#Data for Machine learning

[tagx01]

10 Must-Have Financial APIs for Real-Time Market Tracking in 2025

In today’s fast-moving world of finance, staying updated with real-time market data is essential. Achieving success is easier with up-to-date information, whether you are a trader, financial analyst, or investor. Financial APIs (Application Programming Interfaces) give you quick and easy access to important data like stock prices and economic indicators.

Financial APIs allow users to integrate financial data directly into their platforms. These APIs enable quick analysis, tracking, and decision-making. In 2025, it's essential to have the right financial data at your fingertips. With the help of the best financial APIs, you can stay ahead of market trends, track fluctuations in real-time, and analyze complex economic models.

In this blog, we'll take a look at the top 10 financial APIs that are essential for market tracking in 2025. These APIs cater to both beginners and experts, meeting their needs. APIs are accessible and valuable for anyone looking to enhance their market analysis and trading strategies. They are built to help you navigate the increasingly complex world of finance, making it easy to track stocks, bonds, and economic data.

What is a Financial API?

A Financial API acts as a connector, linking software applications to various financial data sources. APIs allow systems to easily access and share important financial information. With the help of APIs, developers, business owners, and individuals can access real-time or historical data, like market prices, stock performance, economic indicators, and more. Scraping data through multiple sources manually is a difficult task, so APIs help automate the process of fetching financial data.

Financial APIs support users in making smarter decisions quickly by integrating up-to-date financial information directly into their applications or systems. Whether you want to build a new trading strategy, conduct market research, or track investments, APIs are here to help. They make it easier to understand the financial world quickly and efficiently, giving you real-time data to stay ahead in a changing market.

Importance of Financial APIs for Real-Time Market Tracking

Financial APIs are now an essential tool in today’s financial world, providing many benefits across different areas. Here are the main reasons why they are so crucial:

Wider Access to Financial Data

Real-time market data is accessible to everyone, from individual investors to large institutions, through Financial APIs. This makes data available to everyone, helping people make better decisions.

Faster Decision-Making

Financial APIs provide data instantly, helping you respond faster to market changes and make quick decisions. Speed is important for making trades, changing portfolios, or analyzing market trends instantly.

Driving Innovation in Fintech

In the financial industry, APIs play a crucial role in driving innovation and progress. They help developers build products like robo-advisors, trading tools, and custom financial apps using live market data. These tools provide real-time insights for smarter financial decisions.

Enhancing Risk Management

Financial APIs offer key data on risks such as market changes and asset liquidity. This allows businesses to better manage and reduce financial risks, safeguarding investments and assets.

Streamlined Compliance and Reporting

APIs make it easier to comply with regulations by automating data collection for reports and filings. This cuts down on manual tasks, reduces mistakes, and helps businesses stay updated with industry rules.

10 Must-Have Financial APIs

As financial markets evolve quickly, having access to accurate, real-time data is crucial for traders, investors, and analysts. Financial APIs make it easy to integrate live market data into apps, helping users monitor stocks, trends, and important financial metrics. Here are 10 essential financial APIs for 2025 to stay on top of market tracking:

1. Financial Modeling Prep API

Financial Modeling Prep is a popular platform for developers who want to add stock prices, company details, and key financial information to their apps. It provides both real-time and past data, helping users make smart decisions with up-to-date market info. The platform offers data on more than 70,000 stocks, insider trades, and financial reports going back 30 years.

Key Features:

Real-time and historical stock prices

Financial ratios, company fundamentals, and market news

Data formats in JSON and CSV for flexibility

Why Use It: Its extensive stock coverage and reliable data make it a solid option for traders and developers who need a trusted source for financial analysis.

2. Alpha Vantage API

Alpha Vantage offers real-time and historical data on stocks, forex, cryptocurrencies, and various other financial instruments. Its free API includes various technical tools and charts. Premium plans offer better data and higher limits. With easy setup and clear documentation, Alpha Vantage is popular with developers.

Key Features:

Stock, forex, and cryptocurrency data

Technical indicators like RSI, MACD, and moving averages

Free tier with access to basic data

Why Use It: Ideal for small-scale projects or personal use, this API provides reliable and free data access, with the option to scale up for larger applications.

3. Intrinio API

Intrinio is a high-quality data provider, offering real-time and historical data on ETFs, mutual funds, and stocks. Its trusted API offers different pricing options, including customizable plans for businesses. Intrinio is recognized for delivering dependable, high-quality data that professionals and organizations trust.

Key Features:

Real-time stock, ETF, and mutual fund data

Global market coverage

RESTful API with JSON and CSV support

Why Use It: Intrinio offers reliable data that helps financial experts and developers create financial models and trading strategies.

4. Quandl (Nasdaq Data Link) API

Quandl offers a wide range of data, including economic and alternative data, as well as traditional financial market data. It sources information from trusted places like central banks, making it great for detailed economic research.

Key Features:

Alternative data, including demographics and economic indicators

Reliable sources and high-quality datasets

Customizable data requests and flexible API formats

Why Use It: Quandl's access to unique data makes it ideal for financial analysts and researchers seeking deeper market insights.

5. Xignite API

Xignite provides a cloud service that gives access to real-time and past data for financial assets like stocks, commodities, and currencies. It works with SOAP and REST APIs, making it easy to use for many financial applications.

Key Features:

Real-time data on equities, forex, and commodities

API support for multiple programming languages

Enterprise-grade security features

Why Use It: Xignite's infrastructure is scalable and reliable, making it suitable for large organizations or fintech startups requiring real-time market data.

6. Bloomberg Open API

Bloomberg’s Open API provides access to market data, news, and research to developers. Bloomberg is recognized for its comprehensive coverage of various markets. It’s a trusted source for institutional investors, providing valuable market data and easy-to-use analysis tools.

Key Features:

Real-time market data, news, and research

Integration with Bloomberg Terminal for advanced analysis

RESTful API architecture with support for multiple languages

Why Use It: While pricey, Bloomberg provides unrivaled market data that can support sophisticated financial applications and enterprise solutions.

7. Polygon.io API

Polygon.io offers quick market data for stocks, forex, and cryptocurrencies. It works with WebSocket and REST APIs, making it ideal for real-time tracking and analysis.

Key Features:

Real-time data on stocks, forex, and crypto

WebSocket API for live streaming data

News sentiment analysis and event-driven data

Why Use It: Polygon.io excels in providing fast, real-time data that traders and algorithmic systems need for market monitoring and trading.

8. Tiingo API

Tiingo offers accurate stock price data and useful analysis tools. It has different pricing plans for both individuals and businesses, making it affordable for all.

