the 3D substructure of dust, continue to maim the gaseous Perseus arm, study details of the velocities of young stars in the MW, LMC, SMC. Maybe also ultra-precision stellar color standards for reddening? Hope to learn: The best ways to use the results of and limitations of the Apsis pipeline, statistical analysis techniques relevant to Gaia. What I bring: Knowledge of the ISM and reddening, details of spiral structure, some deep learning especially in image analysis.
of the shape of the velocity ellipsoid of the Milky Way’s stellar halo What I bring: Knowledge of Galactic dynamics, and a cheery disposition. Hope to learn: All the cool science that people are doing :) and how to best exploit the various parameters provided by the Gaia Source. I like to think of myself as the guy on the left...
study the dynamical relations between (stellar-)halo substructures identified in Integral-of-Motion space using SDSS/SEGUE/LAMOST+Gaia; 2. constrain the dark matter distribution of the Milky Way with 6D information of blue-horizontal-branch stars and K giants. Hope to learn: how to measure element abundance of stars with low-resolution spectra; chemo-dynamical modeling for streams. What I bring: code of calculating E and L and identifying groups in I.o.M space; SDSS BHB/SEGUE K giants X Gaia catalogs; some experience on distance estimation of halo K giants.
catalogue corrections based on Gaia as reference. Improved dynamical models from combination of Gaia and ground-based astrometry. Hope to learn: Statistical inversion for large data sets. Techniques parameteric or not for combining inhomogeneous data (of different variance, distributions, etc.). Python and visualisation tool What I bring: astrometry and dynamics of Solar System Objects. poor knowledge about much.
Investigate stellar abundances (Hypatia Catalog) in the solar neighborhood via DR2 and how “well measured” stars may be, examine chemical+physical disparity between thin and thick disk stars (since there seem to be a variety of definitions on how you tell the populations apart) Hope to learn: New data-driven methods for exploring large datasets and matching methods to a problem, including the meta- or housekeeping data What I bring: Hypatia Catalog of stellar abundances (www.hypatiacatalog.com), solid knowledge of Python, decent grasp of XGBoost Find me if you want LEGO—>
velocities for hot Jupiter hosts and a control sample, compare velocity dispersions, meet new people in the field! Hope to learn: How to select the best sample from DR2, correctly use full covariance info when using parallaxes What I bring: Experience comparing population ages with velocity dispersions, knowledge about exoplanets, good python knowledge
identify, classify and understand unknown cases where the DR2 astrometric solution is odd, apply ML to this. Learn how to use other catalogues. Hope to learn: New ways of validating and improving future Gaia astrometric solutions, find coherences and incoherences in the data, see how people use the data and find out what we can try to improve in future releases (what statistics are we missing for example). Python, statistics. What I bring: Experience in Gaia astrometric solution, limitations, errors, gaia source statistics. Knowledge of the data model, filters applied to DR2, DPAC internal processing, Java, catalogue Xmatch. Working with large datasets.
(V - K s )-M ks color-magnitude diagram for K2 exoplanet hosting candidate sample. (V - K s )-M ks color magnitude diagram is correlated with metallicity. I am curious to know if there exists a significant metallicity offset for exoplanet hosts. & Get to know other awesome research topics! Hope to learn: Statistics related to Gaia measurement bias, using ML on big data in astronomy, general familiarity with other databases, major differences between Gaia DR1 and DR2 measurements What I bring: Familiarity with MAST database, knowledge of broadband photometry related to low mass stars and exoplanet formation theory.
the kinematic age of the high-metallicity halo, and find substructures therein! (Also maybe make spectroscopic parallaxes?) Hope to learn: How to understand (and exploit!) the Gaia noise model and excess noise outputs, and other housekeeping data. What I bring: Expertise in hierarchical modeling and probabilistic models, self-calibration of surveys and instruments, The Cannon. I love to consult talk.
