tag:blogger.com,1999:blog-10448119Thu, 26 Nov 2020 10:05:22 +0000talkingstarmodelbayesdatastatisticsseminarimagingspectroscopywritingcodeexoplanetsdssgalaxyMilky Waymeetingtimedynamicsastrometrypracticecalibrationnot researchgaiaphotometrycosmologychemistrykinematicsradial velocityKeplerinformationgraphical modelmathematicssubstructureMCMCliteraturemachine learningoptimizationquasarTheCannonproposalgalexGaussian processtelescopelinear algebraphilosophybinary stardisknoisevisualizationcomputingdecisionfundinglarge-scale structuregravitational lensingstar formationtraveldark sectorcatalogregressiongravityblack holeLTFDFCFasteroseismologyclusteringsupernovapoint-spread functionHARPSdustthinkingproper motiontheoryclassificationhalocausationhardwaresearchLSSTlifeTESSatlasreadingSolar Systemtractorclusterparticle physicsspitzermeta datanucleosynthesispanstarrsexperimentcosmographymicroscopygastrophysicsinterstellar mediumweb 2.0white dwarfelectricity and magnetismCDMHSTengineeringmergingopen scienceradioEXPRES2masstestingwiseintergalactic mediumobservingTerra HuntingEuclidpulsarGALAHbaryon acoustic featureEarthdatabasecometeatingfundamental astronomyproject managementpoliticsLAMOSTbrown dwarfcitizen sciencephase spaceFermiemailtransparencyHMFPHATPlanckenvironmentplanetrobotLIGOinterferometryparallaxHerschelminor planetthresherSungamma-ray burstthermodynamicsatomic physicscosmic rayprimusravesignal processingultravioletcharge-coupled devicecompressed sensingdesignevolutiongeometryroweisneurosciencenuclear physicsdigital camerahipparcosamateurdiffractionquantum mechanicsrefereeingtextWFIRSTaccretionarchivebiologyeducationinflationinterpolationarchetypearchitecturedeep learningdiscussiondrinkinghistoryopticsrelativityusno-bAPIJWSTanthropiccausalityhackingmusicossssciencestring theoryastrobiologydissertationintelligencespherexweatherLHCWMAPad hockeryanthropologyclimatecoffeecorrelationeditinggeologypipelinepost-starburstreproducibilityALMABartP1640apasscompressionconfusiondemographicsdiagnosisfailfarm machineryoutreachscatteringsocial mediasonificationx-rayChandraLISAMoonNuSTARPTFadministrationballoonbullshitdaftflickrgame theorylearningnasaplasmapressvlt-sphereASASSNDESIKNNLMIRcamSVMVLAWillman 1ZTFadviceaskapchaos monkeyclothingdragonflyemotionsethicsethnographyexomoonfrequentismfrisbeegamblinggamehandicappinglawmakingphonepolarizationpolemicrantregretsemanticssoundswiftukidssvirtual observatoryvolcanismweaponsHogg's Researchgalaxies, stellar dynamics, exoplanets, and fundamental astronomyhttp://hoggresearch.blogspot.com/noreply@blogger.com (Hogg)Blogger3704125tag:blogger.com,1999:blog-10448119.post-1539154707907480602Sat, 21 Nov 2020 19:40:00 +00002020-11-21T14:40:03.466-05:00catalogdiskgaiaMilky Waystarstar formationstatisticstalkingwhite dwarfwritingselection function and white dwarfs<p>Hans-Walter Rix (MPIA) and I worked on our project to explain, elucidate, and determine the selection function (for ESA <I>Gaia</I> and other surveys today. We decided that a good toy problem is the luminosity function of white dwarf stars. This is a good toy problem because the selection function is necessary, but they aren't so far away that the full three-dimensional dust map is required. I wrote words about this problem in a latex document to get us started.</p>http://hoggresearch.blogspot.com/2020/11/selection-function-and-white-dwarfs.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-2716685575126319210Wed, 18 Nov 2020 17:19:00 +00002020-11-18T12:19:06.054-05:00computingcosmologyliteraturephilosophytalkingthe physics of "do we live in a simulation?"<p>I had a good call with Paula Seraphim (NYU) today, who is doing a literature search on the physics relevant to the question “Do we live in a simulation?”. She found the classic papers by Dyson and by Frautschi in the 1980s on information processing in the expanding universe, and by Feynman on whether one quantum system can exactly simulate another. Meanwhile (really yesterday), I have got ready a class to teach for Blakesley Burkhart (Rutgers) about this subject: What does a physicist (as opposed to a philosopher, say) have to say about this question?</p>http://hoggresearch.blogspot.com/2020/11/the-physics-of-do-we-live-in-simulation.