We spend a lot of time here talking about how TARTLE can help advance a variety of fields through our data marketplace. Whether it’s medical science, tracking the effects of human activity on the climate, or even fields focused on the past like archeology, TARTLE can be of use. Through the sharing of information, both with colleagues and laymen who might have valuable data new advances in understanding can be made, advances that may not happen for years otherwise, if at all.
Today, we turn our gaze to the stars and the field of astronomy, specifically star formation. It’s long been understood that an individual star forms from the slow accretion of gasses into a ball large enough that the pressures and heat generated by its own gravitational force begin a self-sustaining fusion reaction that will go on for billions of years. However, a number of questions remain. Why do some clouds only produce relatively small stars like our own sun while others birth massive giants like Betelgeuse and Rigel in the constellation Orion? Yet others lead to failed stars such as elusive brown dwarfs. Then there is the strange fact that planets form around many of them. Why did they not become part of the star in the center of the system?
Other questions such as why the gas clouds get converted into stars at different rates in different galaxies? Most seem to convert the bulk of their gases into stars in one to ten billion years. Yet others seem to be in a hurry. These starburst galaxies can deplete most of their gas reserves and be filled with stars in under a billion years. Why?
None of that challenges the primary model of stars forming out of massive gas clouds but with these mysteries still in play, it is clear that this is a rudimentary outline at best. As such, astronomers are beginning to turn to Big Data to help get some answers. With hundreds of observatories around the world and even a couple in orbit, insane amounts of data, images of galaxies billions of light years away and of course many stars within our own Milky Way are constantly being taken and stored. Not just photos either. The effort to understand the universe involves more than just pictures. Images are taken in every conceivable part of the spectrum, from the ultraviolet to the infrared and even radio waves from the depths of space are analyzed. Every bit of this data is useful yet it has until recently been siloed off. Fortunately, scientists have begun to see the value in sharing their data, allowing for the creation of massive searchable databases that will allow them to cross reference information from different times and places, using it to test the predictive power of their models. And when they find (as they surely will) evidence that confounds the model, it will be much easier to adjust that model on the basis of new evidence.
TARTLE can of course play a role here and be that data marketplace through which information and theories are shared. Through the marketplace it will be possible to not just refine theories but to determine the best observational techniques and equipment, greatly improving efficiency in the long run.
TARTLE brings other benefits as well. As we brought up recently in an article on archeology, astronomers can anonymously share their own hypotheses, ones that might go against the conventional wisdom. Too often these can’t be aired publicly for fear of reprisals and shaming. Sadly, scientists are as open to bias as anyone else. If you doubt that, check into Einstein’s troubles with accepting the Big Bang sometime. It’s eye-opening. TARTLE can provide a forum for some of these ideas that might be unconventional now but may become accepted as the standard in the future. Now, that says something about the importance and the worth of data.
What’s your data worth?
We spend a lot of time here talking about how TARTLE can help advance a variety of fields through our data marketplace. Whether it’s medical science, tracking the effects of human activity on the climate, or even fields focused on the past like archeology, TARTLE can be of use. Through the sharing of information, both with colleagues and laymen who might have valuable data new advances in understanding can be made, advances that may not happen for years otherwise, if at all.
Speaker 1 (00:07):
Welcome to TARTLE Cast, with your hosts Alexander McCaig and Jason Rigby, where humanity steps into the future and source data defines the path.
Jason Rigby (00:29):
You don't have your alien shirt on today.
Alexander McCaig (00:30):
Yes, I know. I've listened to that lady talk so many times, I don't even listen to her anymore. I just know it's go time.
Jason Rigby (00:37):
It's go time.
Alexander McCaig (00:38):
Yeah. When it's on, and then when it stops-
Jason Rigby (00:39):
Let's dig.
Alexander McCaig (00:39):
Yeah, let's dig. Okay MacArthur. So we always like, everybody likes to look up at the stars. If you don't, you're interesting. You probably don't like Disney World either.
Jason Rigby (00:53):
Yeah.
Alexander McCaig (00:54):
Or dogs.
Jason Rigby (00:56):
Yeah.
Alexander McCaig (00:56):
Nice fluffy blankets, cozy. You probably don't like being cozy.
Jason Rigby (01:00):
How do we go from wolves to domesticated dogs? I know they're working on that, I think I have an article for that, that we're going to talk about, it's one of our future episodes [crosstalk 00:01:11]. When you think about it, it had to be... Because I read the article, I think I put it in one of our... Because we use the Asana, shout out to Asana. And they were talking about, they had just found bones of dogs that were buried next to tribesmen in Siberia area, and they were decorated, the dogs were decorated. And it was very intentional in a way.