DR2 data to improve our understanding of short-period stellar multiplicity. I want to (a) use the RV dispersions from Gaia as a tracer of stellar multiplicity and (b) cross-correlate Gaia with APOGEE and other data sets to refine multiplicity constraints and single out interesting systems for follow up. Hope to learn: How to take advantage of parallaxes and proper motions (and RVs, when available) to pin down stellar models. What I bring: Some familiarity with APOGEE and statistical inferences from sparsely-sampled RV curves. Quirky, unsystematic knowledge of stellar evolution and chemical enrichment.
Hope to do: Study Young Local Associations: from selection/detection to estimation of a dynamical age. Hope to learn: Population selection based on Gaia DR2 or crossmatch with other catalogues. (Python) tools to manage large data sets. What I bring: Familiarity with Gaia DR2 data and archive. Experience in exploring disc kinematics, effects of the galactic bar, spirals, warp. Experience using orbit integration and coordinate transformation from the observables to galactocentric using correlations.
observed and simulated young star clusters and star forming regions; dip my toes in observational astronomy. Hope to learn: How to find star clusters in Gaia DR2; how observed clusters are characterized dynamically and in other ways (color, magnitudes, etc.). What I bring: Knowledge of N-body simulations (esp. using the AMUSE framework), galactic dynamics, and protoplanetary disks; python skills; some simulated star clusters with different initial conditions. + Portegies Zwart & Concha-Ramírez 2017 + Concha-Ramírez et al 2018 (submitted)
hypervelocity stars. Douglas Boubert (IoA, Cambridge -> Magdalen College, Oxford) Hope to learn: Machine learning 102, planet/star problems with Gaia, how to use Bayes on Gaia-scale problems. What I bring: • Battlefield experience with the “features” of Stan/MultiNest. • Bayesian treatment of Gaia correlations and parallax->distances. • Single and binary stellar population synthesis. Hypervelocity stars (might...) come from here.
(+data) • Stream search with pole counts (+PyMGC3 code) • GALSTREAMS library of MW streams' footprints Hope to do: • Search for streams in Gaia DR2 using nGC3 pole counts + RR Lyrae/RC/etc. • Calibrate RR Lyrae distances in G band (+ Oosterhoff type indicator = evolutionary proxy) Hope to learn/get help with: • How to define a selection function for streams • Bayesian Period-Luminosity calibration for RR Lyraes
dark matter substructure in the Milky Way, measuring the number of substructure objects as a function of their mass. Turn this into constraints on dark matter particle physics. Hope to learn: Can we use the Gaia data to measure substructure’s phase-space volume, and relate that to its mass? Can we distinguish globular clusters from dwarf galaxies? What else can GAIA do for dark matter particle physics? What I bring: Some python skills, dark matter particle physics and astrophysics, a charming naivety about non-dark-matter-astronomy.
• artifacts • completeness functions What I bring • binaries, big and small • white dwarf models • short attention span • scripts to submit 1000s of ADQL queries & bring ESA server to its knees Hope to do • Binaries ◦ MS/MS, WD/MS, WD/WD • separation distribution • [Fe/H] dependence
of looking at the DR2 data; exchange methods and knowledge; watch Kareem bring the ESA server to its knees; think about the detection of long-period binaries (50-500 yr) in astrometry data Hope to learn: Issues with the DR1 and DR2 datasets; how to exploit uncertainties astrophysically (i.e. finding RR-Lyrae from photometry uncertainties); error bar calculation for non-Gaussian uncertainties What I bring: Experience with the Gaia astrometric solution; detailed understanding of TGAS and astrometric catalogue combinations; experience with cluster membership searches in DR2
the most stringent constraints we can put on the structure of the inner halo with GDR2 data, given the observational uncertainties? • How can we quantify and visualize the uncertainty on 3D density (sub)structures? • Gaia's selection function • Efficiently dealing with Gaia samples that no longer fit in memory. • Python, C++ • Hands-on experience with Bayesian regression, model comparison, ... • Supervised classification, clustering • Variable stars
systems to construct MW star formation history(-ies) Hope to learn • All the things DPAC (we) did wrong! [feedback] • something new about the Milky-Way from Gaia. What I bring • Some limited Gaia knowledge [DPAC] • ADQL help tutorial mfouesneau/tap • Dust attenuated SED simulations Tools & tutorial [specs. & phot.] mfouesneau/GaiaSprint2018 Expect to do • totally different projects triggered by discussions MPIA (Heidelberg) - Gaia DPAC [CU8]
bring / expertise Stellar Spectroscopy GALAH DR2 (arXiv:1804.06041): 340,000 stars, ≤ 23 abundances 99.8 % overlap with Gaia DR2 86 % with ϖ-unc. < 10%, ADQL X-matches 2MASS,WISE... Hope to do • Low-[Fe/H] stars and their actions • Counter-rotating halo? Hope to learn • Analysing/dissecting action-abundance space • How to get this in a short paper?