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-6795205461222679820Sat, 14 Nov 2020 04:59:00 +00002020-11-25T10:45:58.000-05:00gaiageometryhardwareobservingproposalsdssspectroscopystarwritingopen-fiber proposal<p>The <i>SDSS-V</i> project uses robot fiber positioners to take millions of short (15-ish minutes per visit) spectra in the visible and infrared. Because of the geometric constraints of the fiber positioners, and the targeting, there will be many, many unusued fibers—meaning, many opportunities to add additional spectroscopic targets! The project issued an internal call for proposals for the open fibers. Today I spent time writing one, which is very simple: It is to fill out the unobserved parts of the ESA <i>Gaia</i> color-magnitude diagram, but targeting stars for spectroscopy that do not already have a nearby star with a spectrum. The word “nearby” implies a resolution (how nearby?). The proposals are due in a few days and we still don't know exactly what our resolution should be! Also, do we treat variable stars differently from non-variable stars? We have work to do!</p>http://hoggresearch.blogspot.com/2020/11/open-fiber-proposal.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-8608147231114585929Fri, 13 Nov 2020 04:59:00 +00002020-11-24T11:01:26.603-05:00causationcomputingdataexoplanetmodelradial velocityregressionspectroscopytalkingTheCannontimecomplexifying a residuals model for EPRV<p>Lily Zhao (Yale) has built (what I call) a generative model for the residuals (in the spectral domain) of the spectra taken by <i>EXPRES</i> for a magnetically active star away from the mean spectrum. This generative model takes the pipeline-generated radial-velocity as the label that generates the residuals. Then we do inference to find out whether spectral-shape variations predict or can correct pipeline-generated radial velocities. This model is very conceptually like <a href="https://arxiv.org/abs/1501.07604"><i>The Cannon</i></a>. Today we generalized this model to take two or more pieces of meta-data or labels that can be used to generate the residuals, and then still do inference to correct the radial velocities. We'll see if it helps. My intuition says it does help.</p>http://hoggresearch.blogspot.com/2020/11/complexifying-residuals-model-for-eprv.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-623774922112038006Thu, 12 Nov 2020 04:59:00 +00002020-11-18T13:46:03.984-05:00bayescomputingmachine learningpracticeregressionseminarstatisticstalkingpreparing a NeurIPS tutorial<p>In December (the 7th, to be precise), Kate Storey-Fisher (NYU) and I will be giving a tutorial at the <i>NeurIPS</i> Conference. Our tutorial will be on on machine learning and astronomy. Ordinarily we'd get slides ready and be ready to present but two things are making our preparation harder: One is that we want to deliver not just content, but also working Jupyter notebooks that show the participants how to get and use astronomical data of different kinds. The other is that the online format means that we need to pre-record parts of our tutorial. That's daunting, somehow! We spent a good part of today discussing content, scheduling, and making slides.</p>http://hoggresearch.blogspot.com/2020/11/preparing-neurips-tutorial.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-1047178215803346296Wed, 11 Nov 2020 04:59:00 +00002020-11-18T12:23:51.112-05:00codediskdynamicsgaiakinematicsliteratureMilky WayoptimizationsubstructurethinkingOort constants and the first-order MySpace<p>Today we realized or re-realized that the linear terms in our <i>MySpace</i> project (the project to find the coordinate transformation that maximizes the informative-ness of the velocity sub-structure in the Milky Way disk) are just <a href="https://en.wikipedia.org/wiki/Oort_constants">the Oort constants</a>, or a generalization thereof. But since they maximize the velocity structure, they don't necessarily mean, for us, what Oort expected them to mean. We also got the method working to first order. Or I should say that Adrian Price-Whelan (Flatiron) did. He made use of <i>jax</i>, the auto-differentiation package for <i>Python</i>. It's impressive.</p>http://hoggresearch.blogspot.com/2020/11/oort-constants-and-first-order-myspace.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-352133265071730029Tue, 10 Nov 2020 04:59:00 +00002020-11-16T10:21:49.114-05:00Gaussian processlinear algebramachine learningmodelregressionstatisticswritingwriting down the linear regression GP relationship<p>My loyal reader knows that I have been working on the fundamentals of linear regression for a bit now. Today I did some writing on this topic. Last week, Soledad Villar (JHU) and I got the point that we could write down a specific case where the limit of infinite features in a particular, carefully designed linear regression becomes exactly a Gaussian Process with a particular, carefully chosen kernel. I understand how to generalize this result partially, but not completely: Apparently this will work in an infinity of different bases, with an infinity of different weighting functions or kernels. My goal is to write something pedagogical and useful for practitioners.