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hello

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[Music]

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pleasure to be here

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uh so I'm in the program in Applied

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Mathematics at the University of Arizona

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but I work with Professor Regis farrier

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who's an ecologist and a postdoc antenna

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affolder who is also an ecologist and

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planetary scientist so we developed

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these Global models of Europa and so

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what you see right here is the Europa

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Clipper Mission which is going to launch

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next year in October hopefully and I

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believe it's the largest in terms of

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size spacecraft that NASA's ever built

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because of those massive solar panels

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um because we're when we're five times

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away from the Sun at Jupiter I think the

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brightness scales like inverse Square

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so why do we care about Europa

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so beneath the ice crust we have an

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ocean which potentially contains two to

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three times the amount of water as on

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Earth which is when I first discovered

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that fact I was just so blown away by

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that

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um and we have an energy source from

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tidal heating uh namely we may have

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hydrothermal venting as a result of that

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we saw that on Enceladus because we saw

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silicates in the plumes we haven't

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confirmed that yet in Europa but it's

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very likely based on the amount of tidal

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energy that we expect

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and we also have a rock ocean interface

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so that's very essential for you know

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cycling chemicals needed for life

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uh and we have a lot of convection as

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Sarah talked about

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um which is very essential also for

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so as a result we need these models to

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constrain what we're going to see from

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the mission so we had juice also the esa

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Mission let's not forget about that

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um

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so we need to model all these different

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layers

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um

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so working off what we did for install

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it is to analyze the Cassini data we're

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going to do that here specifically

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focusing on the ecosystem aspect

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um and so what we want to do with that

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is provide clear constraints on what

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kind of BIOS Industries we're going to

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expect based on a number of different

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hypotheses uh of abiotic

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possibilities biotic possibilities and

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so as I said our approach is very

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multi-level uh we are going to first

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look at the bottom layer which is the

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mixing of the core and the ocean to

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assess you know the chemical structure

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coming out of you know coming out

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through through the water passing

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through the core

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and what chemicals are going to come

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through that we're going to have to

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assess what how that mixes in the water

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to assess how much uh chemicals we're

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going to see coming out of the plumes

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and then we also need to take some model

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organisms from Earth to understand the

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metabolisms that we might expect

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along with the ecological and possible

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evolutionary dynamics of the life that

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we're modeling

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so with this framework we're going to

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estimate the likelihoods of certain data

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sets that we're going to see via a

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so to intimidate you a little bit we're

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going to have some math

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um so let's talk about the mixing we

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look at the dimensionless mixing ratio X

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and the temperature T so we have a

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partial differential equation which with

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spatial and time variables

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solving this partial differential

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equation gives us this steady state

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composition

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and temperature of the mixing layer with

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the mass flux density

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we can compute what we're going to see

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coming out of the plumes by integrating

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over the entire space so we we integrate

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the integrand contains this Mass flux

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density and the concentration of a

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specific chemical component that we're

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looking at so whether that be a glycine

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or other amino acids or you know cells

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potentially

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so the concentration of this compound at

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the base of the ocean plume assuming

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that the buoyant mixing layer mixes

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together as we described from the

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previous model

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is given by this quotient of these

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so then we want to look at the cell

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metabolism and output and there's a very

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large variety of possibilities we could

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be working with here so we kind of run

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the model for tons and tons of

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possibilities of Earth analogs

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um depending on these environmental

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conditions so we model this catabolic

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reaction rate of a specific organism

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using the Arrhenius law I know that came

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up yesterday Arrhenius in one of the

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trivia questions

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um

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uh so

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and that's so the Arrhenius law is

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specifically this quotient here and we

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use the catabolic energy constant for

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given by this term and it goes in here

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to understand the ratio of inactivated

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to activated enzymes for the reaction

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now critically we assess the ecological

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dynamics of the system so

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in this case we are dealing with one

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population

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ode model

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describing the Dynamics but we've been

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thinking about incorporating

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multi-species into this because there's

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actually a significant dynamical

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difference when you have competition

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between species in the environment and

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that would seriously affect what we'll

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see when we go there so we have to we

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have to have multiple different

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ecosystem Dynamics models

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and so we solve this and get a steady

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state concentration of a specific uh

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so what can we learn from specifically

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the Europa Clipper Mission so the Europa

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Clipper Mission has some very

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interesting instruments we have a

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mapping Imaging spectrometer so it will

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map the surface and conduct the

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spectroscopy of the surface and the

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distribution of the Organics so as

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things come out of the plumes they they

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fall onto the surface and we should

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expect to see an interesting chemical

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composition I know they're already doing

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this with jwst from the from a distance

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they haven't analyzed that data yet but

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uh should be interesting

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and then to sort of sniff the exosphere

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of Europa we have a mass spectrometer

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Mass specs it'll determine the the

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composition of the of the ocean beneath

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by sniffing that exosphere

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and then we have a surface dust analyzer

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uh it'll have it'll measure like the

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and so in this image you can see kind of

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a remote

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so let's focus on this slide a little

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bit so this is the overall framework so

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at the top we have those mechanistic

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models for the geochemistry and the

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biology that we discussed earlier

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and so the flow of information goes from

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the top as prior probability probability

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densities

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that flow into as parameters into the