dust maps of the Kepler field as Bayesian priors to calculate better distances, absolute magnitudes etc. Make better crossmatches between faint stars in Gaia + other catalogs than the ones provided by ESA. Hope to learn: What is the best practices for calculating distance probabilities in a Bayesian way for a limited sample of stars (rather than modeling the entire galaxy)? What I bring: General low-mass star knowledge. Enthusiasm. Kepler + Gaia crossmatch.
clusters; where are the tidal tails? - Action & angle-space disk substructure? - Wide binary separation as a fn of Jz? - Where are the spiral arms? TO BRING: - Gaia subsets for MW globular clusters - Python , stats , dynamics - Snarkyness & emotional support Adrian Price-Whelan (Princeton)
marginalization CMD + 2D/3D dust maps without stellar models Hope to learn: Galaxy dynamics, white dwarf populations What I bring: Unconditional emotional support (impostor syndrome: sashay away!) File-by-file split of 2MASS-SDSS-PS1 cross-matches + filtering scripts Expertise in statistics and (very) large Bayesian inference problems
the origins of hypervelocity stars with a Galactic Center origin (i.e., “answer” the question: is there an IMBH in the Galactic Center?). I bring ARCHAIN models for the competing theoretical formation mechanisms. Hope to learn: The secrets of the Universe (all of them). What I do: I make (often abstract) models that describe simulated data. Dynamics (chaotic, secular, mostly in the collisional regime), gravity integrators, weird stellar populations (e.g., blue stragglers, sub-subgiant branch stars, etc.), multiple star systems (especially triples)
Hope to do: Start new projects related to dark matter and galactic dynamics. Explore disequilibria features in DR2 and simulations. Push forward Local DM density project. Hope to learn: How to find the coldest White Dwarfs and derive temperatures and luminosities (which can be used to search for WIMP DM capture and annihilation). Anything and everything about disequilibria. What I bring: Knowledge and Expertise: Local DM density determinations, direct and indirect DM detection, Jeans modelling and galactic dynamics, Bayesian fits and MultiNest. Code and Data: 2D integrated Jeans code (GravImage2D). Code skeleton and Docker image for MultiNest. N-body mock data sets of MW type galaxy, with and without satellite mergers - old simulations but they have nice spirals, warps, flaring. ...and “Kiwi Goodness” according to the anonymous wit who came across my draft slide. z v Z
Hope to do New astrometric catalog correction for asteroid observations using Gaia DR2 as reference Hope to learn Cross-match between Gaia and external catalogs Issues with DR2 dataset ADQL queries …. As much as possible from all of you! What I bring Asteroids in DR2 and beyond: astrometry, orbits, possible impacts (the funny stuff!) 14099 asteroids in Gaia DR2 Credit: ESA/Gaia/DPAC/ P.Tanga, F.Spoto
chemical tagging with DBSCAN on the DR2 x APOGEE overlap - validation on known DR2 OCs Hope to learn: - methods to handle high dimensional data - new clustering algorithms - more about machine learning! What I bring: - clustering algorithm expertise - help accessing APOGEE data - excitement!
look at the kinematic substructure around the spiral arms, and the ridges in the velocity distribution. Hope to learn: How to get trustworthy distances, and understand uncertainties / biases in the bayesian distance estimates. Working with actions. What I bring: General dynamics knowledge, brief former member of Gaia DPAC radial velocity team. I can also make mock Gaia data from your galaxy models if anyone still want to do that!