</p>http://hoggresearch.blogspot.com/2020/11/writing-down-linear-regression-gp.htmlnoreply@blogger.com (Hogg)1tag:blogger.com,1999:blog-10448119.post-7000360495724456825Sat, 07 Nov 2020 04:59:00 +00002020-11-13T11:28:05.098-05:00cosmologyGaussian processlarge-scale structuremachine learningmeetingproject managementregressiontalkinghuman aspects of data analysis<p>We had a fun data group meeting today, in which we discussed many human aspects of data analysis (like asking questions in talks and seminars, and sharing work when it is in pre-publication status). I spoke about the connection between Gaussian processes and linear fitting with enormous numbers of basis functions; there is a limit in which they become identical, which is awesome. Group meeting was followed by a conversation with Storey-Fisher about what we are going to work on next: Pulsar timing? Looking for anomalies in large-scale structure? Intensity mapping?</p>http://hoggresearch.blogspot.com/2020/11/human-aspects-of-data-analysis.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-2014768728861753515Fri, 06 Nov 2020 04:59:00 +00002020-11-13T09:19:48.465-05:00machine learningnot researchtalkingwritingtalking about writing<p>It was a low-research day today. But I did have a great conversation with Viviana Acquaviva (CUNY) about the textbook she is writing on machine learning for advanced undergraduates in the natural sciences.</p>http://hoggresearch.blogspot.com/2020/11/talking-about-writing.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-1408413030992042370Thu, 05 Nov 2020 04:59:00 +00002020-11-12T13:31:14.443-05:00linear algebramachine learningmathematicsregressionstatisticstalkinga Gaussian process is a limit of linear regression<p>Today Soledad Villar (JHU) and I completed a problem I've had open for literally years (I think I first worked on it in AstroHackWeek 2017): Does a linear fit become a Gaussian process when the number of components (parameters) goes to infinity? The answer is <i>yes</i>! But you have to choose your features very carefully, and take the limit (to infinite features) sensibly. But if you meet those conditions it works, and the kernel function for the GP becomes a Fourier transform of the squares of the amplitudes of the features. That is, the kernel function in real space is the Fourier transform of the power spectrum in fourier space. There are many details I don't yet understand, but we got it working both theoretically (on paper) and numerically (on the computer).</p>http://hoggresearch.blogspot.com/2020/11/a-gaussian-process-is-limit-of-linear.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-5664224994341453877Wed, 04 Nov 2020 04:59:00 +00002020-11-06T16:34:00.893-05:00dataexoplanetEXPRESmeta datamodelradial velocityspectroscopystarwritingwriting philosophy about EPRV<p>Lily Zhao (Yale), Megan Bedell (Flatiron), and I are working on a project to look at stellar spectral variations in extreme-precision radial-velocity spectroscopy, with <i>EXPRES</i> data. Do these stellar spectral variations tell you anything about stellar noise that distort radial-velocity measurements? This project is very specific and technical, but it connects to some deep ideas in velocity measurement:</p>
<p>In principle you can only precisely measure radial-velocity <i>changes</i> in a star, never the precise absolute or systemic radial velocity. But this precision argument depends on having a constant spectrum. If the spectrum varies, there is no rock to stand on. So this project requires some philosophical backing, I think. I tried to write some of that down this morning. I love stuff like this!</p>http://hoggresearch.blogspot.com/2020/11/writing-philosophy-about-eprv.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-3626484905175170149Tue, 03 Nov 2020 04:59:00 +00002020-11-06T16:22:45.797-05:00diskdynamicsgaiakinematicsMilky Waysubstructuretalkingtheoryrebooting MySpace<p>Some people of a certain age will know what it means when you say that MySpace is dead. But the statement is wrong! Today Jason Hunt (Flatiron) rebooted <a href="https://hoggresearch.blogspot.com/2019/01/myspace-tagging.html">an old project by Price-Whelan and mine</a> called MySpace. The motivation for our reboot: The upcoming ESA <i>Gaia</i> EDR3.</p>
