7 ms·

Particle physicists turn to AI to cope with CERN’s collision deluge

132 points by okket 9y ago

JumpCrisscross 9y ago
Out of curiosity, what fraction of the Bitcoin network’s computing power would solve this computation wall?
- amelius 9y ago
  Is there even a bound on the computational power we'd need to make an advancement here?
  - JumpCrisscross 9y ago
    > Although these techniques would be able to work out the paths even after the upgrades, “the problem is, they are too slow”, says Cécile Germain, a computer scientist at the University of Paris South in Orsay. Without major investment in new detector technologies, LHC physicists estimate that the collision rates will exceed the current capabilities by at least a factor of 10. Sounds like the LHC’s physicists have an idea.
  - jrq 9y ago
    No, but CERN Is known to be careful with their PR. Presumably, and this is just my intuition speaking, a big enough cluster of computers would solve this, but they're taking an opportunity to experiment with different techniques and methods for this experiment, and that's pretty much it. If CERN had an unlimited budget, I suspect they'd do it however they did it before.
    - Analog24 9y ago
      This is not a problem that can be solved just by throwing more compute resources at it. It's not as simple as simply having too much data to process, the real issue is that each detector has a time resolution that goes down to about a nanosecond. If you get one collision per nanosecond then it's pretty straight forward to associate every one of the (possibly) million detector hits to a single event and reconstruct it accordingly. The issues arises when you have more than one event (i.e. collision) within each nanosecond window. You end up with detector readings for each event overlapping each other without a simple way of disambiguating them. This is called "pile up". When I last worked there in 2015 a typical pile up situation was having about 50 collisions per detector reading. It is no simple problem to simultaneously reconstruct 50 collisions from the same set of overlapping detector measurements.
      - cfadvan 9y ago
        Just to insert the usual analogy, if a single collision is the proverbial smashing of two clocks, and then trying to discern their makeup from the wreckage, this is akin to smashing dozens of clocks at the same time. How can you tell where one gear fragment was meant to fit when it’s such a mess?
        whatshisface 9y ago
        One thing that might help is that the collision vertex is slightly different each time. If you already had all of the tracks reconstructed, you'd find that you could point them together into n different centers of mass from which they originated.
        dukwon 9y ago
        This is already what happens. The problem is that forming tracks and vertices becomes much harder as the number increases.
        Analog24 9y ago
        Vertex finding is the first step in most track reconstruction algorithms. But it's still very difficult. The number of trajectories that can be formed from discrete points blows up combinatorically. When you have hundreds of thousands of points you usually don't end up with a few easy to find vertices.
      - dukwon 9y ago
        From what I've heard, the amount of pile-up ATLAS and CMS can handle is limited by the CPU time it takes to reconstruct events, which can be alleviated by throwing more resources at it, but it is much better to develop quicker reconstruction algorithms. Towards the end of last year, they had to start levelling the instantaneous luminosity to 75% of what they could achieve,† primarily to reduce the load on the grid. † Edit: the maximum peak luminosity is still 200% of the design value, so the performance is beyond initial expectations.
        9y ago
- 21 9y ago
  Zero. All the Bitcoin network can do is compute SHA-256 hashes. It's literally useless for anything else.
  - zamalek 9y ago
    I think they might mean the raw computational power (excluding ASICs).
- SiempreViernes 9y ago
