: • How to prevent overfitting (aka subtracting out stellar spectrum) by dimension reduction and/or VAE techniques • How well wobble works for high resolution cross correlation spectroscopy
model O2 sky emission along with OH sky emission ◦ Specifically the 1.27 micron O2 band ◦ Molecular data from HITRAN2016 ◦ O2 level populations described by single temperature (Boltzmann distribution) • To do… ◦ Combine forward modeled sky emission with telluric absorption model
on telluric data (ISOMAP, diffusion maps, variational autoencoder) • How to use Wobble • How better use MolecFit and TAPAS. What I still want to know: • Whether non-linear dimensionality reduction can usefully fit telluric residuals. • What the shape of the telluric manifold looks like. • How microtellurics (which are often highly transient) can be accurately modelled. Yale University