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general model

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then we pass those parameters into the

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biological model for physiology and

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evolution

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which then goes to our habitability

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Criterion so the habitability Criterion

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that we generally use here is it's a

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differential equation describing the

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population so essentially if I were to

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take an Earth-like organism drop it onto

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Europa would the change in the

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population be positive or would it be

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zero so the derivative of population

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growth essentially

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and so we pass that into Bayes theorem

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as prior distributions the posterior

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distributions uh come from these uh so

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we okay actually to be more specific

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we simulate all of these models with

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thousands of data points

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to get what we call pseudo data in

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what's called an approximate Bayesian

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computation and so we pass that into the

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model and we take the observational data

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and we put that into base theorem so if

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any of you are aware of Bayes theorem

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that is a way of assessing the

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probability of a certain event given

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prior information and given posterior

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information

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um and so now we have this cycle of

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information so after we get a

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probability from base theorem we can

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pass it back into the geophysical

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parameters model and then continue to

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constrain as we get more observational

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data

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so here's an example of how we did that

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with Enceladus for Cassini

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so in this graph here you can see the

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blue dots are simulations for

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uninhabitable

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parameter space

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the orange dots are

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uninhabited but habitable

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and the green dots are habitable and

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habited

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so what you can see uh from analyzing

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the methane content of the plume and

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Cassini using this framework was that

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the vast majority of simulations for all

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the possibilities we assessed

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fall under the biotic category

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um

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I mean that speaks for itself right

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so um but there's a very important

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constraint on this namely the

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probability of abiogenesis

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so what we do in this model is assess

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all the possibilities from probability

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zero to probability one of abiogenesis

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and so what we found was at a pretty low

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probability of abiogenesis uh the from

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the previous slide

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um

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with a low probability of a biogenesis

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you see that biotic explanations are the

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most probable for what we're seeing the

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in the methane content so we really need

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to develop our models of abiogenesis to

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really limit limit this variable and

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constrain this probability

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so the conclusion is in order to find

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life we really have to model these

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planetary systems with the assumption

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that there could be life there already

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because if we don't we're going to show

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up there and we're going to think okay

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it's habitable but why because

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potentially the habitability of a planet

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is actually coming from the life is

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already there so we have to incorporate

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that into our into our potential models

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so this will allow us to really enhance

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Mission data analysis

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um

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beforehand uh so you know with when we

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had Cassini we showed up there and we

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had no idea what was going on we're like

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wild plumes this is amazing

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um but it took many many years to really

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assess this so hopefully with Europa

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Clipper we have this framework in hand

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as a software package so the data can be

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fed through real time and the benefit of

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this is then we can narrow down in on

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specific areas of Europa that are of

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interest to us and really maximize the

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scientific yield of these missions

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uh nice talk uh two questions maybe the

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first did you choose a prior for a

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biogenesis

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yeah so we sampled the entire space from

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zero to one probability

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in that in those 10 000 or so

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simulations

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so basically

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um we can't pick a prior for abiogenesis

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there's no constraint on that variable

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uh so essentially we have to run the

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model for all possibilities of

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abiogenesis so so like I said

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specifically with the Enceladus data we

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found that uh much less than 50 percent

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chance abiogenesis ensures that the

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biotic explanation is the most probable

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explanation I believe is around 30 or

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less probability but if you're beneath

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that threshold then really the abiotic

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explanations are way more probable even

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though the samples from the abiotic

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simulations are much smaller but you

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know when the probability of evangelists

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is really low

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it's just not going to happen right so

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okay yeah that answered my second

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hey thank you for your talk so for

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Europa are you also in terms of

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metabolism are you thinking about

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methanogenesis or yeah okay so that was

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the first step to consider um infer

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Enceladus it was kind of a the most

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obvious thing to look at because of the

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methane content

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um with Europa it's more complex uh

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because we don't have any real data from

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Europa and also the environments on

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Europa are much more varied you have

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subsurface pockets of water along with

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the ocean and maybe you have like

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biofilms on the bottom of the eye so

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it's it's a much more complex thing

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um so my hope is to uh run all the

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simulations for all the possible

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hypotheses that exist currently okay and

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I have one little follow-up question so

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you very briefly talked about Dynamic

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communities and you mentioned

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competition are you just looking at

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competition or are you looking at other

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sorts of consortiums like in a biofilm

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there's competition but there's also

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lots of sharing going on right so um if

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we're including multi-species in our

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models we're hoping to incorporate all

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the possible interactions between these

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species

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um

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which is really critical I want to re re

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uh iterate because you know the

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competition can kill off like all of the

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life you know so we need to sort of have

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an evolutionary perspective also and not

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just like a real-time ecological

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perspective to assess these things

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um so really good question

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thank you

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very quick questions

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hi Emily bear Stanford University I was

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wondering so in the biological part of

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the model what kind of data goes into

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that and how can environmental

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microbiologists help modelers improve

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their models what kind of data do you

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need for that right so we need Earth

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analogs primarily we need the you know

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catabolic reaction parameters

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for specific Earth analog so you know we

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might want to go to Antarctica and see

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what's going on there

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and that's our best that's our best way

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to constrain it at the moment

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so that's how I think biologists really

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should really help us in that way

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yeah exactly