First, let me break your question into two pieces:
What automated methods are used by physicists to (i) cull the vast
amount of data collected and (ii) find results that are of interest?
Regarding (i), the technique used is a so-called 'trigger system'. A trigger system decides which events to record to disk and which to discard. It is designed to reduce the frequency of events from about one billion per second to about 1000 per second. The trigger system is composed of three parts:
- The Level 1 trigger, implemented as custom-built hardware, takes coarse-grained information about an event from the calorimeter. It makes a quick decision (about 2 μs) about whether to proceed with the event.
- The Level 2 trigger, implemented as software, takes further information, but not the whole event, to make a decision about whether to veto. This level uses e.g. Kalman filtering to judge whether tracks lead back to a hard scatter
- Level 3 and onwards are software triggers that consider all information about an event
If an event passes the triggers it is saved to disk. Now regarding (ii), finding results that are of interest. Well, what is of interest depends greatly on what you are looking for. Broadly speaking, whatever you are looking for, the same signature can be mimicked by other, so-called background processes. An experimental team will therefore select events in which we expect a high ratio of signal to background.
The features to select upon for were historically decided using physical insight guided by simulations. Recently though, we have indeed seen the advent of machine learning techniques to tackle this problem. See e.g., 1807.06038, 1902.02634, 1806.02350. This is typically seen as a classification problem: we must classify events as signal or background.
I should point our that machine learning is popular in a smaller sub-problem: jet reconstruction, where one tries to reconstruct so-called jets from the hadronic calorimeter. See e.g., 1609.00607.