Key Features:

Real-time stock prices, dividends, and splits

Fundamental data and earnings reports

RESTful API with JSON and CSV support

Why Use It: Tiingo’s emphasis on data quality and reliability makes it a great choice for both retail investors and institutional clients.

9. IEX Cloud API

IEX Cloud offers a simple financial data API that provides livestock prices, market information, and company details. With an easy platform and flexible pricing, it's perfect for developers and traders who need reliable data.

Key Features:

Real-time stock prices, quotes, and volume data

Corporate actions like dividends and earnings releases

Free tier and premium pricing options

Why Use It: IEX Cloud is an affordable option for developers and traders seeking comprehensive market data and an easy-to-use platform.

10. Yahoo Finance API

Yahoo Finance is known for giving free access to live and past stock prices, market data, and company info. Its API lets users add stock data and financial details to their apps.

Key Features:

Real-time and historical stock prices and market indices

Company financials and analyst estimates

Free tier with limited usage and premium options for additional features

Why Use It: Yahoo Finance is a popular choice for individual investors and those looking for free, easy-to-use financial data for personal or small-scale projects.

How to Select the Best Financial Data APIs?

Choosing the right financial data API is essential for accurate and reliable real-time market tracking. Here are key factors to consider:

1. Accuracy and Reliability

Make sure the API delivers accurate and current data from trusted sources. Check for strong data verification and error-tracking systems.

2. Data Coverage and Variety

Check the types of data available, like stock prices, forex rates, economic trends, and other unique datasets. Ensure they cover the markets or areas you need.

3. Pricing and Cost Transparency

Choose an API with clear pricing. Be mindful of subscription models, pay-per-use fees, or hidden charges like data licensing.

4. API Documentation and Support

Look for APIs with comprehensive documentation and responsive support to simplify integration and troubleshooting.

5. Security and Compliance

Ensure the API follows strong security protocols and complies with relevant regulations (e.g., GDPR) to protect your data.

Best Practices for Implementation

Integrating financial APIs requires careful planning to ensure efficiency and security. Here are key best practices for successful implementation:

1. Start Small

Start with a small-scale integration project to get acquainted with the API and explore its features. Expand the integration as you gain experience.

2. Follow API Documentation

Adhere strictly to the API provider's guidelines for request formats, endpoints, and authentication methods to avoid integration issues.

3. Implement Error Handling

Set up mechanisms to handle errors gracefully.Ensure that users receive clear error messages and that detailed information is logged to assist with troubleshooting.

4. Optimize Performance

Reduce unnecessary data transfers, use caching, and implement asynchronous processing to improve the efficiency and speed of your API calls.

5. Ensure Data Security

Use HTTPS to encrypt sensitive data and implement secure authentication methods to prevent unauthorized access.

Conclusion

Real-time data is essential for making smart financial choices in today's changing world. Financial APIs help monitor stock prices, study economic trends, and create trading strategies. These tools simplify decision-making and keep you ahead.

This blog covers 10 APIs with unique features for 2025. Financial Modeling Prep offers detailed data, while Bloomberg Open API provides market insights. These tools are key for real-time tracking.

TagX Finance API gives you access to reliable market data, forecasts, and past trends. It helps you make better investment decisions. Get insights on stocks, mutual funds, and ETFs in the format you prefer—API or flat files.

Ready to optimize your financial strategies with reliable market data? Contact us today to learn more!

[kaiserouo]

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nom

[river-taxbird]

What does AI actually look like?

There has been a lot of talk about the negative externalities of AI, how much power it uses, how much water it uses, but I feel like people often discuss these things like they are abstract concepts, or people discuss AI like it is this intangible thing that exists off in "The cloud" somewhere, but I feel like a lot of people don't know what the infrastructure of AI actually is, and how it uses all that power and water, so I would like to recommend this video from Linus Tech Tips, where he looks at a supercomputer that is used for research in Canada. To be clear I do not have anything against supercomputers in general and they allow important work to be done, but before the AI bubble, you didn't need one, unless you needed it. The recent AI bubble is trying to get this stuff into the hands of way more people than needed them before, which is causing a lot more datacenter build up, which is causing their companies to abandon climate goals. So what does AI actually look like?

First of all, it uses a lot of hardware. It is basically normal computer hardware, there is just a lot of it networked together.

Hundreds of hard drives all spinning constantly

Each one of the blocks in this image is essentially a powerful PC, that you would still be happy to have as your daily driver today even though the video is seven years old. There are 576 of them, and other more powerful compute nodes for bigger datasets.

The GPU section, each one of these drawers contains like four datacenter level graphics cards. People are fitting a lot more of them into servers now than they were then.

Now for the cooling and the water. Each cabinet has a thick door, with a water cooled radiator in it. In summer, they even spray water onto the radiator directly so it can be cooled inside and out.

They are all fed from the pump room, which is the floor above. A bunch of pumps and pipes moving the water around, and it even has cooling towers outside that the water is pumped out into on hot days.

So is this cool? Yes. Is it useful? Also yes. Anyone doing biology, chemistry, physics, simulations, even stuff like social sciences, and even legitimate uses of analytical ai is glad stuff like this exists. It is very useful for analysing huge datasets, but how many people actually do that? Do you? The same kind of stuff is also used for big websites with youtube. But the question is, is it worth building hundreds more datacenters just like this one, so people can automatically generate their emails, have an automatic source of personal attention from a computer, and generate incoherent images for social media clicks? Didn't tech companies have climate targets, once?

[cyle]

still confused how to make any of these LLMs useful to me.

while my daughter was napping, i downloaded lm studio and got a dozen of the most popular open source LLMs running on my PC, and they work great with very low latency, but i can't come up with anything to do with them but make boring toy scripts to do stupid shit.

as a test, i fed deepseek r1, llama 3.2, and mistral-small a big spreadsheet of data we've been collecting about my newborn daughter (all of this locally, not transmitting anything off my computer, because i don't want anybody with that data except, y'know, doctors) to see how it compared with several real doctors' advice and prognoses. all of the LLMs suggestions were between generically correct and hilariously wrong. alarmingly wrong in some cases, but usually ending with the suggestion to "consult a medical professional" -- yeah, duh. pretty much no better than old school unreliable WebMD.

then i tried doing some prompt engineering to punch up some of my writing, and everything ended up sounding like it was written by an LLM. i don't get why anybody wants this. i can tell that LLM feel, and i think a lot of people can now, given the horrible sales emails i get every day that sound like they were "punched up" by an LLM. it's got a stink to it. maybe we'll all get used to it; i bet most non-tech people have no clue.

i may write a small script to try to tag some of my blogs' posts for me, because i'm really bad at doing so, but i have very little faith in the open source vision LLMs' ability to classify images. it'll probably not work how i hope. that still feels like something you gotta pay for to get good results.

all of this keeps making me think of ffmpeg. a super cool, tiny, useful program that is very extensible and great at performing a certain task: transcoding media. it used to be horribly annoying to transcode media, and then ffmpeg came along and made it all stupidly simple overnight, but nobody noticed. there was no industry bubble around it.

