Three Major Physics Discoveries and Counting • Hacker News Board

Re: Three Major Physics Discoveries and Counting

#11

Post by lokimedes » Sun, Jul 22, 2018, 12:16 PM UTC

frumiousirc wrote: post #10

DuskStar wrote: post #4

Earlier quoted context omitted.

In my opinion, CERN is one of the few groups out there that can call their data set "big data" without talking out of their ass. (If it can fit in RAM, it ain't Big Data. Multiple TBs fit in RAM) Their detectors produce something on the order of a petabyte per second which is then pared back immensely to become something that's actually storable. Most of the machine learning I've heard of involving CERN is in reducin…

Although LHC experiments are toward the top of the heap in terms of data rates, many particle and astronomical experiments produce "big data". One quarter of the DUNE experiment will acquire about 50 EB/year (that's exabyte), outputting about 10 PB/year to tape. LSST will produce data in the few PB/year range.

Not to belittle your examples, but in a historical context the LHC held a specific position. Remember that the ATLAS/CMS/ALICE/LHCb experiments started recording data at 10 GB/s back in 2008. Now, ten years later it is only natural that large data rates are becoming the norm.

Re: Three Major Physics Discoveries and Counting

#12

Post by konschubert » Sun, Jul 22, 2018, 12:19 PM UTC

lokimedes wrote: post #9

sempron64 wrote: post #2

Interestingly, she mentions using machine learning for analysis of the Higgs Boson data. Does anyone here know more about this?

We (Member of the ATLAS Experiment, 2008-2016) used Neural networks for trigger decisions (to record or ignore a collision) and Boosted Decision Trees were the big thing among many ATLAS physicists back around 2008-9, so that was also used quite a bit. For the experiments at the LHC you can consider the actual analysis of data a Multivariate Hypothesis Testing exercise. The thing is a counting experiment, you have a…

Sadly, the ML research in physics and in the rest of the world are extremely disconnected. To the point where one side has never heard of the tools that the other side uses all day.

I think the blame here is mostly on the science community which isn't paying much attention to ML tooling, best practices and research and instead keeps reinventing the wheel, over and over again.

Re: Three Major Physics Discoveries and Counting

#13

Post by lokimedes » Sun, Jul 22, 2018, 12:34 PM UTC

konschubert wrote: post #12

lokimedes wrote: post #9

Earlier quoted context omitted.

We (Member of the ATLAS Experiment, 2008-2016) used Neural networks for trigger decisions (to record or ignore a collision) and Boosted Decision Trees were the big thing among many ATLAS physicists back around 2008-9, so that was also used quite a bit. For the experiments at the LHC you can consider the actual analysis of data a Multivariate Hypothesis Testing exercise. The thing is a counting experiment, you have a…

Sadly, the ML research in physics and in the rest of the world are extremely disconnected. To the point where one side has never heard of the tools that the other side uses all day. I think the blame here is mostly on the science community which isn't paying much attention to ML tooling, best practices and research and instead keeps reinventing the wheel, over and over again.

That may have changed a bit now. I'm no longer part of the physics community, but it seems that the physicists at least are relying more and more on mainstream tools. The outward flow of knowledge comes more from ex-physicsts like myself, who work in industry. Most of us work in Tech, Fintech or other ML/Stats driven industries, where many reimplement what they have learned during their physics days.

Re: Three Major Physics Discoveries and Counting

#14

Post by [deleted] » Sun, Jul 22, 2018, 12:41 PM UTC

mathgenius wrote: post #6

abecedarius wrote: post #3

A intriguing remark near the end: We are also very excited about the near future, because we plan to start using quantum computing to do our data analysis.

It was nice to see this comment, because to me it means that she is always looking to the future & optimistic. One of the near-term applications of quantum computing is for machine learning. For example, the D-wave chip is for optimising a binary fitness function. We will soon see if it works out.

[deleted]

Re: Three Major Physics Discoveries and Counting

#15

Post by WilliamEdward » Sun, Jul 22, 2018, 12:41 PM UTC

abecedarius wrote: post #3

A intriguing remark near the end: We are also very excited about the near future, because we plan to start using quantum computing to do our data analysis.

I don't know how she could say this with any authority. One of the biggest roadblocks in quantum computing is finding a faster way to make quantum logic gates. So far they're only making them one by one, when tens of thousands are needed. We will not be using this tech anytime soon.

