Technology

68772 readers

4368 users here now

This is a most excellent place for technology news and articles.

Our Rules

Follow the lemmy.world rules.
Only tech related news or articles.
Be excellent to each other!
Mod approved content bots can post up to 10 articles per day.
Threads asking for personal tech support may be deleted.
Politics threads may be removed.
No memes allowed as posts, OK to post as comments.
Only approved bots from the list below, this includes using AI responses and summaries. To ask if your bot can be added please contact a mod.
Check for duplicates before posting, duplicates may be removed
Accounts 7 days and younger will have their posts automatically removed.

Approved Bots

founded 2 years ago

MODERATORS

[email protected]

394

High school student uses AI to reveal 1.5 million previously unknown objects in space. (www.caltech.edu)

submitted 3 days ago by [email protected] to c/[email protected]

52 comments fedilink hide all child comments

you are viewing a single comment's thread
view the rest of the comments

[–] [email protected] 36 points 3 days ago* (last edited 3 days ago) (1 children)

I was hoping the article would tell us more about the technique he developed.

The model I implemented can be used for other time domain studies in astronomy, and potentially anything else that comes in a temporal format

All I gathered from it is that it is a time-series model.

[–] [email protected] 27 points 3 days ago* (last edited 3 days ago) (1 children)

I found his paper: https://iopscience.iop.org/article/10.3847/1538-3881/ad7fe6 (no paywall 😃)

From the intro:

VARnet leverages a one-dimensional wavelet decomposition in order to minimize the impact of spurious data on the analysis, and a novel modification to the discrete Fourier transform (DFT) to quickly detect periodicity and extract features of the time series. VARnet integrates these analyses into a type prediction for the source by leveraging machine learning, primarily CNN.

They start with some good old fashioned signal processing, before feeding the result into a neutral net. The NN was trained on synthetic data.

FC = Fully Connected layer, so they're mixing FC with mostly convolutional layers in their NN. I haven't read the whole paper, I'm happy to be corrected.