VS blurring in the Galactic disk? Hope to learn: Selection function APOGEE-RC x Gaia DR2 Do orbital actions have errors? What I bring: A radial migration model for the Milky Way disk (https://arxiv.org/abs/1805.09198)
multiple stellar systems, dynamical matter density measurements Hope to learn: Everything and Gaia systematics (completeness, selection effects, etc.) What I bring: Experience with Bayesian hierarchical models and astrostatistics
map of young stars in the solar neighbourhood. Hope to learn: clustering algorithms. What I bring: general knowledge of star formation; photometry, ages (isochrone fitting), kinematic modelling of (young) moving groups. l [deg] b [deg] 360 0 -90 90
abs mags vs spectral type relations for subdwarfs. Potentially enlarge the sample of subdwarfs with fundamental parameters. What I bring: How to use the BDNYC Database and SEDkit to determine fundamental parameters of brown dwarfs, knowledge about subdwarfs and brown dwarfs What I want to learn: More about Gaia, what its lower limit is for faint sources, and other cool stuff about python.
and stellar overdensities in the MW. Relations between VOD, HerAq, EriPhe overdensities? Hope to learn: Science that I’m not working on. Dynamical modeling/fitting with 6D or 4-5D phase space info. Cool Science results/plots. What I bring: Anything about DES DR1. LOSV from spectroscopic observations on streams and dwarf galaxies. Cool science results. AAT observing TONIGHT! KICP workshop on Jun 27-29 if you are interested in DES DR1 and other DECam data, and synergy with Gaia. Registration is full but email or talk to me (email@example.com) at the sprint. Appear on arxiv this week
is to continue in the spirit of looking for unique signatures of “non-vanilla” dark matter interactions by looking for distinct morphologies and substructure • I have lots of ideas and my motto is “fail faster.” I think it could be fun to kill some of these ideas or see them develop if we manage not to kill them. • My background is in particle theory and cosmology so I am eager to learn all I can about what can reasonably be done with this dataset-- both in terms of limitations of the data and other complications from the baryonic side • I have experience with Gaia data (DR1) doing a Holmberg & Flynn type analysis to search for a thin dark matter disk ← my face if you want to find me and chat! If you can’t find me my email is firstname.lastname@example.org
to do: Tilt angle of the velocity ellipsoid as a function of height |Z| away from the mid-plane using Gaia DR2. Hope to learn: How to get reliable individual space velocities **and their uncertainties** from Gaia measurements **-covariances-** What I bring: I can be helpful with the latest data releases from APOGEE, GALAH, RAVE and their data products.
out what’s going on with this stuff! Want to learn: How to use Gaia more carefully, more about dynamical modelling, and what is this dark matter thing, anyway? Also, MW Halo anyone?? I bring: - a bit of knowledge about galpy (Fast orbit parameter/ action estimation, coordinate transforms) - A catalogue of actions/angles/orbits/freqs (+uncertainties!) for Gaia RV sample - APOGEE knowledge (selection function!) - Numerical simulations and analysis of them (in the interests of Gaia science!) Would love to collaborate on funky stuff i’ve never done before!
