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you

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[Music]

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yes Oh Thank You Ravi for that excellent

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introduction so when we when we think

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about think about detecting bio

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signatures we often think about okay so

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we we're going to disc average that

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planet where we're going to get a disk

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average spectrum we're going to put that

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spectrum that spectrum is going to get

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analyzed by our telescopes and we're

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going to recover that spectrum it'll

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have evidence of absorbing molecules

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perhaps that have a Rayleigh scattering

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tale which will tell us something about

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atmospheric math and those absorption

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features will fingerprint certain

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molecules and we'll use the evidence of

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those molecules to infer things like the

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presence of life oxygen maiden for

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oxygen takoto synthesis or surface

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features from the vegetation vegetation

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like earth or perhaps other types of

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pigments we heard about in the previous

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session I think we often tend to think

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of the spectra as sort of out there and

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static and you know the features are

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there and maybe we'll have the

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signal-to-noise to get them or maybe

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maybe they don't but one complication to

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think about is the influence of

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time-dependent properties of the

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planetary spectrum so as you're

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integrating that disk average spectrum

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it's going to take a long time the

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planets going to be rotating it's going

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to be going through phases and there are

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a lot of properties that are going to be

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dependent on they're going to change

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with time including surface fractions of

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different surface types so surface

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heterogeneity like land and ocean cloud

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heterogeneity we talked we talked about

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how important clouds were and the

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stochastic city of cloud cover there are

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other effects like phase reddening

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forward and back scattering from clouds

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your effective path links to the

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atmosphere will change if you're looking

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at Crescent phase or if you're looking

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at quadrature gibbous phase and those

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all affect you know your your

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signal-to-noise that you can recover so

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this image to the right this rotating

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earth is actually part of our model

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that's used to generate the spectral

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Earth database every pixel is a spectrum

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and so the the blue the blue pixels look

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like ocean and then the you know white

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pixels look like clouds and the sort of

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brown pixels are land surfaces and all

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those pixels are integrated together

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into our disk average spectrum and we

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can do this at several different

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Lucian's and so on the right is the

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highest resolution that's number seven

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and then and then number one is the

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lowest resolution we're adding something

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all these up to get our disc averaged

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spectrum and the more pixels we use the

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more the higher fidelity we can actually

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represent the spectrum of the earth and

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we can do this for two different phases

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so for example here it's gibbous

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quadrature and Crescent phases this kind

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of spans that amount of phases that the

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range of phases will actually be able to

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deserve with direct imaging telescopes

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because of interworking angle

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constraints and angular separation

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between the planet and the star on the

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right you can see here clearly and

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thankfully it shows up on this projector

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you can see this glint spot it's very

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beautiful but this is really in the

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model the model is doing glint so what

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does this look like spectrally so here's

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a one hour cadence earth rotating

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through 24 hour period and I've noted

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the major absorbing features from water

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from oxygen from ozone and Rayleigh

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scattering and the you see a lot of

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lines there that's those are solar lines

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that are reflected back and the initial

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spectrum is a very high resolution that

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we can degrade it to progressively lower

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resolutions now look what happens to the

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spectrum when I go to Crescent phase

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okay okay so you can see on the on the

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Left axis the spectrum is a lot dimmer

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but it's also a different color it's a

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lot redder the Rayleigh scattering tail

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went away relatively the the

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near-infrared is a lot brighter and the

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variability is a lot bigger too because

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you have a smaller fraction of the earth

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that fraction is covered by ocean or

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land or clouds that has a bigger

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temporal impact okay and so we can do

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this with varying viewing geometries too

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so here's a mid latitude pixel and you

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can see Africa and North America then

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come into view equatorial at a

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quadrature and then pull on this is a

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the South Pole and you can see

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Antarctica kind of peeking out and then

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South America and Australia coming in

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and so each pixel here again as a

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and so how do we doing that well we're

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doing the full radiative transfer for

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each for each individual pixel that

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includes absorption that includes

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rayleigh scattering and we do that for

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each time it's kind of surface type each

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kind of cloud type and we use blind

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intensities from high trend including

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the gases that are listed here water

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carbon dioxide oxygen ozone methane

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carbon monoxide and nitrous oxide so

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biosignature gases included and then we

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use data products from a suite of NASA

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of NASA's Earth observing satellites so

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those data products are publicly

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available and they allow us to get

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things like the mixing ratios of

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different gases as a function of