  Dunno, how much memory and storage does the network have? The CERN grid currently[1] has 1 EB of storage and 750k of CPUs, and that pushes out 2 million jobs per day. From own experience of one of the 162 sites, you have something like 6 GB of ram per CPU core, and often jobs need more than that so that in practice you are memory bound. [1]: https://indico.cern.ch/event/466934/contributions/2524828/attachments/1490181/2315978/BigDataChallenges-EPS-Venice-080717.pdf https://indico.cern.ch/event/466934/contributions/2524828/at...
  - jamesblonde 9y ago
    They also have 0 GPUs. (I visited there a couple of weeks ago and talked to them, among other things, about this challenge).
- tim333 9y ago
  Maybe they could issue Cerncoins, one issued for each particle traced. Then if they went up in value the miners could switch to that.
deleted 9y ago
[deleted]
dean177 9y ago
“The top three performers of this phase hosted by Google-owned company Kaggle, will receive cash prizes of US$12,000, $8,000 and $5,000.” “incomparably more difficult”
- lima 9y ago
  It's a competition, people aren't doing it for the prizes.
  - mkagenius 9y ago
    Yes, but many certainly are doing it for the prizes.
    - danbruc 9y ago
      I am not sure, that sounds like a bad idea. Your chances of being one of the three wining teams are not terribly high and even if you manage to win, $12k for three months of work is not that much even if you work alone. Assuming there will be someone submitting a solution obtained using state of the art algorithms, it would probably be pretty naive to assume you have any significant chance of winning by just spending a couple of hours and throwing some generic machine learning algorithms at the problem. It immediately looked like a really interesting challenge to me, but after reading a bit about the state of the art it seems like three months is a pretty short time to come up with a meaningful result even if you could work on it fulltime. Many people already invested a lot of time in that problem and existing solutions are quite sophisticated and good. The material actually mentions that they expect that you will have to take into account things like adjacent detectors overlapping by a few pixels or how particles may light up several pixels if they hit the detector at very shallow angles and cross several pixels as they pass through the detector. The first thing someone probably considers is something like a Hough transformation and it turns out the creators of the challenge mention that in the material and submitted a solution based on this as a bench mark which achieves a score of about 20 %. If I read the related documents correctly, a meaningful result will require a score of at least about 90 % and the state of the art would probably be somewhere around 95 % to 98 %. The current leader is at 26.48 %, admittedly the challenge is only 5 days old. I am really curious where the scores will be at the end.
mkagenius 9y ago
Its very economical for companies to have 100s of AI programmers work on this (for a few of months) for $25k. But surprisingly the value they get out of it is certainly only 1 team's work.
- itissid 9y ago
  Can't they ask the top K teams for their solutions. Offer some smaller prize to them. I believe Kaggle supports that. Even if you have 10 top teams of 5 people each and gave them a sum of 300K total its damn cheap than hiring 4 or 5 full time engineers working for < 1 year.
  - jononor 9y ago
    Usually in Kaggle they ask every team elegible for a prize for their solution. I believe it is just as much to ensure there was no cheating/hacking, than to actually make use of the solution itself.
- jononor 9y ago
  This competition (and the followup) is official for important conferences in the field, so I expect many teams to publish their results. In general there is lots of discussion, tips & tricks posted online during such competitons, both in Kaggle forums and in blogs. So the value is certainly more than that off the winner, and most of it is available to everyone, not just the sponsors.
konschubert 9y ago
What's the news here? Using Machine Learning in data analysis is common practice in particle physics. Is the news that they want to use ML in the trigger selection now? Also, which one of the four experiments is doing this? EDIT: Ah, it's CMS.