LLMs feel like they're competing for a space that ubiquitous and useful that we'll take for granted today like ffmpeg. they just haven't fully grasped and appreciated that smallness yet. there isn't money to be made here.

[mostlysignssomeportents]

The surprising truth about data-driven dictatorships

Here’s the “dictator’s dilemma”: they want to block their country’s frustrated elites from mobilizing against them, so they censor public communications; but they also want to know what their people truly believe, so they can head off simmering resentments before they boil over into regime-toppling revolutions.

These two strategies are in tension: the more you censor, the less you know about the true feelings of your citizens and the easier it will be to miss serious problems until they spill over into the streets (think: the fall of the Berlin Wall or Tunisia before the Arab Spring). Dictators try to square this circle with things like private opinion polling or petition systems, but these capture a small slice of the potentially destabiziling moods circulating in the body politic.

Enter AI: back in 2018, Yuval Harari proposed that AI would supercharge dictatorships by mining and summarizing the public mood — as captured on social media — allowing dictators to tack into serious discontent and diffuse it before it erupted into unequenchable wildfire:

https://www.theatlantic.com/magazine/archive/2018/10/yuval-noah-harari-technology-tyranny/568330/

Harari wrote that “the desire to concentrate all information and power in one place may become [dictators] decisive advantage in the 21st century.” But other political scientists sharply disagreed. Last year, Henry Farrell, Jeremy Wallace and Abraham Newman published a thoroughgoing rebuttal to Harari in Foreign Affairs:

https://www.foreignaffairs.com/world/spirals-delusion-artificial-intelligence-decision-making

They argued that — like everyone who gets excited about AI, only to have their hopes dashed — dictators seeking to use AI to understand the public mood would run into serious training data bias problems. After all, people living under dictatorships know that spouting off about their discontent and desire for change is a risky business, so they will self-censor on social media. That’s true even if a person isn’t afraid of retaliation: if you know that using certain words or phrases in a post will get it autoblocked by a censorbot, what’s the point of trying to use those words?

The phrase “Garbage In, Garbage Out” dates back to 1957. That’s how long we’ve known that a computer that operates on bad data will barf up bad conclusions. But this is a very inconvenient truth for AI weirdos: having given up on manually assembling training data based on careful human judgment with multiple review steps, the AI industry “pivoted” to mass ingestion of scraped data from the whole internet.

But adding more unreliable data to an unreliable dataset doesn’t improve its reliability. GIGO is the iron law of computing, and you can’t repeal it by shoveling more garbage into the top of the training funnel:

https://memex.craphound.com/2018/05/29/garbage-in-garbage-out-machine-learning-has-not-repealed-the-iron-law-of-computer-science/

When it comes to “AI” that’s used for decision support — that is, when an algorithm tells humans what to do and they do it — then you get something worse than Garbage In, Garbage Out — you get Garbage In, Garbage Out, Garbage Back In Again. That’s when the AI spits out something wrong, and then another AI sucks up that wrong conclusion and uses it to generate more conclusions.

To see this in action, consider the deeply flawed predictive policing systems that cities around the world rely on. These systems suck up crime data from the cops, then predict where crime is going to be, and send cops to those “hotspots” to do things like throw Black kids up against a wall and make them turn out their pockets, or pull over drivers and search their cars after pretending to have smelled cannabis.

The problem here is that “crime the police detected” isn’t the same as “crime.” You only find crime where you look for it. For example, there are far more incidents of domestic abuse reported in apartment buildings than in fully detached homes. That’s not because apartment dwellers are more likely to be wife-beaters: it’s because domestic abuse is most often reported by a neighbor who hears it through the walls.

So if your cops practice racially biased policing (I know, this is hard to imagine, but stay with me /s), then the crime they detect will already be a function of bias. If you only ever throw Black kids up against a wall and turn out their pockets, then every knife and dime-bag you find in someone’s pockets will come from some Black kid the cops decided to harass.

That’s life without AI. But now let’s throw in predictive policing: feed your “knives found in pockets” data to an algorithm and ask it to predict where there are more knives in pockets, and it will send you back to that Black neighborhood and tell you do throw even more Black kids up against a wall and search their pockets. The more you do this, the more knives you’ll find, and the more you’ll go back and do it again.

This is what Patrick Ball from the Human Rights Data Analysis Group calls “empiricism washing”: take a biased procedure and feed it to an algorithm, and then you get to go and do more biased procedures, and whenever anyone accuses you of bias, you can insist that you’re just following an empirical conclusion of a neutral algorithm, because “math can’t be racist.”

HRDAG has done excellent work on this, finding a natural experiment that makes the problem of GIGOGBI crystal clear. The National Survey On Drug Use and Health produces the gold standard snapshot of drug use in America. Kristian Lum and William Isaac took Oakland’s drug arrest data from 2010 and asked Predpol, a leading predictive policing product, to predict where Oakland’s 2011 drug use would take place.

[Image ID: (a) Number of drug arrests made by Oakland police department, 2010. (1) West Oakland, (2) International Boulevard. (b) Estimated number of drug users, based on 2011 National Survey on Drug Use and Health]

Then, they compared those predictions to the outcomes of the 2011 survey, which shows where actual drug use took place. The two maps couldn’t be more different:

https://rss.onlinelibrary.wiley.com/doi/full/10.1111/j.1740-9713.2016.00960.x

Predpol told cops to go and look for drug use in a predominantly Black, working class neighborhood. Meanwhile the NSDUH survey showed the actual drug use took place all over Oakland, with a higher concentration in the Berkeley-neighboring student neighborhood.

What’s even more vivid is what happens when you simulate running Predpol on the new arrest data that would be generated by cops following its recommendations. If the cops went to that Black neighborhood and found more drugs there and told Predpol about it, the recommendation gets stronger and more confident.

In other words, GIGOGBI is a system for concentrating bias. Even trace amounts of bias in the original training data get refined and magnified when they are output though a decision support system that directs humans to go an act on that output. Algorithms are to bias what centrifuges are to radioactive ore: a way to turn minute amounts of bias into pluripotent, indestructible toxic waste.

There’s a great name for an AI that’s trained on an AI’s output, courtesy of Jathan Sadowski: “Habsburg AI.”