<p>The idea is to figure out how the velocity-space structure (the moving groups, as it were) in the local disk varies with position, with a data-driven model, and then interpret the variations with position in terms of dynamical properties of the Milky Way. Hunt's innovation is to apply this same procedure to simulations as well as data, and use the output to classify the velocity substructure (classify as in: Does it come from resonances or disrupting clusters or what?). We discussed some of the math and optimization involved. Because we phrase this as the fitting of an expansion.</p>http://hoggresearch.blogspot.com/2020/11/rebooting-myspace.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-2045069432039199387Sat, 31 Oct 2020 03:59:00 +00002020-11-04T10:19:20.010-05:00climateclusteringcosmologylarge-scale structurelinear algebraliteraturepracticestatisticswritingKate submits her first first-author paper!<p>We submitted Kate Storey-Fisher's (NYU) paper on estimating the correlation function to the AAS Journals (probably ApJ, but they decide now, not us). I am so excited. It's been a great project and it has beautiful results and—if we can get this method adopted—we will save future missions and projects a lot of compute time. (And therefore reduce their carbon footprints!)</p>
<p><i>[Note added later: <a href="https://arxiv.org/abs/2011.01836">Here is the manuscript</a>.]</i></p>http://hoggresearch.blogspot.com/2020/10/kate-submits-her-first-first-author.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-551723395499089202Fri, 30 Oct 2020 03:59:00 +00002020-11-04T10:20:25.089-05:00Gaussian processlinear algebramachine learningmathematicspracticeregressionseminarstatisticstalkingbig, huge linear regressions<p>I spoke (remotely) at CCA today about linear regression (fitting linear models for the purposes of prediction), when the linear regressions have huge numbers of parameters. Yes huge: More than the number of data points! It turns out that even though you can thread the data perfectly—your chi-squared will be exactly zero—you can still make good predictions for held-out data. That surprised the crowd, which, in turn, surprised me: Many in this crowd use Gaussian processes and deep learning, both of which have these properties: More parameters than data, can fit any training data perfectly, and yet still make good, non-trivial predictions on held-out data.</p>
<p><a href="https://speakerdeck.com/dwhgg/linear-regression-with-huge-numbers-of-parameters">My slides are here</a>. Should I write something about all this?</p>http://hoggresearch.blogspot.com/2020/10/big-huge-linear-regressions.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-1746697699269164285Thu, 29 Oct 2020 03:59:00 +00002020-11-02T10:04:18.762-05:00exoplanetmodelnoiseradial velocitystartalkingBode's-Law noise<p>When we think about finding extra-solar planets from the reflex motions they imprint into stellar radial-velocity data, we think about the problem of noise: There is shot noise, there are spectrograph-calibration offsets, there are imprints of the atmosphere, there is surface convection on the star and asteroseismic modes, there is magnetic activity, flaring, and so on! It's a mess. But there's also noise from <i>other, unmodeled and undiscovered planets</i>. That is, the other things orbiting the star, other than the planet of interest.</p><p>Today, Winston Harris (MTSU), Megan Bedell (Flatiron) and I came up with a plan for inserting this planetary-system noise into Harris's simulations of radial-velocity data. The question arose: What periods to use for the planets? And Bedell suggested that we adapt <a href="https://en.wikipedia.org/wiki/Titius%E2%80%93Bode_law">the Titius–Bode law</a>! Hilarious. This gives us an extra-solar system architecture that makes sense, and simultaneously trolls anyone reading our paper.</p><p><i>[Insert here obligatory objection to naming things after people.]</i></p>http://hoggresearch.blogspot.com/2020/10/bodes-law-noise.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-1063931532881583037Wed, 28 Oct 2020 03:59:00 +00002020-11-02T09:58:25.657-05:00clusteringcosmologylarge-scale structurelinear algebraliteraturestatisticstalkingwritingfinishing a paper is hard!<p>I spent research time today with Kate Storey-Fisher on the final details in her new paper on a continuous-function estimator for the 2-point correlation function. It removes binning from the estimation (all current estimators bin), and makes the results far less computationally costly to interpret.</p>http://hoggresearch.blogspot.com/2020/10/finishing-paper-is-hard.