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you

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[Music]

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hi my name is Kat and I would like to

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share with you one of my favorite images

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Earthrise so this was taken by Apollo 8

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in the 60s and it is a decade since then

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we've been able to explore the solar

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system further and beyond in fact we've

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been able to image planets around other

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stars

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this is HR 8799 one of the self-luminous

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giant planets that current direct

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imaging surveys are able to study in the

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near-infrared and then on a flip slide

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in terms of smaller planets we've been

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able to study those around ultra cool

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and low mass host stars using the using

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transit spectroscopy and then locating

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decades after today we have the James

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Webb Space Telescope coming online and

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we have w first hab X and d'Ivoire and

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what do these three have in common

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they're all interested in imaging

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exoplanets around sun-like stars in the

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visible take W first for example here we

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have the planet to start contrast ratio

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versus separation and this is what I was

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mentioning earlier about the self

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luminous giant planets for the current

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state of direct imaging with W first and

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it's coronagraph we're going to be able

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to cover this part of the regime to

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study non transiting planets and that

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are those that are cooler in temperature

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and then coming down over here if you

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recall - Maggie turn balls talked on

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Monday where she mentioned the

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possibility of pairing a star shade with

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W first this once we over plot the

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contrast curve for W first and star

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shade this is where we have the four

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system planets at ten parsecs so this

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tells us that W firs might have the

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possibility of imaging rocky exoplanets

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in the future and so when these missions

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take the images of the planets we're

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going to get spectroscopy on them and

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we're also get going to get data that

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are noisy and the challenge will be to

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backing out the properties of these

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planets

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and given the data quality we want to

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know how robustly will we be able to get

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those information and more importantly

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how do we quantify that so I'm going to

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be talking about how we use how we're

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going to simulate data in order to we're

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going to simulate data and then we're

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going to build a retrieval tool that

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will then help us constrain those

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properties based on our simulated data

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ok so let's start with a spectrum of the

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earth here we have the Geo Metro sources

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wavelengths going from the near

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ultraviolet to the infra red and all

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three missions that I mentioned are

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going to be interested in exploring the

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possibility of imaging the visible

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wavelengths and in this particular

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region there are many interesting

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features that we'd be that we will

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capture with our model to begin we have

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the nitrogen really scattering and then

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we have water absorption that I will

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mark somehow yes Rayleigh scattering

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water absorption ozone absorption is

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over here and then there are oxygen

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features as well oh all right

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thanks for the tip awesome great so

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those are the features that we're going

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to recreate in our model which is

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basically a planet it's visible

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hemisphere would be divided into 100

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points and for each point we're going to

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run radiative transfer on it as well as

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including scattering to determine the

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geometric albedo overall and then we're

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going to translate that into the planet

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2 star flux ratios which is what we're

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going to be able to observe and speaking

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of what we're actually going to be

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observing it's going to be something

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that's lower in resolution so here I

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have an example of Planet 2 star flux

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ratio versus wavelength at a resolution

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of 70

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and what's more is that these data are

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going to be noisy so in this case we

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have a data set that our signal-to-noise

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ratio or SNR of 20 what's going to be

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really informative is we if we have a

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way to justify a certain signal-to-noise

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ratio that will allow us to quantify

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constraints on water vapor or oxygen so

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in this case I have a spectrum that's

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signal-to-noise ratio of 10 and so how

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will that do in comparison to the

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signal-to-noise of 20 and would we be

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able to get away with signal-to-noise of

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5 and still get the amount of constraint

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that we would want on interested

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quantities so we aim to understand what

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information is present as a function of

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signal-to-noise ratio and to do that

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we're going to use a Bayesian retrieval

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framework that chiu-hung introduced the

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concept pretty well earlier this morning

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essentially we're going to have a bunch

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of input parameters that have prior

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distributions that we'll draw from and

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construct one set of parameters we're

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going to put it into the forward model

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which generates a high-resolution albedo

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spectrum that we then bend down to the

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resolution of the data and this is when

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we'll get to do the interesting

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goodness-of-fit comparison by feeding it

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into a chi-square likelihood function

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and it's going to be an iterative

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process where we'll draw from those

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prior distributions again and again and

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compare that goodness of fit again and

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again until we construct the posterior

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probability distributions for each of

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those parameters another way of thinking

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about a retrieval is this is a very

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data-driven method so for a given set of

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data and we propose a set of parameters

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there's going to be a corresponding

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spectrum that follows that proposed set

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of parameters and then for a given range

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of parameters because there's going to

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be a range of spectra and so we'll be

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able to visualize the spread and our

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confidence in terms of how well our

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model fit the data so here we have red

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is lunch Sigma for the spread we have

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coral that

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for the two sigma spread and then

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there's also a blue line over here and I

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just want to say that we're using the I

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think I forgot to mention we're using

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the noise model that Ty Robinson created

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for coronagraphs and in terms of the

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larger error bars over here in the red

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end this is partially due to the fact

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that there are fewer photons from the

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host star in this particular part of the

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spectrum and the texture quantum

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efficiency in the red end tends to fall

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off or at least that's the case for a W

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type W first type scenario we're

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considering so going back to this idea

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of posteriors I'm going to start with an

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example where we only have two free

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parameters so this is an atmosphere with

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a surface pressure that we're going to

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retrieve for and water vapor and we're

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assuming we know everything else so here

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are those posterior distributions after

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we perform the retrieval and you can see

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this is for water this is for surface

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pressure and then the blue lines

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indicate the truth input values and the

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power of the retrieval methods that were

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able to attach uncertainties to our