And that brings me back to the Dictator’s Dilemma. If your citizens are self-censoring in order to avoid retaliation or algorithmic shadowbanning, then the AI you train on their posts in order to find out what they’re really thinking will steer you in the opposite direction, so you make bad policies that make people angrier and destabilize things more.

Or at least, that was Farrell(et al)’s theory. And for many years, that’s where the debate over AI and dictatorship has stalled: theory vs theory. But now, there’s some empirical data on this, thanks to the “The Digital Dictator’s Dilemma,” a new paper from UCSD PhD candidate Eddie Yang:

https://www.eddieyang.net/research/DDD.pdf

Yang figured out a way to test these dueling hypotheses. He got 10 million Chinese social media posts from the start of the pandemic, before companies like Weibo were required to censor certain pandemic-related posts as politically sensitive. Yang treats these posts as a robust snapshot of public opinion: because there was no censorship of pandemic-related chatter, Chinese users were free to post anything they wanted without having to self-censor for fear of retaliation or deletion.

Next, Yang acquired the censorship model used by a real Chinese social media company to decide which posts should be blocked. Using this, he was able to determine which of the posts in the original set would be censored today in China.

That means that Yang knows that the “real” sentiment in the Chinese social media snapshot is, and what Chinese authorities would believe it to be if Chinese users were self-censoring all the posts that would be flagged by censorware today.

From here, Yang was able to play with the knobs, and determine how “preference-falsification” (when users lie about their feelings) and self-censorship would give a dictatorship a misleading view of public sentiment. What he finds is that the more repressive a regime is — the more people are incentivized to falsify or censor their views — the worse the system gets at uncovering the true public mood.

What’s more, adding additional (bad) data to the system doesn’t fix this “missing data” problem. GIGO remains an iron law of computing in this context, too.

But it gets better (or worse, I guess): Yang models a “crisis” scenario in which users stop self-censoring and start articulating their true views (because they’ve run out of fucks to give). This is the most dangerous moment for a dictator, and depending on the dictatorship handles it, they either get another decade or rule, or they wake up with guillotines on their lawns.

But “crisis” is where AI performs the worst. Trained on the “status quo” data where users are continuously self-censoring and preference-falsifying, AI has no clue how to handle the unvarnished truth. Both its recommendations about what to censor and its summaries of public sentiment are the least accurate when crisis erupts.

But here’s an interesting wrinkle: Yang scraped a bunch of Chinese users’ posts from Twitter — which the Chinese government doesn’t get to censor (yet) or spy on (yet) — and fed them to the model. He hypothesized that when Chinese users post to American social media, they don’t self-censor or preference-falsify, so this data should help the model improve its accuracy.

He was right — the model got significantly better once it ingested data from Twitter than when it was working solely from Weibo posts. And Yang notes that dictatorships all over the world are widely understood to be scraping western/northern social media.

But even though Twitter data improved the model’s accuracy, it was still wildly inaccurate, compared to the same model trained on a full set of un-self-censored, un-falsified data. GIGO is not an option, it’s the law (of computing).

Writing about the study on Crooked Timber, Farrell notes that as the world fills up with “garbage and noise” (he invokes Philip K Dick’s delighted coinage “gubbish”), “approximately correct knowledge becomes the scarce and valuable resource.”

https://crookedtimber.org/2023/07/25/51610/

This “probably approximately correct knowledge” comes from humans, not LLMs or AI, and so “the social applications of machine learning in non-authoritarian societies are just as parasitic on these forms of human knowledge production as authoritarian governments.”

The Clarion Science Fiction and Fantasy Writers’ Workshop summer fundraiser is almost over! I am an alum, instructor and volunteer board member for this nonprofit workshop whose alums include Octavia Butler, Kim Stanley Robinson, Bruce Sterling, Nalo Hopkinson, Kameron Hurley, Nnedi Okorafor, Lucius Shepard, and Ted Chiang! Your donations will help us subsidize tuition for students, making Clarion — and sf/f — more accessible for all kinds of writers.

Libro.fm is the indie-bookstore-friendly, DRM-free audiobook alternative to Audible, the Amazon-owned monopolist that locks every book you buy to Amazon forever. When you buy a book on Libro, they share some of the purchase price with a local indie bookstore of your choosing (Libro is the best partner I have in selling my own DRM-free audiobooks!). As of today, Libro is even better, because it’s available in five new territories and currencies: Canada, the UK, the EU, Australia and New Zealand!

[Image ID: An altered image of the Nuremberg rally, with ranked lines of soldiers facing a towering figure in a many-ribboned soldier's coat. He wears a high-peaked cap with a microchip in place of insignia. His head has been replaced with the menacing red eye of HAL9000 from Stanley Kubrick's '2001: A Space Odyssey.' The sky behind him is filled with a 'code waterfall' from 'The Matrix.']

Image: Cryteria (modified) https://commons.wikimedia.org/wiki/File:HAL9000.svg

CC BY 3.0 https://creativecommons.org/licenses/by/3.0/deed.en

 — 

Raimond Spekking (modified) https://commons.wikimedia.org/wiki/File:Acer_Extensa_5220_-_Columbia_MB_06236-1N_-_Intel_Celeron_M_530_-_SLA2G_-_in_Socket_479-5029.jpg

CC BY-SA 4.0 https://creativecommons.org/licenses/by-sa/4.0/deed.en

 — 

Russian Airborne Troops (modified) https://commons.wikimedia.org/wiki/File:Vladislav_Achalov_at_the_Airborne_Troops_Day_in_Moscow_%E2%80%93_August_2,_2008.jpg

“Soldiers of Russia” Cultural Center (modified) https://commons.wikimedia.org/wiki/File:Col._Leonid_Khabarov_in_an_everyday_service_uniform.JPG

CC BY-SA 3.0 https://creativecommons.org/licenses/by-sa/3.0/deed.en

[automaticclappingxboxterminator]

AUTOMATIC CLAPPING XBOX TERMINATOR GENISYS

[mc-tums-fog]

Under The Desert Sky

Pairing: Elliott Marston x GN! Reader

Chapter II: When Clusters of Stars Tell Stories

Chapter Summary: Every action has a reaction, that’s what you were taught at a young age. You just never figured your actions would cause Elliott Marston to have this kind of reaction.

Content Warnings For This Chapter: Period-Typical Racism (Mentions against the Aboriginal people and Native Americans)

Notes:

Wrote this chapter immediately after the first, and was proud with it initially. But now I'm not too sure. Did some minor rewrites but still, not too sure. I think that's just me second guessing myself, plus figuring out the exact order of events for the next chapters. I'm trying to trust the process gang.

Read on Ao3 or below the cut:

It took you about a week since the conversation between you and Elliott to notice a pattern and figure out what the catch was.