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-5475818725932076837Tue, 27 Oct 2020 03:59:00 +00002020-11-01T21:17:31.112-05:00meetingnot researchpracticeproject managementCCA leadership retreat<p>This afternoon, David Spergel (Flatiron) and the CCA group leaders (and some friends) had a retreat to discuss long-term mission and plans. Given the global sitch, this retreat was only partially in-person. We discussed the plans of all the groups, and whether there is a coherent, cross-cutting mission statement that could be adopted by the full CCA. I think there is! We also discussed the structure of the organization, and how we want to be organized in the future. Not research, maybe? But in support of research, in the long run.</p>http://hoggresearch.blogspot.com/2020/10/cca-leadership-retreat.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-4741397343409916692Sat, 24 Oct 2020 03:59:00 +00002020-11-01T21:14:18.283-05:00exoplanetEXPRESradial velocityregressionspectroscopystartalkingTerra Huntingtimespectral-shape influences on radial-velocity measurements<p>As my loyal reader knows, I have been interested in finding out how radial-velocity measurements of stars (for, say, exoplanet discovery) are affected by shape changes in the stellar spectrum. Today Lily Zhao (Yale) had a breakthrough: She did regression of residuals away from a constant-spectrum fit to radial-velocity data, and showed that the residuals can be used to predict the radial velocity! That is, she can show that stellar spectrum shape predicts measured radial velocity, over and above the expected Doppler shift. And she did this with proper cross-validation, so the result looks solid. I'm stoked! I need to write down some theory this weekend.</p>http://hoggresearch.blogspot.com/2020/10/spectral-shape-influences-on-radial.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-3462106916365676779Fri, 23 Oct 2020 03:59:00 +00002020-10-28T09:32:39.590-04:00bayesbinary stargaiaphotometryradial velocitysdssspectroscopystartalkingtimeradial-velocity vs photometric variability at short periods<p>Jonah Goldfine (NYU) is looking, with Adrian Price-Whelan (Flatiron) and me, at short-period binary star systems in the NASA <i>TESS</i> data. We find that most of the short-period binaries that Price-Whelan finds in the <i>APOGEE</i> radial-velocity data have interesting variability in their photometry in the <i>TESS</i> data. Today we compared light curves folded on the Price-Whelan period found by <i>The Joker</i> with light curves folded on the period found with the Lomb-Scargle periodogram. There are lots of stars where <i>The Joker</i> gets the period better than the light curve, which is surprising, since we are validating with the light curve! There are so many kinds of variability to consider. Goldfine is going to start with ellipsoidal variations, I think.</p>http://hoggresearch.blogspot.com/2020/10/radial-velocity-vs-photometric.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-2139229637990171331Thu, 22 Oct 2020 03:59:00 +00002020-10-28T09:27:45.145-04:00astrometrybayesinformationmachine learningmeetingphotometryradial velocityregressionspectroscopystartimeforwards vs backwards predictions; astrometry from rv<p>Gaby Contardo (Flatiron) and I are asking whether you can predict the next data point in a stellar light curve from the last N data points or whether you can post-dict (is that a word?) the previous data point in a light curve from the next N. The results are surprisingly rich. She showed some of them in Stars and Exoplanets Meeting today.</p><p>In that same meeting I showed my attempt to measure the astrometry (celestial position and proper motion) of a star from the radial-velocity variations you see on an Earth-bound observatory. It is surprisingly precise! But not as precise as direct astrometric measurements.</p>http://hoggresearch.blogspot.com/2020/10/wednesday-21-forwards-backwards-and.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-8864320153423651641Wed, 21 Oct 2020 02:59:00 +00002020-10-27T07:55:14.168-04:00astrometrycalibrationfundingimagingmachine learningproposaltalkingweb 2.0funding astrometry.net as an open-source project<p>Dustin Lang (Perimeter) called me today and alerted me to <a href="https://science.nasa.gov/researchers/sara/grant-solicitations/roses-2020/amendment-59-release-final-text-e7-open-source-tools-libraries-and-frameworks">this NASA funding call</a> related to open-source projects. He argued that we need to take <a href="http://nova.astrometry.net/"><i>Astrometry.net/</i></a> to the next level. I agree! So we kicked around project and development ideas and vowed to take a stab at a letter of intent.