- diehunde 9y ago
  I think it's that they posted part of their data on Kaggle to see if someone can come up with a better model
- dukwon 9y ago
  > Is the news that they want to use ML in the trigger selection now? Not quite (and that wouldn't be news). It's using ML for track reconstruction. Not even LHCb does this.
  - konschubert 9y ago
    Ah, cool! That's really quite interesting!
  - tempay 9y ago
    LHCb does use ML for trigger selections already[1]. [1] https://cds.cern.ch/record/2243560?ln=en https://cds.cern.ch/record/2243560?ln=en
    - dukwon 9y ago
      Yep. I meant we don't use it for track reconstruction.
  - ibeckermayer 9y ago
    I was working in UBC's ATLAS lab last summer, and this appeared to be the main focus of nearly all the PhD student's research. Using ML models for track reconstruction.
konschubert 9y ago
The problem with AI in particle physics is that you need to very well understand the efficiency of the selection ( rate of false negative and false positive classification). And this rate of course isn't uniform for all kinds of events. Thus, the uncertainty on the selection efficiency tends to grow with the complexity of the machine learning model. This directly hurts your sensitivy, the very thing you were trying to improve by using an AI - based process. It's a trade-off.
- jessriedel 9y ago
  From what I remember doing this years ago, the selection efficiency is usually determined through numerical simulation rather than analytically. So why would your estimate of the efficiency get worse with more complicated models?
  - dukwon 9y ago
    In general, simulation doesn't agree with data perfectly. The more the variables (and their correlations) aren't reproduced faithfully, the more the efficiency determined from simulation is likely to depart from the truth. It's even possible to accidentally train an algortihm to discriminate between simulation and data rather than signal and background. It's common practice to check data/simulation agreements between variables before using them for training. Reweighting procedures are used to improve agreement.
- Analog24 9y ago
  As already pointed out, detector efficiency is determined through extremely detailed simulations of the entire system. Those measurements are done in a completely orthogonal manner so the complexity of the model has nothing to do with the systematic uncertainties that arise from the selection efficiency.
  - konschubert 9y ago
    > As already pointed out, detector efficiency is determined through extremely detailed simulations of the entire system. It depends on the experiment, LHCb for example does not use simulated background. In any case, the more complex your model gets (number of variables) the exponentially more simulated Monte Carlo events you need to fill that multidimensional space.
jononor 9y ago
Curious to see what kind of techniques end up doing best on this. Worked on using GPUs in this areas back in 2010 for the ALICE project. Which was pretty straight port of an existing algorithm for finding certain phenomena given tracks. I believe the track reconstruction then used a multistage process ending with a Kalman filter.
nonbel 9y ago
>"In the new problem, she says, you have to find in the 100,000 points something like 10,000 arcs of ellipse." Does anyone know how long this currently takes?
- danbruc 9y ago
  That certainly depends on the amount of computing power you can throw at it and what level of accuracy you are trying to achieve. But the most naive attempt using a Hough transformation will probably take a couple of milliseconds on your average computer. However, the accuracy will be far from good enough with such a simple approach. I have no idea what amount of time state of the art algorithms will use, but I could well imagine that it is essentially a tunable parameter that is chosen to get the best accuracy given the amount of data you have to process and the time available to complete the task.
  - mattheww 9y ago