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latitude and longitude and so this plot

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represents for one pixel over the range

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of data of dates that we've modeled so

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this extends a month as seen from from

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from the moon so earth is recapitulating

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all the phases that you would see as an

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exoplanet with a with an edge on

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inclination so this is one pixel this is

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a range of how these gases vary with

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time over one month and then on the

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right here's all of the pixels of the

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earth and all the times so what's the

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full range of the gas at each altitude

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and so this is all being self

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consistently this is the real earth real

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real retrievals being inputted into the

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model and then we can also get the

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surface coverages so that the ice extent

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is a NASA data product so we can get

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that and the surface cloud the cloud

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coverage extents and positions we can

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get that so when you saw those movies

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with the clouds in there those are the

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real cloud positions for the date and

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time we modeled in the real clouds

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thicknesses so the left is the clear sky

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fraction of the different surface types

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we've modeled and then the right is the

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different cloud types and then this is

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the observing window we've modeled and

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how you know you oscillate the fraction

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of each surface type you're viewing

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because you have one viewing angle and

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you kid this is ground truth so and

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we're later testing these we can go back

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and see what do we put into the model

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and so this this is a snapshot for

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snapshot spectra at four different

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phases at Crescent gibbous quadrature

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and Crescent phases and I've noted up in

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the full face panel major absorbing

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features from different

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asses you can see real quick here that

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as you as you go to Crescent phase of

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course you get a lot less reflected

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light and that's expected your

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mid-infrared of course is more stable

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because the night side of the earth is

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still warm it's still emitting it's

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still emitting light and so the spectra

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of data products we have extend all

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these wavelengths and out from point 1

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to 200 microns so one thing that's been

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mentioned is using broadband photometry

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to try to more encapsulate real quickly

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what what the planet might look like the

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kind of gauge is habitability before you

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spend the observing time on spectra and

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so we did that too we did a you - b b -

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b v- R&R my sight magnitudes here's how

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they change with phase I'm so close to

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full phase and from quadrature to full

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phase you can actually get the

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modulation from the surface at Crescent

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phases you get a lot of phrase resonant

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reddening because you've scattered out

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the blue light and here's a great color

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color plot that's put together by Jake

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Lustig Jaeger and it shows the range of

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colors just for the earth the earth goes

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through as it goes through its phases so

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if we're thinking about surface bio

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signatures and how they change with or

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how they might how they might be

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different how we might identify them we

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also have to consider that there's an

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ABS here above that planet and that's

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going to induce color changes as a

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function of phase and so there's going

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to be a spread in colors depending on

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when you look at the planet so you have

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to consider that but those that spread

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and colors actually telling you there's

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an atmosphere and giving you properties

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about that atmosphere so we can use that

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and learn from that and so so I want to

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talk about other applications of this

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database so one thing we did was take a

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loo board model so loo bar is a large UV

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ultraviolet optical infrared telescope

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concept and so we use the chronograph

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noise model from Tyler Robinson and we

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simulated ok what would what would a

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Lubo are see at these different phases

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using as input our spectral Earth

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database and so here you can see you've

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got a great rail a tail o - Abe and

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ozone Bant Hartley Huggins out UV band a

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chap we ozone band and let's look at

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them as they change their phases so if

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you look

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and I give is faith our error bars have

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shrunk a little bit oh I should say that

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the parameters we use we assumed for

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this where respect to resolving power of

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eighty a mere diameter of 12 meters and

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distance of five parsecs and this is for

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a hundred our integration so so one

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hundred our integration gives you these

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error bars with the twelve meter

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telescope now if we go to crescent phase

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you can see a big difference we've

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completely removed the Rayleigh

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scattering tail so that's gone the earth

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is now a different color than it was

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before

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but another cool thing that happens is

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that the chap we ozone band now gets

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deeper because your path length to the

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atmosphere is bigger and so you get more

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ozone absorption now that that you

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shaped that you shape an earth spectrum

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is actually really diagnostic of Earth's

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spectrum and and separates it from the

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other terrestrial planets and from

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potential abiotic plants we could

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imagine just like bare rock with a

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nitrogen surface would be blue and it

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wouldn't have methane absorption that

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would separate it out from Neptune and

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Uranus but it wouldn't have that you

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shape and there it is again and so the

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idea here is that you could get

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broadband photometry to look for that

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you shape before you invested in the

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spectrum surface mapping is something

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that's been talked about I want to