No, he didn’t lower your pay. Come payment day you found that it was the same as in the last two weeks. No, the workload hadn’t suddenly increased. It was like the other times, and you had already gotten used to it at this point. No, the men didn’t try their luck with getting back at you somehow. Comments and looks here and there, but it didn’t seem like they were going to carry out anything big. The only notable difference was how Coogan did his best to not talk to you, when he could help it. Not like you were complaining.

But maybe some of those things would have been more preferable. Because when you realize what the catch was, how minor and inconvenient it would be to others, it quickly spiraled your mind with questions that had no answers to them.

Elliott Marston would take any opportunity that he saw fit to interact with you in some way.

The first two days, you paid no mind and thought it was even reasonable. You had gotten into a fight with one of his men after all. You figured this was just his way of making sure it didn’t happen again, or to show you “who was boss”. On these first two days, he was observing you more often than he had previously. Even coming up to you to talk about the work you were doing and going to be doing. This was something he did during the first few days of working for him, where he was directing you, but then he made his men give you orders after some time. If it was just this, you probably would have brushed it off.

But the third and fourth day was when you started to question his behavior. Sure, he’d watch you from afar, come up to speak about the work, same as before. But then there would be a few times throughout the day that he’d just… started talking to you.

At first, you thought he was just mulling to himself aloud. You never really caught into it on the third day, with being focused on your work. It was the fourth when you noted he said something when you walked by him to do another one of your tasks. You paid it no mind. It wasn’t until you had walked past him again a second time that you completely registered that he was talking to you. Not to himself . To you . And only then did you register how irregular that was. In the past, if you happened to walk by him, he wouldn’t say anything. Just a quick look and go back to whatever he was doing. Unless the heat was really getting to you, you never recalled him doing this before.

“I’m sorry, did you need me to do something?” You weren’t exactly kind in your tone like you were previously whenever you asked that question, in case you didn’t hear one of the workers on the ranch talk to you the first time. So, you figured he wanted you to do something, and you didn’t realize since you were so focused.

From his front porch leaning on one of his pillars, he studied you for a moment. Once again wearing that unreadable expression, which was even harder to see under his hat.

“You don’t seem to pay much attention to your surroundings when working.”

You didn’t know what to make of that… statement? It didn’t sound like a question, but you weren’t sure what kind of observation that was, besides an obvious one.

“I pay attention when something or someone needs my attention.” Was all you could offer, wanting to end the conversation soon.

“From what I was told, it seemed like the men didn’t need your attention when talking among themselves earlier this week.”

You didn’t even try to hide your annoyance when he said that.

“I was giving the two Aboriginal women you have on your grounds attention. He only got my full attention after his comments about my family,” You wiped off some of the dirt that had been forming on your clothes. Not like it mattered; they would get dirty again. “Was that all, or may I get back to work?”

Was it a bit stupid and dangerous to give him mouth even after he was gracious with allowing you to stay? Sure. But you couldn’t really give a damn. You wanted to earn your paycheck, and the sooner you could get through the days, the sooner you’d get it.

You expected him to continue on whatever else he had on his mind just to irritate you and regain control of the situation. Instead, he gave a quick, dismissive nod. And so, you left, wanting to put that interaction aside and focus on what you had left to do.

But it didn’t stop with that. From the fourth and fifth day he continued to do this every time you walked by him. He was still doing his previous routine of watching you from afar and coming up to you directly to tell you what to do. But now he would add these small comments if you happened to be nearby while doing your work. It wasn’t even about the fight at that point. He would make comments about anything. The particular gun he carried in his holster that day. Deserters that were still on the loose. The Australian land in general.

You gave curt replies because you just wanted to stay focused on your work. But even with the small amount of replies you did give he would somehow make do and continue on with whatever he was going on about. And not totally wanting to push your luck into waving him off without the risk of your job security, you decided to listen. You figured, if he was the one to initiate the conversation in the first place, then he shouldn’t be mad if hardly any work managed to get done that day. Plus, he was always on his porch when talking, and if the sun was angled right and you were standing in the correct position, the shade would cover you up. So more for your benefit, you listened.

…Admittedly, you found some of his topics interesting to listen to. In some ways that statement on being a student was correct. He sounded intelligent with what he had to say. Whenever he talked about America, he was correct on a number of things. But some areas you knew he wasn’t.

And maybe it was a mistake on your part for the following events that would occur, but you decided to contest the stuff he was wrong about on the fifth day.

“The tribes did uphold those treaty deals.” You said in response to how America would often negotiate treaties among the different tribal groups. “The only reason some of them were broken was because the army kept infesting their lands.”

He must’ve not expected you to say anything at all that weren’t just replies to end the conversation, as he looked at you with just a hint of being surprised.

“Where did you hear that nonsense?”

“That nonsense ,” You gave him a look. “I witnessed. When working on one of the farms in America the owner became close with one of the tribes nearby. They would make trades, giving them crops for some herbal medicine for his animals that got sick.”

You leaned one of those pillars facing more away from Elliott, who had been sitting nearby on a chair.

“The head of that tribe would come and talk about a treaty that had been going on that the military kept breaking. He wanted the farms’ owner to be a witness to one of these meetings, and I came along as I would often be the one making the deliveries to the camp.”

You shook your head as you looked down. You hadn’t noticed that Elliott stopped what he’d been doing, cleaning his gun, and gave his full attention to you.

“Didn’t matter though. The military didn’t listen to our testimonies of our firsthand accounts, where we knew they didn’t break it. They were disrespectful the whole time. The tribe was forced to move once the military took over it. Then the farm went to shit because they couldn’t get the medicine for the animals.”

You thought about the farmer and the tribe’s leader. You hoped they, and their families were doing good while you were down here. It was never easy for you to make connections with other people, with how they treated your parents. But they were one of the few that showed kindness to your troubles.

“The owner of the farm never got help from the military?”

You looked over at Elliott. For once, you could hear just the slightest indication of an emotion that wasn’t stern. He sounded like he cared about whatever happened to him.

You snapped out of that observation and shrugged. “Sometimes they tried to provide some medicine, with the exchange of us giving them some crops. But it didn’t work as well as the herbal medicine, so he stopped dealing with them all together. Didn’t make the army men happy but I don’t think he really cared all that much.”

He looked down, his brows furrowing a bit to the point where you could see a line forming between them. It was an indication he was in thought of the story you told. You noticed how he would often do this, trying to dissect and think about what it was people had said to him, and particularly with you. In a strange way, you found this… respectable, was the best your compliment for him was going to get. Most people don’t take the time to fully process what they or others say to them. Unless it was in the heat of the moment when he was having a quick and rushed discussion, he still took the time to consider what he was going to say, or what others said.