    Hough transform is basically useless for precision tracking in a high multiplicity environment. Tracks have 5 degrees of freedom, so the memory costs make it infeasible. I think ATLAS uses it in the trigger, where you don't need to actually reconstruct all tracks, just find out if there are a couple passing certain criteria.
    - danbruc 9y ago
      You don't have to accumulate the transformation result into an actual five dimensional array, you can just transform the points, keep them in a list, a quad tree, or whatever you like, and then just run a clustering algorithm on the transformed points. Probably complicated by the fact that every point can vote for several parameters so that you are not actually clustering points but something like lines or planes associated with the points. That seems also to be - but I did not look at the code - more or less what the creators of the challenge implemented and submitted as a benchmark implementation, admittedly with the expected poor performance score of only about 20 %.
      - 9y ago
amenod 9y ago
Someone posted what looks like a very nice summary of domain knowledge: https://www.kaggle.com/pranav84/beginner-s-guide-to-cern-s-particle-tracking-data/notebook https://www.kaggle.com/pranav84/beginner-s-guide-to-cern-s-p...
- jononor 9y ago
  Here is a very to-the-point summary of particle tracking: http://www.physics.iitm.ac.in/~sercehep2013/track2_Gagan_Mohanty.pdf http://www.physics.iitm.ac.in/~sercehep2013/track2_Gagan_Moh... - link found in Kaggle forums
danbruc 9y ago
A simplified model of the task. You throw 10,000 springs of various sizes (particle trajectories) into a box and record the intersection points (hits) with a set of (spring-penetrable) nested cylinders (detectors) in that box. You will get on average about 10 intersection points per spring. Now given those 100,000 intersection points, reconstruct the size, position, and orientation of the 10,000 springs. Lets add the facts that all the springs are aligned along one axis and that the cylinders are nested around that axis to be geometrically closer to reality. The hardness is due to possible ambiguities of what collection of springs caused the observed intersection points, imperfections in the helical shape of the springs and the cylindrical shape of the detectors, limited resolution of the measured intersection points, and missed (detector efficiency »only« 99 %) or added (detector noise) intersection points.
- Coding_Cat 9y ago
  Plus, the detector is multi-layered around the point of collision. Each layer measuring something slightly differently with different accuracies. Some springs are even completely invisible and you can only find them by looking at what is missing (neutrally charged particles).
mattheww 9y ago
The main thing of interest here is whether a system that knows about physics (traditional approaches) can be beaten by a system that has no a-priori knowledge of physics (OOB DL) or if someone will find a way to integrate physics knowledge into a DL approach.
DrNuke 9y ago
Hard hard problem, good luck to competitors. The new Kaggle-Google is a cheapskate trap but still fun a couple hours a week reading the forums and the most brilliant kernels.
jaddood 9y ago
I am sure the people at CERN have reasons in choosing machine learning for that purpose, but I don't see it ideal for this case. Machine learning might be able to give you your results faster, but generally with a lot lower accuracy. But suppose the teams got to a really good accuracy in their programs, it's still for the known and expected. In particle physics research — or any research for that matter — you cannot simply give it a shot, because your system might miss very interesting collisions while you think it's working perfectly fine. I think quantum computing can be really quite useful here given that the analysis can benefit from parallel processing. Should ML be necessary, I recommend develop it in a way that its doings are understandable. (I'm not sure they'll even read the comment to get the recommendation, but let's give it a try.) Explainable AI is becoming really significant these days, and some big companies and organisations are working on it. If I'm not mistaken, the DARPA is working on a project as such, so it's really not too far off.