planets orbiting brown dwarfs/low mass stars using the astrometric excess noise and other flags (2) Clean up the 25pc sample (3) Find triple systems as comovers to tight brown dwarf binary systems Hope to learn: The caveats of Gaia data and quality cuts, i.e. what is a real Gaia source and what’s not. Cool python/pandas tricks to make code more efficient. What I bring: lots of brown dwarf and binary systems chatter, non-pretty IDL/Python code to identify and characterize unresolved spectral binaries of brown dwarfs, SPLAT, SpeX spectra, moral support :)
rate as a function of vertical action. Compile metallicities from literature, etc. Convert Jz to age. Do young multis look different to old multis? Learn: what is The Best Way to calculate vertical actions with uncertainties for Gaia stars (and how well can you approximate this for Kepler stars without RVs?)? Bring: methods and code for measuring ages of main sequence stars. All planets Single planets Multiple planets
of dark matter from the stars. Hope to do: Interested in the completeness function of DR2, so I can make the local density distribution of the stars. Hope to learn: More about the measurements of the stars in dwarf galaxies. What I bring: I worked on the velocity distribution of metal poor stars, so always happy to discuss related topics: Mergers of the Milky Way, dynamics of the stellar halo, properties of the stars, etc... I am happy to have any particle physics/dark matter-related topics!
the Cepheid distance ladder using Gaia data. I would also like to help nice people do cool things. I know about model selection & assessment, Stan, MultiNest, PolyChord, likelihood- free inference and the crushing superiority of tea to coffee. Hello my name is Stephen Feeney and I work at the Flatiron Institute
the extinction curve. Measure extinction rather than reddening, and add Gaia bands. Hope to learn: When to be skeptical of Gaia data. Comparison of Gaia distances and red clump distances show more scatter than I expected; help please! What I bring: Lots of dust. Its 3D structure and properties. Expertise in photometric data sets like PS1, the Legacy Survey, and the DECam Plane Survey. Modeling and analysis of data.
Find M-dwarfs in Gaia DR2. Map the galaxy in spt density and study the mass function. Estimate what percentage of M-dwarfs SDSS observes. Want to Learn: Cares when working with Gaia data (flags, parallaxes, etc.). Can help with: Gaia Data Queries and cross-matches.
Checking the asteroseismic scaling relations using Gaia parallaxes. • Anisotropy profile and dynamical modelling of the stellar halo using SEGUE data and GAIA DR2 proper motions. • Fast methods to compute better distances from GAIA DR2, making use of prior information from isochrones and or proper motions. Want to learn: New ideas in Bayesian Nonparametric modelling, neural networks and proper treatment of uncertainties in GAIA DR2. What I bring: • A new improved version of BMCMC, an MCMC code for Bayesian data analysis with ability to solve hierarchical models. • Expertise with, GALAH , mock catalogs (Galaxia), selection function of GALAH, RAVE, Kepler, K2GAP, Bayesian estimation of stellar parameters. • Efficient handling of large data sets with EBF file format.
mock catalogs generated from the Latte simulations. • Simulation insight in radial migration, thick disk formation, dark matter distribution, & stellar halo dynamics. Hope to learn (from you!): • How to select, crossmatch, and understand errors for BHB stars. • Ages of GALAH stars? Would love to hear about the Cannon applied to this sample. Hope to do: • Poke at the stellar halo’s velocity anisotropy profile (β) for different metallicity stars • Explore connections between chemistry & kinematics in disk via GALAH-Gaia & hunt for radially migrating stars. 3 color image from Ananke (m12i)
Gaia DR2 and APOGEE Elena D’Onghia & Boquan Chen University of Wisconsin-Madison Questions: • How does star formation occur in open clusters? In dense regions or hierarchically? • How do open clusters evolve and dissolve? • Can we identify new open clusters in the Milky Way disk? What we Bring: Customized Shared Nearest Neighbors (SNN) clustering and other clustering algorithms in Python, Insight on simulations of dynamics What we hope to learn: whether we can learn about stellar ages and masses and how to better constrain star forming regions What we hope to do: find new open cluster candidates based on our algorithm.