Alexander McCaig (01:35):
They probably in some way worship that dog.
Jason Rigby (01:37):
Yeah, or they went along in battle with them, maybe? Or they were man's best friend. But-
Alexander McCaig (01:43):
I'd love to see the first guy who'd be like, I'm going to domesticate this thing.
Jason Rigby (01:46):
Yeah, but then I'd just picture these guys sitting around a fire, and then the wolves are out there howling. But then there's one wolf that's kind of curious, and then a guy gives him a little bit of food, and he comes a little bit closer, and he comes a little bit closer.
Alexander McCaig (01:59):
Should I trust this guy? [inaudible 00:02:00] onto his forum and he's like, well, wait a minute, can we still save the relationship?
Jason Rigby (02:05):
Yeah, for real. We don't recognize this, but animals are wild.
Alexander McCaig (02:12):
They are wild.
Jason Rigby (02:13):
They're just being animal.
Alexander McCaig (02:14):
And we always go up assuming that they think and act like a human being.
Jason Rigby (02:18):
But is it everyone that goes to the fire a little bit quicker and then it's just thousands of years and it's just a [Mitski 00:02:22]. Next thing you know, you go from a wolf to this super lovable Golden Retriever.
Alexander McCaig (02:26):
Yeah, or a Doge.
Jason Rigby (02:27):
Yeah, yeah.
Alexander McCaig (02:28):
And then it turns into a cryptocurrency.
Jason Rigby (02:29):
Yes. Which I bought more. It went down and I bought more.
Alexander McCaig (02:32):
Good for you.
Jason Rigby (02:33):
I don't know, it's just a fun thing. Whatever.
Alexander McCaig (02:35):
I don't care [crosstalk 00:02:36]-
Jason Rigby (02:35):
It's gone up. I've made $247 so far. I know. Isn't that exciting?
Alexander McCaig (02:40):
That's great. Good for you. Cryptocurrency off a Dogecoin. It's like two weeks worth of groceries for you.
Jason Rigby (02:44):
Yeah.
Alexander McCaig (02:45):
Good for you.
Jason Rigby (02:45):
Or a week's worth.
Alexander McCaig (02:47):
Gosh, your consumption rate's so high. So is mine. We just eat a lot.
Jason Rigby (02:51):
I know, yeah. I mean, you do a lot of climbing and when you go to the gym and you work out really hard, you just need more fuel. I mean, the intermittent fasting doesn't work for me. I tried that so many times.
Alexander McCaig (03:03):
Dude, fuel is key for growth. Not only for us, but also for stars that are forming in the night sky, in nearby galaxies.
Jason Rigby (03:14):
[crosstalk 00:03:14] climbing to nearby, not far away galaxies, nearby.
Alexander McCaig (03:18):
Do you want to know why? Because everything's interconnected. You just got to look at it.
Jason Rigby (03:20):
It is. Yeah. So this article, how stars form in nearby galaxies, you're like, why are we talking about stars? Why are we talking about galaxies? Well, listen to this.
Alexander McCaig (03:27):
Stars are cool, star galaxies.
Jason Rigby (03:29):
Robert Feldman sheds new light on this topic with the help of a data-driven observational measurement.
Alexander McCaig (03:34):
No pun intended, because it stars in his life.
Jason Rigby (03:37):
Yes.
Alexander McCaig (03:37):
Yeah. Data-driven methods. So what we want to do is we want to take tons of photographs of stars, which have a range of one to eight or nine billion years for their development. For one star.
Jason Rigby (03:55):
Yeah. And it's born in dense clouds and molecular hydrogen gas that permeates the interstellar space. And then physics, a star formation is complex. Recent years, they've seen this substantial progress towards understanding how stars form in a galactic environment. But here's the question they wanted to propose to data.
Alexander McCaig (04:10):
I like this.
Jason Rigby (04:11):
What ultimately determines the level of star formation in galaxies?
Alexander McCaig (04:15):
So what they are finding out, like your car, if I want to ultimately know how long I can drive the car, it's going to be dependent on how much fuel I have. So having these interstellar molecular gases, if there's enough of them, fantastic, but then how efficient is my engine? Am I using a lot of it very quickly to develop these stars? Or do I use very little over longer periods of time to develop that star? And so what they're doing is they're taking tons of these different spectrum photographs of these nearby stars that are growing, and they're saying, let's look at the changes over time in the amount of molecular gases that we have here in the size of the star as it begins to grow, or the number of stars that are growing. And then we can analyze the rates in a Bayesian's statistical model.