132 points by okket 9y ago

JumpCrisscross 9y ago

Out of curiosity, what fraction of the Bitcoin network’s computing power would solve this computation wall?

amelius 9y ago
Is there even a bound on the computational power we'd need to make an advancement here?
- JumpCrisscross 9y ago
  > Although these techniques would be able to work out the paths even after the upgrades, “the problem is, they are too slow”, says Cécile Germain, a computer scientist at the University of Paris South in Orsay. Without major investment in new detector technologies, LHC physicists estimate that the collision rates will exceed the current capabilities by at least a factor of 10. Sounds like the LHC’s physicists have an idea.
- jrq 9y ago
  No, but CERN Is known to be careful with their PR. Presumably, and this is just my intuition speaking, a big enough cluster of computers would solve this, but they're taking an opportunity to experiment with different techniques and methods for this experiment, and that's pretty much it. If CERN had an unlimited budget, I suspect they'd do it however they did it before.
  - Analog24 9y ago
    This is not a problem that can be solved just by throwing more compute resources at it. It's not as simple as simply having too much data to process, the real issue is that each detector has a time resolution that goes down to about a nanosecond. If you get one collision per nanosecond then it's pretty straight forward to associate every one of the (possibly) million detector hits to a single event and reconstruct it accordingly. The issues arises when you have more than one event (i.e. collision) within each nanosecond window. You end up with detector readings for each event overlapping each other without a simple way of disambiguating them. This is called "pile up". When I last worked there in 2015 a typical pile up situation was having about 50 collisions per detector reading. It is no simple problem to simultaneously reconstruct 50 collisions from the same set of overlapping detector measurements.
    - cfadvan 9y ago
      Just to insert the usual analogy, if a single collision is the proverbial smashing of two clocks, and then trying to discern their makeup from the wreckage, this is akin to smashing dozens of clocks at the same time. How can you tell where one gear fragment was meant to fit when it’s such a mess?
      - whatshisface 9y ago
        One thing that might help is that the collision vertex is slightly different each time. If you already had all of the tracks reconstructed, you'd find that you could point them together into n different centers of mass from which they originated.
        dukwon 9y ago
        This is already what happens. The problem is that forming tracks and vertices becomes much harder as the number increases.
        Analog24 9y ago
        Vertex finding is the first step in most track reconstruction algorithms. But it's still very difficult. The number of trajectories that can be formed from discrete points blows up combinatorically. When you have hundreds of thousands of points you usually don't end up with a few easy to find vertices.
    - dukwon 9y ago
      From what I've heard, the amount of pile-up ATLAS and CMS can handle is limited by the CPU time it takes to reconstruct events, which can be alleviated by throwing more resources at it, but it is much better to develop quicker reconstruction algorithms. Towards the end of last year, they had to start levelling the instantaneous luminosity to 75% of what they could achieve,† primarily to reduce the load on the grid. † Edit: the maximum peak luminosity is still 200% of the design value, so the performance is beyond initial expectations.
      - 9y ago
21 9y ago
Zero. All the Bitcoin network can do is compute SHA-256 hashes. It's literally useless for anything else.
- zamalek 9y ago
  I think they might mean the raw computational power (excluding ASICs).
SiempreViernes 9y ago
Dunno, how much memory and storage does the network have? The CERN grid currently[1] has 1 EB of storage and 750k of CPUs, and that pushes out 2 million jobs per day. From own experience of one of the 162 sites, you have something like 6 GB of ram per CPU core, and often jobs need more than that so that in practice you are memory bound. [1]: https://indico.cern.ch/event/466934/contributions/2524828/attachments/1490181/2315978/BigDataChallenges-EPS-Venice-080717.pdf https://indico.cern.ch/event/466934/contributions/2524828/at...
- jamesblonde 9y ago
  They also have 0 GPUs. (I visited there a couple of weeks ago and talked to them, among other things, about this challenge).
tim333 9y ago
Maybe they could issue Cerncoins, one issued for each particle traced. Then if they went up in value the miners could switch to that.

deleted 9y ago

[deleted]

dean177 9y ago

“The top three performers of this phase hosted by Google-owned company Kaggle, will receive cash prizes of US$12,000, $8,000 and $5,000.” “incomparably more difficult”

lima 9y ago
It's a competition, people aren't doing it for the prizes.
- mkagenius 9y ago
  Yes, but many certainly are doing it for the prizes.
  - danbruc 9y ago
    I am not sure, that sounds like a bad idea. Your chances of being one of the three wining teams are not terribly high and even if you manage to win, $12k for three months of work is not that much even if you work alone. Assuming there will be someone submitting a solution obtained using state of the art algorithms, it would probably be pretty naive to assume you have any significant chance of winning by just spending a couple of hours and throwing some generic machine learning algorithms at the problem. It immediately looked like a really interesting challenge to me, but after reading a bit about the state of the art it seems like three months is a pretty short time to come up with a meaningful result even if you could work on it fulltime. Many people already invested a lot of time in that problem and existing solutions are quite sophisticated and good. The material actually mentions that they expect that you will have to take into account things like adjacent detectors overlapping by a few pixels or how particles may light up several pixels if they hit the detector at very shallow angles and cross several pixels as they pass through the detector. The first thing someone probably considers is something like a Hough transformation and it turns out the creators of the challenge mention that in the material and submitted a solution based on this as a bench mark which achieves a score of about 20 %. If I read the related documents correctly, a meaningful result will require a score of at least about 90 % and the state of the art would probably be somewhere around 95 % to 98 %. The current leader is at 26.48 %, admittedly the challenge is only 5 days old. I am really curious where the scores will be at the end.

mkagenius 9y ago

Its very economical for companies to have 100s of AI programmers work on this (for a few of months) for $25k. But surprisingly the value they get out of it is certainly only 1 team's work.