Re: Three Major Physics Discoveries and Counting

#16

Post by dguest » Sun, Jul 22, 2018, 5:43 PM UTC

lokimedes wrote: post #9

sempron64 wrote: post #2

Interestingly, she mentions using machine learning for analysis of the Higgs Boson data. Does anyone here know more about this?

We (Member of the ATLAS Experiment, 2008-2016) used Neural networks for trigger decisions (to record or ignore a collision) and Boosted Decision Trees were the big thing among many ATLAS physicists back around 2008-9, so that was also used quite a bit. For the experiments at the LHC you can consider the actual analysis of data a Multivariate Hypothesis Testing exercise. The thing is a counting experiment, you have a…

She's not talking about TMVA or RooStats when she says "Machine Learning": those would be "MVA" and "Statistics tools" in our jargon. She's talking about XGBoost[1].

[1]: https://xgboost.readthedocs.io/en/latest/

Re: Three Major Physics Discoveries and Counting

#17

Post by dguest » Sun, Jul 22, 2018, 6:11 PM UTC

sempron64 wrote: post #2

Interestingly, she mentions using machine learning for analysis of the Higgs Boson data. Does anyone here know more about this?

As others have commented we've been using tools like neural networks and boosted decisions trees for a long time. We have quite good simulation which tells us what particles would look like in our detector, but one thing our simulation tells us is that it's often really hard to tell the difference between a Higgs Boson and some other "background" process.

So the logic goes like this: if we trust our simulation, we can simulate the Higgs, and simulate the background, and then train a neural network to tell us which is which. Then we turn the network loose on our data. If it sees lots of things that look like Higgs, yay, we discovered something!

For machine learning tools, we had a few homegrown implementations that didn't get far beyond physics (probably because they weren't particularly user-friendly). But physicists would have referred to techniques like this as "Multivariate Analysis" (or "MVA") a few years ago.

More recently we've started to reach out more to industry and use their tools, which actually much nicer! What she's referring to here is one particular analysis her team contributed to [1], which relied on XGboost [2]. Beyond that we've used Keras a fair bit to identify some types of particles.

[1]: https://arxiv.org/abs/1806.00425 [2]: https://xgboost.readthedocs.io/en/latest/

Re: Three Major Physics Discoveries and Counting

#18

Post by guitarbill » Sun, Jul 22, 2018, 7:09 PM UTC

konschubert wrote: post #12

lokimedes wrote: post #9

Earlier quoted context omitted.

We (Member of the ATLAS Experiment, 2008-2016) used Neural networks for trigger decisions (to record or ignore a collision) and Boosted Decision Trees were the big thing among many ATLAS physicists back around 2008-9, so that was also used quite a bit. For the experiments at the LHC you can consider the actual analysis of data a Multivariate Hypothesis Testing exercise. The thing is a counting experiment, you have a…

Sadly, the ML research in physics and in the rest of the world are extremely disconnected. To the point where one side has never heard of the tools that the other side uses all day. I think the blame here is mostly on the science community which isn't paying much attention to ML tooling, best practices and research and instead keeps reinventing the wheel, over and over again.

> I think the blame here is mostly on the science community which isn't paying much attention to ML tooling, best practices and research

While the science community doesn't have a great track record for quality software engineering, that's an awfully arrogant position.

Most ML tooling sucked, and it's only just getting better in terms of usability. But even then it's very software engineer-y in the worst kind of way, e.g. "Coming soon: PyTorch 1.0 ready for research and production" [0]. Great.

If you're doing research in one field, you don't really want to spend the time to become an expert in another one just to do some analysis. What you want is tools you can reliably (ab)use, like maths. But there often isn't a straight-forward way of getting the uncertainties on values output from many ML constructs.

Yes, the term "statistical learning" has been around since at least 2001. But it isn't widely known/talked about/understood, and most trendy ML "tutorials" gloss over it completely. Maybe this is unfair criticism. After all, most ML applications in software don't require that stricter treatment, and why should somebody playing around with ML be burdened with this rigorousness? At the same time, it's easy to come away from ML thinking "I don't understand this at all, it's a black box, it doesn't do what I need it to".

And we haven't even talked about what a pain reproducibility is in ML.

> instead keeps reinventing the wheel, over and over again.

If people keep reinventing it, maybe the problem isn't the people... yeah, physicists don't write great code (guilty), but ML tooling is full of hype and currently feels a bit Javascript-y.

[0] https://pytorch.org/2018/05/02/road-to-1.0.html