to learn: What I bring: - (Hierarchical) Bayesian models based on the Gaia measured quantities (e.g. parallaxes, proper motions and photometries) - kinematic and dynamical properties of the Galactic disk, local dark matter density - looking for N-body simulations with dwarf galaxy perturbations so that we can compare with our observed result in the outer disk LAMOST data and our latest view of the Galactic outer disk structure extending to 20 kpc (1804.10485), the anisotropy of the stellar halo (1805.04503) and the rotational stellar halo (1805.08326) Distributions of the mass-ratio (q) of binarities as a function of metallicity and mass of primary stars (m). As a first step, I will firstly focus on the unresolved solar-like binarities (with shorter periods) from LAMOST+Gaia within a few hundreds parsecs. Then I will extend to longer periods by searching wide binaries in the similar volume. q m1
for astrometric, weak gravitational lensing by dark matter substructures in the Milky Way, using templates for the lensing-induced proper motion of background sources. Hope to learn: Real and fictitious proper motion correlations in DR2; covariance in astrometric parameters of nearby sources; how to deal with extreme outliers in the astrometric data. What I bring: Particle physics background, expertise in microphysics of dark matter models.
mass ratio distribution, dependence of binary fraction on metallicity and stellar population. Wide binary disruption limit. Also: I’m interested in removing GDR2 parallax systematics. Hope to learn: Stuff What I bring: Access to a large SQL database with major surveys (ask me) Some expertise in SQL/Python/Stan/Gaia log10(Separation/1pc)
What I bring: Characterize the stellar stream of Palomar 5 in Gaia and search for the potential extension of its leading arm The science being done with Gaia data and to swap “stream- finding” strategies. Knowledge on galactic dynamics and stream modeling techniques Left: The Pal 5 stream as seen in Pan-STARRS. Right: Pal 5 evolved in potential with a rotating bar. Red circles: potential extension of the leading arm.
learn: What I bring: • Determine empirically how color and metallicity effect the abs. Mag of RC • Do Chemical Cartography/tag in various elements (e.g. Ba seems to be enhanced in outer galaxy) GMM in STAN; Best distance/velocity practices Expertise in spectroscopic surveys / abundances/RC stars and standard candles 2 1
learn: What I bring: • M31 proper motion - writing (leads: David Hendel and Andy Tzanidakis) • Contributions to ◦ Pal 5 interpretation (lead: Sarah Pearson) ◦ Outer disk projects (leads: Chervin Laporte, Adrian Price-Whelan) ◦ abundances/dynamics (leads: Keith Hawkins, Moiya McTier, Melissa Ness) More about disk sub-structures - properties and intuition for interpretation Enthusiasm! Experience: dynamics of streams and outer disk; populations and abundances.
learn: What I bring: Finish paper on Anticenter d~10 kpc and start modeling ACS: 1. Improve current Density/PM maps of Galaxy d~10 kpc using MSTOs, RC, RGB (see also Adrian leading work on extending maps as a function of distance) 2. Use GOG/Galaxia remove contaminants in current maps -> better interpret/dissect signal on global breathing signal of disc + signs of spiral arms towards Galactic center & kinematics of substructure in Monoceros. 3. Fitting a model to the ACS “stream” (tidal tail) Bayesian methods to fit orbit to streams Many results on density + pml, pmb maps in MSTOs, RCs, RGBs for disc at 10 kpc + selections of individual tracers (RC+RGB) along ACS over 160 degrees Numerical simulations to interpret results/signals (useful maybe for other people working on disc perturbations) MSTOs DR2 RCs median mub Simulations MW+Sgr
if far from comfort zone (star formation, exoplanets) ◦ Acquaintances, met very interesting people in 2016 NYC sprint • Want to learn: ◦ How astronomers work with Gaia data (Archive, local DB, Spark?) ◦ How to improve the Archive to better serve community ◦ Hot topics after DR2 ◦ Prepare for Gaia ESAC exploration lab • Plan to bring: ◦ Gaia Archive and hardware expertise ◦ Enthusiasm, amazement