itissid 9y ago
Can't they ask the top K teams for their solutions. Offer some smaller prize to them. I believe Kaggle supports that. Even if you have 10 top teams of 5 people each and gave them a sum of 300K total its damn cheap than hiring 4 or 5 full time engineers working for < 1 year.
- jononor 9y ago
  Usually in Kaggle they ask every team elegible for a prize for their solution. I believe it is just as much to ensure there was no cheating/hacking, than to actually make use of the solution itself.
jononor 9y ago
This competition (and the followup) is official for important conferences in the field, so I expect many teams to publish their results. In general there is lots of discussion, tips & tricks posted online during such competitons, both in Kaggle forums and in blogs. So the value is certainly more than that off the winner, and most of it is available to everyone, not just the sponsors.

konschubert 9y ago

What's the news here? Using Machine Learning in data analysis is common practice in particle physics. Is the news that they want to use ML in the trigger selection now? Also, which one of the four experiments is doing this? EDIT: Ah, it's CMS.

diehunde 9y ago
I think it's that they posted part of their data on Kaggle to see if someone can come up with a better model
dukwon 9y ago
> Is the news that they want to use ML in the trigger selection now? Not quite (and that wouldn't be news). It's using ML for track reconstruction. Not even LHCb does this.
- konschubert 9y ago
  Ah, cool! That's really quite interesting!
- tempay 9y ago
  LHCb does use ML for trigger selections already[1]. [1] https://cds.cern.ch/record/2243560?ln=en https://cds.cern.ch/record/2243560?ln=en
  - dukwon 9y ago
    Yep. I meant we don't use it for track reconstruction.
- ibeckermayer 9y ago
  I was working in UBC's ATLAS lab last summer, and this appeared to be the main focus of nearly all the PhD student's research. Using ML models for track reconstruction.

konschubert 9y ago

The problem with AI in particle physics is that you need to very well understand the efficiency of the selection ( rate of false negative and false positive classification). And this rate of course isn't uniform for all kinds of events. Thus, the uncertainty on the selection efficiency tends to grow with the complexity of the machine learning model. This directly hurts your sensitivy, the very thing you were trying to improve by using an AI - based process. It's a trade-off.

jessriedel 9y ago
From what I remember doing this years ago, the selection efficiency is usually determined through numerical simulation rather than analytically. So why would your estimate of the efficiency get worse with more complicated models?
- dukwon 9y ago
  In general, simulation doesn't agree with data perfectly. The more the variables (and their correlations) aren't reproduced faithfully, the more the efficiency determined from simulation is likely to depart from the truth. It's even possible to accidentally train an algortihm to discriminate between simulation and data rather than signal and background. It's common practice to check data/simulation agreements between variables before using them for training. Reweighting procedures are used to improve agreement.
Analog24 9y ago
As already pointed out, detector efficiency is determined through extremely detailed simulations of the entire system. Those measurements are done in a completely orthogonal manner so the complexity of the model has nothing to do with the systematic uncertainties that arise from the selection efficiency.
- konschubert 9y ago
  > As already pointed out, detector efficiency is determined through extremely detailed simulations of the entire system. It depends on the experiment, LHCb for example does not use simulated background. In any case, the more complex your model gets (number of variables) the exponentially more simulated Monte Carlo events you need to fill that multidimensional space.

jononor 9y ago

Curious to see what kind of techniques end up doing best on this. Worked on using GPUs in this areas back in 2010 for the ALICE project. Which was pretty straight port of an existing algorithm for finding certain phenomena given tracks. I believe the track reconstruction then used a multistage process ending with a Kalman filter.

nonbel 9y ago

>"In the new problem, she says, you have to find in the 100,000 points something like 10,000 arcs of ellipse." Does anyone know how long this currently takes?