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advertise a poster by Jacob Lustig

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Jaeger who's going to give a talk in

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this room 215 on Wednesday using the

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spectral earth database I've mentioned

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two tests retrieval models and see how

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well we can actually pull out ocean

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fraction a vegetation fraction and land

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fraction so please come see that one

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other thing that's kind of cool is that

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this model has also been used to model

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the earth shine effect on permanently

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shadowed craters on the moon so it's

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permanently shadowed from the Sun but

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not from the earth and so our models

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been use of that little bit beyond bio

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signatures but it shows the kind of

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flexibility of what this database can be

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used for and so finally I would kind of

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want to ask people in this room who

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might be working on instrument

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simulators who may be working on

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different kinds of bio signatures what

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can the spec to earth database do for

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you we can do arbitrary phase viewing

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angles haze and viewing angles we can do

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different observing cadence so an hour

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50 minutes we can work with that and we

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can switch from one surface to another

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so we can switch out our vegetation for

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anoxygenic photo choice for exam

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and look at the affect on the spectrum

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and how we're cover with retrieval

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models that bio signature would be and

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so and so with that I'll thank you all

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and take questions Thank You Eddie so

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that was really great job not because of

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your session chair but it's really good

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all right we are open for questions

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creating great talk so a lot of the

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planets that we're most interested at

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the moment regarding recent discoveries

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are planets around em dwarves where the

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the integration time will be a

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non-negligible amount of the orbital

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period yes so in that case I guess you

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have a convolution of many of these

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specific phases you've been shown is

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that going to be a problem for backing

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out individual spectra or corresponding

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to a specific phase in those case yeah

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so that's a great question and to answer

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that two things one is the the angular

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separation of the plant stars very bad

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for M stars you know how those own is

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close by so we'll only be able to

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directly image to close by once but we

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still might be able to do a few and to

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answer your question is absolutely you

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would have to consider the fact that the

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planets changing in phase depending on

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your inclination you might be lucky if

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it's based on it's always quadrature and

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you have to consider that you have to

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consider its rotation and so to back it

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out to back out to back out those

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quantities you would you would you would

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need more observations and in terms of

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the specifics that are treatable I would

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encourage you to come and to Jake's talk

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on Wednesday but I would say that in

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terms of rotation I don't think that

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it's going to be any worse for rotation

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because you're still going to have to

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look at the planet for the same amount

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of time whether it's whether it's an M

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star G Star for for disentangling those

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phase effects maybe maybe maybe that's

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maybe that's worse but what you can do

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is you can bin so you can say well I

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looked I looked at it for this amount of

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time in this phase and if you if you if

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you've observed over several orbital

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periods you can rebuild your data

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assuming depending on how you do the

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integration and you can do the same

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thing

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you do with a Earth's orbiting a g-star

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great thanks

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Sean good hey Sean hey Eddie

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great talk that wasn't a crescent face

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was particularly cool so the question I

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have is thinking on the other end from

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the color spectrum one thing that the

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leVoir stdt has been telling us is that

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we need to think of a way to fingerprint

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these different planets because we're

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going to observe them for let's say 100

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hours go away to other science or maybe

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other exoplanet targets and then come

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back and they'll all have moved in their

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orbits yeah and one thing we're worried

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about is is this phase dependence right

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blocking us from knowing which planets

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are the ones we saw before yeah yeah

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it's just popped outside the iwi right

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it is there it is can can you guys look

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at something like you know the SPECT

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resolution we have to get at to find the

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fingerprint you know to find the things

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that aren't dependent on phase or maybe

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I don't know yeah well I mean I mean it

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depends if you're if you're investing a

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lot of time with the planet and you get

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the rotation rate and you can match

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rotation rates yeah but again that takes

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a long time and depends on how you do

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your integrations I think I think we've

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talked about 100 hour integration really

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what you're talking about is like a

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hundred one hour integrations or

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something like that yeah you know a

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thousand ten minute integrations or I

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did that wrong but well you know what I

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mean the detector array oh yeah oh it's

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okay yes yeah but then you can read in

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those data and then you can get a better

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better time-dependent information from

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that so maybe temporal the temp the

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Signum the signature maybe in the

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temporal domain not the spectral domain

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is what your saw yeah so so yes yeah

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okay the twirl domain is another

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leverage point yes yes okay awesome

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thank you all right so we are done for

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this session the morning session and

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we'll meet again at 1:30