And you realized it wasn’t just in his words that he did this. You realized how he would do this for his actions. The way he moved had a certain precision about it. Even in a frazzled state that you would sometimes see him in there was still somehow an air of thought that surrounded his movements. You could see how he became a ranch owner and a skilled gunslinger; with the few times you saw him using his gun before. It made sense. In his line of work, he couldn’t afford to be careless.

You hadn’t realized how you were staring intently at him mulling this over until one of his men called you over for help getting control over a wild horse they found and wanted to tame. You blinked as you looked over, and quickly rushed from down the porch, wincing a bit as your ribs were still in pain. You must’ve spent too long in the shade because you instantly felt your face heat up as you jogged away from the porch and into the sunlight.

The sixth day followed this similar format, where he would talk to you by his porch when you were nearby. Whenever you did, something about it made you feel like you could challenge him a bit more. And for whatever reason, he allowed it, and would challenge you back. In this back and forth you would learn a few things more about his country and him with yours. He would learn about the city life you had, and he would talk about the ranch life. You didn’t know what to make of these conversations after the first few times.

And you found yourself doing something you hadn’t expected yourself to do at the start of the seventh day.

You made conversation with him first.

It was early enough in the morning, and you were already getting ahead in some areas, thanks to working a bit longer in the evening prior. You put some water on your face and noted Elliott leaving his home to check on his horse. He did this every morning, he never wanted anyone else to take care of his steed, Maverick as he called him.

But as he was walking down the steps and to the stables, you felt like his appearance was off. You couldn’t pinpoint how though. From where you were standing you were a great distance away from him, but even so, you could still tell something wasn’t right. As the sun got a bit higher, casting more light onto the ranch, and onto him, it suddenly hit you.

He wasn’t wearing all black attire. His vest was a deeper shade of a maroon.

You tried to think if he always had a vest like that. With how busy you were with your tasks, you could never really look at him all that much, other than when he was talking to you. Or when you could catch some conversations between him and his men. But at those times you could only recall him wearing something black. Sometimes it was a full black coat with a vest and white button up to contrast it. Other times it was just his black vest and white button up. Rarely did you ever see him without a vest. If he did always have this one, you never noticed until now.

So, you felt like you had to make a comment on it. It was rational to you. Besides, you already had to go and feed the animals that were close by anyway.

When you walked by carrying the scraps for the livestock while he was still tending to his horse, you said something.

“I thought you only dressed like the grim reaper.”

He stopped brushing his horse’s hair and looked over at you. His look of confusion was clear as day. You clarified as you kept feeding the animals.

“I didn’t think you even liked any other color other than black is what I mean.”

He took note of his vest now and seemed to ponder further with what you said.

“I’ve worn this vest before.” He replied.

“Well, I’ve only seen you wear black. I never noticed this vest.”

“Really.”

You didn’t catch how he didn’t frame it as a question, or how thoughtful he sounded. You were more focused on the idea that he could like other things, how implausible that seemed.

“I’m not sure how you could mostly wear black, when the sun is so damn hot.” You said it more to yourself than to him, trying to rationalize a common thought you had about his choice of clothing aloud.

“It’s proper attire that suits my character well.”

“For a funeral maybe.”

You didn’t realize how much you had gone back and forth on this singular topic on something so small. You didn’t even realize that as you were working, he would follow you to finish this conversation. Which delved into a conversation on what you liked to wear, which was whatever was practical, you were never too picky growing up. That led to him rationalizing that his clothing was practical in getting to his weaponry quickly. That led you to asking about what shooting a gun was like, and him explaining how even being an expert he still found himself closing his eyes as a reaction whenever it went off. Which made you think about the times that you did see him use his gun, and he was right.

Throughout the morning it went like this, him following you around with you never phasing in doing your work. At times he would need to leave and would excuse himself. But then he’d get back right to wherever the two of you left off. By midday you didn’t realize how exhausted you were. A bit odd, since you’ve never gotten tired this quickly before. By the afternoon, when it was time to send out letters by one of the workers who were already going into town for a supply run, he let you know the payment you were sending to your family and gave you the leftover percentage to you personally. Noting it was the same, you were going to help the rest of the workers in loading up some crates for their journey. But before you could, Elliott stopped you and told you to get into some shade because the last thing he wanted was to drag another worker out of the sun.

You didn’t realize this was the first time he allowed you to have a break. Because if you did, it would’ve been a bigger deal. But you were exhausted. So, you went to the lodge and crashed out for some time.

Upon waking up, you could see that the sun had started to go down. You tried to get your bearings on why you went to bed earlier than usual when you could hear two men talking outside to each other, as one of the windows was partially open. They seemed like they were in the middle of a conversation when they came near the lodge.

“-y arm must’ve pulled something when carrying the crates,” The voice, you could recognize to be Dobkin, groaned. “Should’ve gotten the rookie to do it.”

You immediately knew he was talking about you. He’d called you that before, with how you were one of the newer workers for the ranch, despite now being here for a couple of months. You were about to ignore it when the other voice, you placed to be O’Flynn, spoke next.

“If you did, you’d be cuttin’ into Mr. Martson’s ‘buddy’ time.” He snickered.

You perked up at that. What did he mean by “buddy time”?

“If the rookie has time to be talking with Mr. Marston, then the rookie can take the time to actually do some work.”

You had been doing work. That’s all you have been doing for these past few months. In your still tired state, you wondered why he would even phrase it like that.

“You think it’s odd too, right? How they talk to each other?” O’Flynn asked. “I mean, that weasel beats the shit out of Coogan, and Mr. Marston doesn’t do anything about it.”

“It’s none of my business what Mr. Marston decides to do.” Dobkin responded back. He groaned again. “Christ this arm. Let’s go find a bottle.”

“All I’m saying is,” O’Flynn said as Dobkin’s footsteps were walking away. “Either Mr. Marston is planning a proper funeral or a wedding.”

Their voices grew distant, and you thought about what they said. You knew O’Flynn was just being a little shit saying that last part. But your conversations with Elliott did give you a pause. You really thought about it for a moment as you laid on the cot, looking up at the ceiling.

The past few days he had been observing you more and interacting with you. He sought you out to talk to you, as if he wanted to. And you realized that didn’t make any sense. Here you are working at his ranch even after beating up a man and not holding back when speaking either. How was it that you were still standing and breathing even? As best as you could with your ribs.

And more importantly, you had engaged with him back. You talked with him. Today you even talked to him first. Why?