danbruc 9y ago
That certainly depends on the amount of computing power you can throw at it and what level of accuracy you are trying to achieve. But the most naive attempt using a Hough transformation will probably take a couple of milliseconds on your average computer. However, the accuracy will be far from good enough with such a simple approach. I have no idea what amount of time state of the art algorithms will use, but I could well imagine that it is essentially a tunable parameter that is chosen to get the best accuracy given the amount of data you have to process and the time available to complete the task.
- mattheww 9y ago
  Hough transform is basically useless for precision tracking in a high multiplicity environment. Tracks have 5 degrees of freedom, so the memory costs make it infeasible. I think ATLAS uses it in the trigger, where you don't need to actually reconstruct all tracks, just find out if there are a couple passing certain criteria.
  - danbruc 9y ago
    You don't have to accumulate the transformation result into an actual five dimensional array, you can just transform the points, keep them in a list, a quad tree, or whatever you like, and then just run a clustering algorithm on the transformed points. Probably complicated by the fact that every point can vote for several parameters so that you are not actually clustering points but something like lines or planes associated with the points. That seems also to be - but I did not look at the code - more or less what the creators of the challenge implemented and submitted as a benchmark implementation, admittedly with the expected poor performance score of only about 20 %.
    - 9y ago

amenod 9y ago

Someone posted what looks like a very nice summary of domain knowledge: https://www.kaggle.com/pranav84/beginner-s-guide-to-cern-s-particle-tracking-data/notebook https://www.kaggle.com/pranav84/beginner-s-guide-to-cern-s-p...

jononor 9y ago
Here is a very to-the-point summary of particle tracking: http://www.physics.iitm.ac.in/~sercehep2013/track2_Gagan_Mohanty.pdf http://www.physics.iitm.ac.in/~sercehep2013/track2_Gagan_Moh... - link found in Kaggle forums

danbruc 9y ago

A simplified model of the task. You throw 10,000 springs of various sizes (particle trajectories) into a box and record the intersection points (hits) with a set of (spring-penetrable) nested cylinders (detectors) in that box. You will get on average about 10 intersection points per spring. Now given those 100,000 intersection points, reconstruct the size, position, and orientation of the 10,000 springs. Lets add the facts that all the springs are aligned along one axis and that the cylinders are nested around that axis to be geometrically closer to reality. The hardness is due to possible ambiguities of what collection of springs caused the observed intersection points, imperfections in the helical shape of the springs and the cylindrical shape of the detectors, limited resolution of the measured intersection points, and missed (detector efficiency »only« 99 %) or added (detector noise) intersection points.

Coding_Cat 9y ago
Plus, the detector is multi-layered around the point of collision. Each layer measuring something slightly differently with different accuracies. Some springs are even completely invisible and you can only find them by looking at what is missing (neutrally charged particles).

mattheww 9y ago

The main thing of interest here is whether a system that knows about physics (traditional approaches) can be beaten by a system that has no a-priori knowledge of physics (OOB DL) or if someone will find a way to integrate physics knowledge into a DL approach.

DrNuke 9y ago

Hard hard problem, good luck to competitors. The new Kaggle-Google is a cheapskate trap but still fun a couple hours a week reading the forums and the most brilliant kernels.

jaddood 9y ago

I am sure the people at CERN have reasons in choosing machine learning for that purpose, but I don't see it ideal for this case. Machine learning might be able to give you your results faster, but generally with a lot lower accuracy. But suppose the teams got to a really good accuracy in their programs, it's still for the known and expected. In particle physics research — or any research for that matter — you cannot simply give it a shot, because your system might miss very interesting collisions while you think it's working perfectly fine. I think quantum computing can be really quite useful here given that the analysis can benefit from parallel processing. Should ML be necessary, I recommend develop it in a way that its doings are understandable. (I'm not sure they'll even read the comment to get the recommendation, but let's give it a try.) Explainable AI is becoming really significant these days, and some big companies and organisations are working on it. If I'm not mistaken, the DARPA is working on a project as such, so it's really not too far off.