
Fast Supernovae Detection Using Neural Networks - dsr12
https://blog.tensorflow.org/2020/09/fast-supernovae-detection-using-neural-networks.html
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DiogenesKynikos
Automated detection of "transients" (anything that changes in the sky) will
become increasingly important as large surveys vacuum up ever more data.

LSST is supposed to get going in 2022, imaging the entire visible sky every
few days from the Atacama desert in Chile.[1] It's supposed to take a 3.2
Gigapixel image every 30 seconds or so, pretty much non-stop for 10 years.
There will be tons of interesting transients hidden in that stream of data,
and the key is flagging them as quickly as possible, so that other telescopes
can do targeted follow-up. LSST itself is really the discovery machine, but it
doesn't give much information about the transients it discovers. It just tells
you that the transients are there, and that you should take a look.

Unless we're going to have a room full of people going through 120 3.2
Gigapixel images an hour, comparing them with reference images of the same
part of the sky, we need algorithms to flag interesting transients. The
algorithms should give us their guess of what sort of transient we're looking
at (Type Ia supernova, Type IIb supernova, microlensing event, etc.) with
"this is nothing like anything I've been trained on" possibly being the most
interesting answer.

One of the big worries is that LSST will discover so many transients that
there won't be enough resources to follow them all up. There probably aren't
enough spectrographs installed worldwide to get a good spectrum of every
transient. That makes it critical to automatically ranki transients by novelty
or importance.

1\.
[https://en.wikipedia.org/wiki/Large_Synoptic_Survey_Telescop...](https://en.wikipedia.org/wiki/Large_Synoptic_Survey_Telescope)

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onhn
> astronomers use supernovae to measure distances, which is important for
> cosmologists to study, for instance, the expansion of the universe and dark
> energy.

AFAIK supernovae datasets are usually obtained as a survey, resulting in a
statistical sample which can be used to compute cosmological observables. Here
it seems that there is a new sample bias introduced by the neural network
classifier. Can this bias be accurately quantified?

~~~
gammarator
A perceptive question. Typically, this bias is assessed by injecting fake
supernovae into the images and seeing if they are correctly recovered.

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GistNoesis
How many are we expecting Supernovae detection are we expecting to detect ?
What percentage of these are the neural network able to successfully detect ?

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scott31
Calling something "fast" without any benchmarks should be a crime, punishable
by ~6 months prison for the team lead

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antognini
Presumably the benchmark here is waiting for a graduate student to wake up,
log on to their computer, and mark the image as having a supernova or not.

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amelius
Of course, from now on we won't detect any "that's odd" types of Supernovae
...

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siliconvalley1
Who said 100% of all future Supernova detection work will only be done by
algorithms?

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BurningFrog
All dictionaries I find online say "The plural form of supernova is supernovas
or supernovae."

I think we should phase out these pseudo latin plurals from English. They're
just annoying and were a bad idea when introduced in the 1700s.

~~~
Mayzie
“nova” is Latin though.

~~~
BurningFrog
It _came_ from latin, but it's now an English word.

Most English words are imported from other languages, but we conjugate them
according to English grammar, not the origin language. Except for pluralizing
latin nouns.