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estimates but of course life is not that

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easy we are not always going to only

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have two unknowns so we've developed a

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forward model where we have nine

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retrievable parameters and this is a set

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of parameters that we think represents a

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minimum number of parameters that are

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necessary in order to recreate an

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earth-like spectrum so we have surface

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pressure surface albedo the mixing

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ratios for water ozone and oxygen and we

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have a cloud layer that has a cloud top

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pressure of Delta P and obstacle depth

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and then an F cloud parameter to

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represent fractional cloudiness and for

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the sake of not being too complicated we

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decided that we want to assume that we

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know the radius the semi-major axis what

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the background gas is and we're also

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implementing an isothermal pressure

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temperature profile so once our forward

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model is assembled we're able to

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simulate the spectra between point four

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and one micron at a resolution of

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seventy and for four different

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so first up we have the results from the

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signal-to-noise ratio of five data and

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just by eye you can tell that there's

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not going to be much information that

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the data will be able to give us and we

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see that very well with the spectral

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Fitz and I've marked the water features

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over here because I'm about to walk

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through the posterior distribution for

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water for each signal-to-noise ratio so

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you can see how that changes once the

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data get better so first it's the same

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signal to noise and you can see so this

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is log of water vapor mixing ratio and

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this is the true value so there is no

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detection for this SNR going up to SNR

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of 10 there is still no detection and we

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step it up to SNR 15 and this is where

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it got really awesome where we were able

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to get a constraint on the water vapor

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pressure mixing ratio and returning to

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that comparison to the spectra you can

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see in this case we actually are able to

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trace out the features much better and

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that's because we have that constrained

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water vapor now however water is not the

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only thing we care about and if we look

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instead at the broader picture and we

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think about the gases were interested in

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water ozone and oxygen we see that in

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this case so this is water this is ozone

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this oxygen comparing SNR 15 and SNR 20

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you can see that it is only with SNR 20

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that we are able to actually constrain

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all three molecules so this shows us

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that retrievals are actually able to

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allow us to make these statements and

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not only that we're able to back it up

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with quantitative reasoning so moving

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forward what we're excited about

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implementing is connecting or expanding

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beyond just studying earth-like planets

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we want to be able to model super earth

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we want to be able to model many

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Neptune's because those are the Kepler

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mission found are the most common small

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planets in the galaxy

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and then as Sean alluded to earlier

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we're going to expand our wavelength

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range such that we can accommodate the

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interest

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5x and levar as we move forward in

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exploring what types of data we'll be

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able to enable our accurate inference

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about an atmosphere and then furthermore

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we want to be able to implement surface

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wavelength dependent surface albedo so

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Jake who's speaking after me will be

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able to tell us about how important it

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is to consider the heterogeneity for the

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surface of the planet so to wrap up we

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have created a retrieval framework that

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is able to allow us to quantitatively

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say how confident we are in our

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understanding of a terrestrial

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atmosphere when observed with a future

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space-based coronagraph or starshade

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imaging mission we have studied

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simulated spectra from 0.4 to 1 micron

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at a resolution of 70 and we found that

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it is only at a signal-to-noise ratio of

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20 that were able to constrain

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quantities like water ozone and oxygen

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all together moving forward there are

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lots of things we want to be able to

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implement and so I would like to hear

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what your thoughts are and take any

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questions thank you

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[Applause]

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that's great talk in terms of the inputs

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for your model let's let's say one of

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these flagships get selected is flying

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into the 2030s how important are the NA

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prior knowledge of the semi-major axis

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the radius or we might be able to get a

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limit on the radius from a dynamical

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Mass how important is the the

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information on the planet before the

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mission actually flies that we get a few

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images and how critical is it or is it

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is do you think you can there's not

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enough degeneracies you might be able to

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get away without that information so in

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terms of radius we have already tried

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retrieving on radius and in fact if you

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refer to Michael and IX 2016 paper where

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they're looking at jovian planets in the

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reflective light they retrieve for

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radius and so that is something that we

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might be able to put constraints on

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ourselves because it's going to be

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difficult to actually measure that for a

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planet but for semi-major axes I think

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that'll be really important to know well

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in terms of getting the uncertainties on

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that down because that could impact just

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where the the data sit and so but we

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haven't looked into throwing that into

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our retrieval so at least for the

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semi-major axis I would say that would

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be something in terms of a strong tree

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we want to be able to get well I can

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obviou mandel from goddard one common

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one question the comment is you probably

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are aware of this but I'd like to hear

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which if you guys have started exploring

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it each of those points or parts of the

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spectrum will be taken on with different

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observations with W first at least

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you'll get in on an optical band you'll

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get a certain ifs band and so it'd be

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really nice to be able to prioritize one

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observation or a set of observations

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rather than saying we need the whole

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spectrum have you guys started doing

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that or is that yeah so we have an

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instrument model set up right now where

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we have the two blue filters as well as

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the from 692

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600 - 600 to 900 nanometers in space so

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we have that set up we have yet to

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separate those three ifs bands though I

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mean I know that some parts of the

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spectra they overlap so it would be

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interesting to think about that in terms

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of having very good signal to noise

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ratios in that particular area so we're

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like halfway there

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okay and my question was are we going to

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have to cut the oh you're I just had one

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short question yeah you can I was just

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you you said you took all the points on

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a hemisphere and yet you have a globally

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averaged profile why do you have to use

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all of the individual points across your

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your atmosphere um maybe maybe it was my

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wording that was off about the global

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yawn so yeah we can talk later

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Thanks all right let's get cut another