You rubbed your eyes, your face not hurting as much when you touched it. It didn’t make sense to you. His behavior toward you and yours to him. You tried to rest further. But as the others would come in and rest as well, as much as you closed your eyes, it was hard to sleep with the lingering questions still in your mind. It had gotten well into the night, but you decided that you couldn’t sleep and put on your work outfit just so you had something warm to be in when outside.

You went back to the spot you were at the last time you gazed at the stars, only you stood up this time, as if it would get you closer to them.

They were still as beautiful as ever. And while they brought you a sense of comfort, they couldn’t answer any of the questions you had. But even if they could, it’s not like they would know what to do in your shoes anyway. They could just be there to listen, and you figured that satisfied you enough. That, and you could simply appreciate their beauty.

It amazed you on what you’d missed out on. It was the one thing you could give Australia credit for. You’ve never seen anything like this. You didn’t even think you would ever see something like this.

You stood there, for how long you weren’t sure, just basking in the starlight. You thought about making this a thing to do every night, just looking at the stars. Though you worried it would end up losing its beauty, if you had too much of one good thing. Suddenly, your ears picked up the precise footsteps coming from behind. You could easily recognize them, and it brought you back to your dilemma and questions.

Like last time, he took the place by your left, only not as towering as he was when you were sitting down like before. Upon leaning his arms on the fence’s wooden planks, he nodded to himself in satisfaction. Then he looked out to where you had been looking, right at the stars.

He didn’t rush to say anything like he had when this first happened. He took the time to let the quiet set back in before he could break it. Which of course, he did eventually.

“You know, I’m not paying you to stargaze.” He spoke.

Of course he had to act like this was above him too.

“I’m not telling you to.” You replied back, not even looking at him.

“You couldn’t tell me what you could get paid for any-” He started to go on before you cut him off. You decided that the only way you were going to get answers to your questions was from him.

“Why have you been trying to talk to me?”

He seemed to act like this was the first time you were blunt with him, as if you hadn’t been talking to him bluntly ever since last week. It annoyed you even more. So you made that clear. 

“Don’t act like you don’t get why this confuses me. You’re smarter than that. I beat up one of your men, I talked back to you when you tried to talk to me about it. And even if this was to just uphold a deal with my cousin, you still go out of your way to try to talk to me when neither of us have any reason to.” 

You finally looked over to him. “Why?”

He didn’t say anything at first. Despite being in the dark, without his hat on, and being about three feet from him, you could make out his face. Once again, he kept studying you and seemed to be contemplating what he was going to say next. He turned his head back to the stars and seemed to be contemplating them as well. Like the answer was up there.

You didn’t sense any heaviness in the silence like the last time you both talked under the stars. You’d almost call it peaceful. At least, there were no warning signs yet of anything dangerous to come. So, you waited. And eventually he spoke.

“My mother used to talk about how the stars told stories.”

You gave him a quizzical look.

He must’ve seen your reaction, because he let out a very light chuckle. You realized then that this was the first time you heard any kind of laughter coming out of him. Or even a broader smile than his cocky smirk he would have on from time to time.

“Sounds completely ridiculous right? But she wasn’t wrong. Certain stars have formations that if you really looked closely enough with the right materials, you could see them. They’re called constellations. Each one relates back to a character in a story rooted in Greek mythology.”

You looked back up to the sky. Personally, you couldn’t see anything distinct about them, besides that they were all beautiful. You saw some clusters that were brighter than others, sure. Nothing that told you a story though.

“I always found that interesting,” He continued. “That if you observed and interacted with the stars long enough, they would tell you a story.”

He looked over at you, causing you to look over to him as well.

“I would like to know yours. And I hope you’d like to know mine.”

It was the first time he said anything like it was an offer, rather than a demand. Like he was giving you a say in the matter. And it was said in the same tone that you briefly caught when claiming that you didn’t have the right to make that judgement about how he wasn’t better than anyone. 

It sounded like sincerity, if you had to give it a name.

You had already thought he knew enough, what he needed to know before hiring you. That your family was in financial trouble, and you were the only one who could work and could do the work well. That was it, the gist of what your cousin said, though including more of the circumstances on why you were the only one working. You didn’t even think he wanted to know more. But you also found yourself more focused on the latter half of what he said. How he hoped you would get to know him.

And you really hadn’t thought you would want to get to know him more. Why would you? You felt like he made his character and who he was pretty clear.

But then again…

You realized just how much you were engaging back with him. The details you remembered about these past conversations between the two of you. You talked to him more within the past week than you had within the past months since you’ve been here.

Did you want to get to know him more?

Something about that question made your heart miss a beat. But you couldn’t explain why.

You looked back up to the stars. Like they would have an answer for you. Something. Anything. You tried to clear your head and with whatever thought popped up, whatever your gut instincts were telling you, you would go with it. You couldn’t see how your choice would screw you over if he was giving you the option.

And so, after taking a moment, that’s what you did. You went with whatever came to your mind first. With the stars being witnesses to it.

[hackeocafe]

How To Learn Math for Machine Learning FAST (Even With Zero Math Background)

I dropped out of high school and managed to became an Applied Scientist at Amazon by self-learning math (and other ML skills). In this video I'll show you exactly how I did it, sharing the resources and study techniques that worked for me, along with practical advice on what math you actually need (and don't need) to break into machine learning and data science.

[queen-mabs-revenge]

communist generative ai boosters on this website truly like

[ai-innova7ions]

Neturbiz Enterprises - AI Innov7ions

Our mission is to provide details about AI-powered platforms across different technologies, each of which offer unique set of features. The AI industry encompasses a broad range of technologies designed to simulate human intelligence. These include machine learning, natural language processing, robotics, computer vision, and more. Companies and research institutions are continuously advancing AI capabilities, from creating sophisticated algorithms to developing powerful hardware. The AI industry, characterized by the development and deployment of artificial intelligence technologies, has a profound impact on our daily lives, reshaping various aspects of how we live, work, and interact.

[nixcraft]

I'm applying for a "shift manager position at a data center operations that hosts AI applications," and this is what they want to know 😂👇

[datascienceunicorn]

The Data Scientist Handbook 2024

HT @dataelixir

[raptorstudiesstuff]

Life update -

Hi, sorry for being MIA for a while and I'll try to update here more frequently. Here's a general update of what I've been up to.

Changed my Tumblr name from studywithmeblr to raptorstudiesstuff. Changed my blog name as well. I don't feel comfortable putting my real name on my social media platforms so I'm going by 'Raptor' now.

💻 Finished the Machine Learning-2 and Unsupervised Learning module along with projects. Got a pretty good grade in both of them and my overall grade went up a bit.