</p>http://hoggresearch.blogspot.com/2020/10/funding-astrometrynet-as-open-source.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-1604309721270854956Tue, 20 Oct 2020 03:59:00 +00002020-10-25T12:52:55.186-04:00calibrationdesignexoplanethardwaremeetingproject managementradial velocityspectroscopyTerra HuntingTerra Hunting LFC adoption<p>Today was a board meeting for the <i>Terra Hunting</i> collaboration. It was an important meeting, because in it, we more-or-less approved adding a laser-frequency comb to the required hardware of the project. This increases our budget, and brings in new partners. But I'm a huge fan (or I should say “<i>And</i> I'm a huge fan”), because our new partners will be awesome, and, just as important, as Lily Zhao, Megan Bedell, and I have shown, the LFC is great for constructing a fully data-driven, non-parametric calibration method for the spectrograph.</p><p>Calibration paper to appear on arXiv soon!</p>http://hoggresearch.blogspot.com/2020/10/terra-hunting-lfc-adoption.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-8513101066291492312Sun, 18 Oct 2020 03:59:00 +00002020-10-25T21:37:45.183-04:00bayescodelinear algebramathematicspracticestatisticsfast multivariate-Gaussian evaluations<p>In <a href="https://arxiv.org/abs/2005.14199">our paper on factoring products of Gaussiasn</a>, we recommend using the matrix inversion lemma (and we should have recommended the matrix determinant lemma too) to speed calculations. This weekend I wrote the most efficient <tt>numpy</tt> code I could to implement the log-Gaussian formulae in that paper. Here were my rules: <i>Never</i> construct a zero matrix or a sparse matrix using <tt>diag()</tt> or anything else that makes a container of mainly zeros. <i>Never</i> use <tt>inv()</tt>, only <tt>solve()</tt>. <i>Always</i> store and re-use elements of the calculation, especially those shared between the inversion and determinant lemmas. In the end I got a factor of thousands in speed over naive implementations, which themselves were hundreds of times faster than just constructing the matrices and operating on them.</p><p>That was fun, and a rare moment of pure coding for me.</p>http://hoggresearch.blogspot.com/2020/10/fast-multivariate-gaussian-evaluations.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-3734903296661049464Sat, 17 Oct 2020 03:59:00 +00002020-10-25T12:52:55.189-04:00bayesbinary starexoplanetmathematicsradial velocitysignal processingspectroscopystarstatisticsTerra HuntingtimeLombâ€“Scargle and The Joker are the same at zero eccentricity<p>As my loyal reader knows, our <a href="https://thejoker.readthedocs.io/en/latest/">radial-velocity companion-search code called <i>The Joker</i></a> is a brute-force least-square fitting (plus some Bayes) of an elliptical orbit at every possible period, orientation, and phase. In astrophysics, <a href="https://docs.astropy.org/en/stable/timeseries/lombscargle.html">the Lomb–Scargle periodogram</a> is a workhorse tool that, under the hood, is a brute-force least-square fitting of a sinusoid at every possible period and phase. So these two ideas are fundamentally incredibly similar. And indeed, today Winston Harris (MTSU) demonstrated quantitatively that these are the same, and will become identical as eccentricities go to zero. That's interesting, because if we are doing companion search and we don't mind making the approximation that eccentricities are zero, the fitting of sinusoids (even at arbitrary phase, because: trig identities) is way, way faster than the fitting of Kepler functions.</p>http://hoggresearch.blogspot.com/2020/10/lomb-and-joker-are-same-at-zero.htmlnoreply@blogger.com (Hogg)0tag:blogger.com,1999:blog-10448119.post-858269098168818621Fri, 16 Oct 2020 03:59:00 +00002020-10-21T11:40:24.459-04:00cosmologydatadesignexperimenthardwareinformationpracticeproposaltalkingnext-generation cosmology?<p>Today Juna Kollmeier (Carnegie) convened a tiny meeting with Dalal, Percival, and me to discuss the next generation of cosmology missions and projects. We wandered around metrics, around data-analysis methods (forward modeling?), and new hardware, but didn't come up with much specific to say, yet. But Kollmeier is right to be thinking forward, because the landscape is changing and the most interesting objects of cosmological research are evolving on time-scales shorter than project execution. Of course that's always the case for interesting disciplines!</p>http://hoggresearch.blogspot.com/2020/10/next-generation-cosmology.htmlnoreply@blogger.com (Hogg)0