Time Resolved Stellar Parameters & Chemical Abundances for high precision Variable Stars with APOGEE data (it works! see right) -- try to estimate Extinction from DIBs. ◦ Think about Baade-Wesselink method in the light of Gaia. ◦ Convince folks here to make their awesome codes run on APOGEE DR14 into official Value-Added-Catalogs for APOGEE DR16! (ahem, Kareem, Ted, Adrian, Yuan-Sen, among others ... ) • I need to learn: ◦ Proper way to think about Gaia systematics for luminosity calibration. ◦ How you use APOGEE so that we can make it better, stronger, friendlier, better documented, better calibrated, <your gripe here> • I bring: ◦ Scary amount of knowledge about APOGEE (esp. known problems in DR14) ◦ Lots of thinking/doing/observing to prepare for the (Extra)Galactic Distance Scale as anchored by Gaia. Co-Chair, APOGEE-2 Science Working Group Surface Gravity log(g) Metallicity [M/H] Effective Temperature
binary systems from Gaia data (RV error, lack of RVs, photometry, astrometric noise). There are millions of binaries from any of these data! Binarity in the field/clusters; w.r.t. stellar [Fe/H]; stellar-mass black hole companions. No fear in asking dumb questions. Expertise in data analysis, stars, etc. How should I responsibly interpret Gaia housekeeping data? learn bring
in the halo, star formation history of the galaxy, work with awesome people. Hope to learn: everything cool being done with Gaia! Best practices and pitfalls. All of this “Bayes” stuff people keep talking about. What I bring: lots of (probably dumb) questions, mediocre coding, ML, and data viz (d3) skills.
outer Galactic disk? Monoceros, Tri-And, warps, flares, etc.; what are all these things, and why are they there? Use proper motion + every other useful parameter to dissect the disk. A mild obsession with making all-sky maps of the MW, and the infrastructure to do so. Hope to learn: Hope to do: What I bring:
there a Vertical Acceleration Relation?” by Comparing local solutions of the Jeans and Poisson Equations I bring to the Sprint a background in Dark Matter pheno McGaugh et. al. The Radial Acceleration Relation
measure the proper motion of Andromeda using PS1/2MASS-selected stars (w/ Kathryn Johnston, Andy Tzanidakis, you???) Hope to learn: how to build a mixture model for M31+contaminants, best practices for selection from CMDs What I bring: galactic and stream dynamics knowledge, lots of minor merger N-body simulations Also interested in: streams, substructure, the stellar halo, RR Lyrae Andy Tzanidakis
amount of dust outside disk with RR Lyrae. Hope to learn: I am very interested in the bifurcation of Sagittarius Stream, structures and dynamics in Milky-way etc. Gaia parallax systematics. Purity and completeness of the RR Lyrae sample. What I bring: some knowledge of RR Lyrae, enthusiasm to topics of dynamics
stream (other streams?) Search for streams around GCs Search for extended debris (dwarfs) around GC streams LEARN: Gaia systematics Improved isochrone selection Things I don’t know BRING: Orbits in the presence of the LMC Orbits marginalized over MW potential uncertainties Orbit/stream modelling of streams Knowledge of effect of substructure on tidal streams N-body simulation library/expertise Pal 5 in GDR2 Ophiuchus stream
lensing / velocity anomalies 2. Join in your cool project idea! 3. Find applications of machine learning 4. Hang out and have fun learning new things. Bring: 1. ADQL / SQL magic tricks in accessing gaia data 2. One weird trick with the machine learnings 3. Know the ways of the python. 4. Can make pretty plots. 5. Inspiring karaoke game.
how is it moving? What does that mean? How far can we trace it? ➢ Other streamy stuff: detect tails from known globulars? Good proper motions and clean evolved-star CMDs for existing streams? Measure solar motion? Hope to learn: Our current map of the halo. Efficient methods for large-area studies. How to use variable stars from DR2 (LPV/Miras, RRL). And so, ad infinitum... What I bring: some dynamics and statistics. An attempt at suppressing proper motion systematics.
as a function of color • Compare nearby open cluster morphology (... or possibly something else) Semyeong Oh (Princeton) /se-mjʌŋ/ Learn: • stellar multiple (binaries – star clusters) formation and disruption • Gaia DR2 limitations Bring: • experience with data analysis / visualization tools