Alexander McCaig (05:04):
So what they're saying is, okay, here from observation is our very first look. We've taken these pictures, here's the first model, let's look at the rate. Okay, now that we've observed it, there's actually a variance in that rate. We need to go back and retune the model's original hypothesis. So every time they take a new photo, it's coming back in and retuning, that's the caustic model, so that they can find a normalized curve for the amount of stars, how big they are, the amount of gas and how fast the gas is depleted. So that's the general idea in the big data senses. Okay, if we're observing, taking tons of information and watching those small changes happen, something that would be very difficult to do for the human eye, we can facilitate big data in these learning algorithms to normalize our curves and understand, okay, we know for sure that if we see this volume of molecular gases in space, we're going to have a star formed in one billion years or nine billion years. We're going to have 1 of them or 10 of them.
Jason Rigby (05:58):
Yeah. And they're looking at the physics of the gas, the star conversion. So they look at gas inflows, outflows and gas consumptions. And then gas depletion timescale and measuring gas masses reliability. So now we have all these detection limits, so now we can look at this as a data packet.
Alexander McCaig (06:16):
Yeah. Isn't that just amazing? You have a satellite taking images and it's measuring gas content from something that is fricking light years away. That is astonishing. If you really wrap your head around it, it's truly incredible to look at something like that. And what they're doing is they're creating this data-rich data packet. And how cool if we can get... Remember we talked about the Rubin Observatory?
Jason Rigby (06:42):
Right.
Alexander McCaig (06:42):
What if we could get all the observatories around the world to start packing in these data packets? Now we've had one telescope doing all this analysis and we compare it with another one and we can have a real big supercomputer analyze the whole thing because now we've collected insights from all these observatories. Right over here in New Mexico, as you're on your way to Socorro or pass Socorro where you go to the Very Large Array, if you look left on the mountainside in Magdalena, there's this beautiful brand new observatory that sits up there, but you'd never know. It's just like a blip on top of a mountain, but it's collecting so much information. So how do we share something that's so decentralized and disparate collecting all its info with all these other pieces around here? Collective knowledge, there's a lot of good stuff. I bet you they could solve and normalize that gas usage model for star formation a lot quicker.
Jason Rigby (07:29):
Yeah. And this is what they talk about, and this is a scitechdaily.com article. It will be paramount to continue the development of statistical and data science methods to accurately extract the physical content from these new observations, and to fully uncover the mysteries of star formation in galaxies.
Alexander McCaig (07:48):
Yeah. That's so cool. That gets me so fired up. It's so interesting to think that even our sun, which is massive, is puny compared to so many other things out there. For instance, if you compare it to Betelgeuse, which sits in Orion's Belt, it's one of the biggest observational stars around us, this blue super giant. But all that came together was gas forming. And because it sits in an environment of no gravity, it creates this massive perfect sphere and everything's just kind of fallen into itself under its own gravity. It's just amazing. This cycle of implosion and regeneration. If I want to give someone some secrets to some interesting physics, implosion and regeneration, right? Look at the design of an apple, toroid structures, stuff like that. But it's amazing that our world and who we are, our physical body, this material, has come together because of stars in molecular gases and things just binding and swirling.
Alexander McCaig (08:44):
And it's incredible that we now have the power to observe it. And if we want to accelerate that observation, we collectively need to take all that telescopic data from all the observatories and bring them all together at once. Big data is going to do a lot of good. And it's awesome that we're trying to understand what's going on in our galaxies, but it's equally just as important to understand what's going on with us internally as a human being.
Jason Rigby (09:07):
Yes. And if you're an observatory and you want to work with TARTLE, you can go to tartle.co and sign up. And-
Alexander McCaig (09:15):
And if you're low on funds for your observatory, we'll open up a new annuity stream.
Jason Rigby (09:20):
Yes.
Alexander McCaig (09:20):
Start selling the data that you have coming out of that observatory. Hello?
Jason Rigby (09:24):
Yeah. We'd love to be able to have a conversation with you. You can go to tartle.co.
Alexander McCaig (09:29):
Thanks.
Speaker 1 (09:37):
Thank you for listening to TARTLE Cast, with your hosts Alexander McCaig and Jason Rigby, where humanity steps into the future and source data defines the path. What's your data worth? (silence).