📝 Started applying for data science internships and jobs but got rejected from most of the companies I applied to... 😬

I'll start applying again in a week or two with a new resume. Let me know any tips I can use to not get rejected. 😅

💻 Started SQL last week and really enjoying it. I did get a bad grade on an assignment though. Hope I can make up for it in the final quiz. 🤞

🏥 Work has been alright. We're a little less staffed than usual this week but I'm trying not to stress too much about it.

📖 Currently reading Discworld #1 - The Color of Magic. More than halfway through.

📺 Re-watched the Lord of The Rings movies and now I'm compelled to read the books or rewatch the Hobbit movies.

"There's good in this world, Mr Frodo, and it's worth fighting for." This scene had me in tears and I really needed to hear that..

📺 Watched the first 4 episodes of First Kill on Netflix and I don't know what I was doing to myself. The writing and dialogue is so cheesy and terrible. The acting is okay-ish. It's so bad that it turned out to be quite hilarious. Laughed the whole time.

🎧 Discovered a new (for me) song that I'm obsessed with right now - Mirrors by Justin Timberlake.

📷 Took some really cool pics on my camera..

Might start the 100 days productivity challenge soon as that is the only way I find myself to be consistent.

Peace ✌️

Raptor

PS. Please don't repost any of my pictures without permission.

[mindblowingscience]

Cornell quantum researchers have detected an elusive phase of matter, called the Bragg glass phase, using large volumes of X-ray data and a new machine learning data analysis tool. The discovery settles a long-standing question of whether this almost–but not quite–ordered state of Bragg glass can exist in real materials. The paper, "Bragg glass signatures in PdxErTe3 with X-ray diffraction Temperature Clustering (X-TEC)," is published in Nature Physics. The lead author is Krishnanand Madhukar Mallayya, a postdoctoral researcher in the Department of Physics in the College of Arts and Sciences (A&S). Eun-Ah Kim, professor of physics (A&S), is the corresponding author. The research was conducted in collaboration with scientists at Argonne National Laboratory and at Stanford University.

Continue Reading.

[datasciencewithmohsin]

Simple Linear Regression in Data Science and machine learning

Simple linear regression is one of the most important techniques in data science and machine learning. It is the foundation of many statistical and machine learning models. Even though it is simple, its concepts are widely applicable in predicting outcomes and understanding relationships between variables.

This article will help you learn about:

1. What is simple linear regression and why it matters.

2. The step-by-step intuition behind it.

3. The math of finding slope() and intercept().

4. Simple linear regression coding using Python.

5. A practical real-world implementation.

If you are new to data science or machine learning, don’t worry! We will keep things simple so that you can follow along without any problems.

What is simple linear regression?

Simple linear regression is a method to model the relationship between two variables:

1. Independent variable (X): The input, also called the predictor or feature.

2. Dependent Variable (Y): The output or target value we want to predict.

The main purpose of simple linear regression is to find a straight line (called the regression line) that best fits the data. This line minimizes the error between the actual and predicted values.

The mathematical equation for the line is:

Y = mX + b

: The predicted values.

: The slope of the line (how steep it is).

: The intercept (the value of when).

Why use simple linear regression?

click here to read more https://datacienceatoz.blogspot.com/2025/01/simple-linear-regression-in-data.html

[jcmarchi]

Researchers detect a new molecule in space

New Post has been published on https://thedigitalinsider.com/researchers-detect-a-new-molecule-in-space/

Researchers detect a new molecule in space

New research from the group of MIT Professor Brett McGuire has revealed the presence of a previously unknown molecule in space. The team’s open-access paper, “Rotational Spectrum and First Interstellar Detection of 2-Methoxyethanol Using ALMA Observations of NGC 6334I,” appears in April 12 issue of The Astrophysical Journal Letters.

Zachary T.P. Fried, a graduate student in the McGuire group and the lead author of the publication, worked to assemble a puzzle comprised of pieces collected from across the globe, extending beyond MIT to France, Florida, Virginia, and Copenhagen, to achieve this exciting discovery. 

“Our group tries to understand what molecules are present in regions of space where stars and solar systems will eventually take shape,” explains Fried. “This allows us to piece together how chemistry evolves alongside the process of star and planet formation. We do this by looking at the rotational spectra of molecules, the unique patterns of light they give off as they tumble end-over-end in space. These patterns are fingerprints (barcodes) for molecules. To detect new molecules in space, we first must have an idea of what molecule we want to look for, then we can record its spectrum in the lab here on Earth, and then finally we look for that spectrum in space using telescopes.”

Searching for molecules in space

The McGuire Group has recently begun to utilize machine learning to suggest good target molecules to search for. In 2023, one of these machine learning models suggested the researchers target a molecule known as 2-methoxyethanol. 

“There are a number of ‘methoxy’ molecules in space, like dimethyl ether, methoxymethanol, ethyl methyl ether, and methyl formate, but 2-methoxyethanol would be the largest and most complex ever seen,” says Fried. To detect this molecule using radiotelescope observations, the group first needed to measure and analyze its rotational spectrum on Earth. The researchers combined experiments from the University of Lille (Lille, France), the New College of Florida (Sarasota, Florida), and the McGuire lab at MIT to measure this spectrum over a broadband region of frequencies ranging from the microwave to sub-millimeter wave regimes (approximately 8 to 500 gigahertz). 

The data gleaned from these measurements permitted a search for the molecule using Atacama Large Millimeter/submillimeter Array (ALMA) observations toward two separate star-forming regions: NGC 6334I and IRAS 16293-2422B. Members of the McGuire group analyzed these telescope observations alongside researchers at the National Radio Astronomy Observatory (Charlottesville, Virginia) and the University of Copenhagen, Denmark. 

“Ultimately, we observed 25 rotational lines of 2-methoxyethanol that lined up with the molecular signal observed toward NGC 6334I (the barcode matched!), thus resulting in a secure detection of 2-methoxyethanol in this source,” says Fried. “This allowed us to then derive physical parameters of the molecule toward NGC 6334I, such as its abundance and excitation temperature. It also enabled an investigation of the possible chemical formation pathways from known interstellar precursors.”

Looking forward

Molecular discoveries like this one help the researchers to better understand the development of molecular complexity in space during the star formation process. 2-methoxyethanol, which contains 13 atoms, is quite large for interstellar standards — as of 2021, only six species larger than 13 atoms were detected outside the solar system, many by McGuire’s group, and all of them existing as ringed structures.  

“Continued observations of large molecules and subsequent derivations of their abundances allows us to advance our knowledge of how efficiently large molecules can form and by which specific reactions they may be produced,” says Fried. “Additionally, since we detected this molecule in NGC 6334I but not in IRAS 16293-2422B, we were presented with a unique opportunity to look into how the differing physical conditions of these two sources may be affecting the chemistry that can occur.”