Julia - Understanding JuMP Gurobi outputs - optimization

Question

I am often using Gurobi with JuMP and I noticed that there are still parts of its outputs that I don't understand. Unless it is documented somewhere and the link would be most welcome, could you help understand the following? :)
Found heuristic solution: objective 5820.0000000
Presolve removed 33 rows and 11 columns
Presolve time: 0.00s
Presolved: 607 rows, 331 columns, 2445 nonzeros
Variable types: 111 continuous, 220 integer (220 binary)
Root relaxation: objective 1.157500e+03, 64 iterations, 0.00 seconds
Nodes | Current Node | Objective Bounds | Work
Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time
0 0 1157.50000 0 25 5820.00000 1157.50000 80.1% - 0s
H 0 0 2535.0000000 1157.50000 54.3% - 0s
0 0 1236.00000 0 23 2535.00000 1236.00000 51.2% - 0s
0 0 1273.65351 0 41 2535.00000 1273.65351 49.8% - 0s
0 0 1274.59375 0 41 2535.00000 1274.59375 49.7% - 0s
0 0 1274.69841 0 42 2535.00000 1274.69841 49.7% - 0s
0 0 1309.98305 0 42 2535.00000 1309.98305 48.3% - 0s
0 0 1310.26027 0 42 2535.00000 1310.26027 48.3% - 0s
0 0 1340.01176 0 47 2535.00000 1340.01176 47.1% - 0s
0 0 1342.47826 0 49 2535.00000 1342.47826 47.0% - 0s
0 0 1342.60000 0 49 2535.00000 1342.60000 47.0% - 0s
0 0 1362.32468 0 50 2535.00000 1362.32468 46.3% - 0s
0 0 1363.08000 0 49 2535.00000 1363.08000 46.2% - 0s
0 0 1363.13077 0 49 2535.00000 1363.13077 46.2% - 0s
0 0 1370.79545 0 53 2535.00000 1370.79545 45.9% - 0s
0 0 1375.50000 0 52 2535.00000 1375.50000 45.7% - 0s
0 0 1375.50000 0 52 2535.00000 1375.50000 45.7% - 0s
0 0 1376.70025 0 52 2535.00000 1376.70025 45.7% - 0s
0 0 1376.70122 0 53 2535.00000 1376.70122 45.7% - 0s
0 0 1376.70122 0 53 2535.00000 1376.70122 45.7% - 0s
0 2 1376.98418 0 53 2535.00000 1376.98418 45.7% - 0s
* 255 157 14 2457.0000000 1473.00000 40.0% 22.5 0s
H 407 223 2397.0000000 1548.00000 35.4% 20.3 0s
* 1962 758 22 2355.0000000 1772.85714 24.7% 16.8 0s
*14326 2205 27 2343.0000000 2088.50000 10.9% 15.7 3s
From what I think I already know, in order of apparition:
Presolve
Found heuristic solution: objective 5820, Gurobi launched a presolve and found a solution of value 5820
Presolve removed 33 redundant or useless variables as well as 11 constraints?
Presvoled: sizes of the model presolved
Types of variables: some are binary and some are continuous.
Root relaxation: LP relaxation has an objective of 1 157.5
Nodes
Expl (1): * empty or H, I don't know the difference when there is these symbols but I noticed all rows with a given symbol (H, *, or empty) will have same outputs.
Expl and Unexpl number of explored nodes and unexplored nodes in the solving tree.
Current Node
Obj: objective of current node being explored?
Depth: tree's depth where solving is currently
IntInf: I have no idea for this one
Objective Bounds
Incumbent: current value of best valid solution found
BestBd: Best lower bound currently found
Gap: This is probably a gap between current best solution and best bound but I couldn't deduce how it is computed.
Work
It/Node: Here we need to find what is an iteration
Time: Time spent in solving until then.

Answer 1

The details can be found at https://www.gurobi.com/documentation/9.1/refman/mip_logging.html.
Let me just cite those that answer your question:
The Nodes section (the first two columns) provides general quantitative information on the progress of the search. The first column shows the number of branch-and-cut nodes that have been explored to that point, while the second shows the number of leaf nodes in the search tree that remain unexplored. At times, there will be an H or * character at the beginning of the output line. These indicate that a new feasible solution has been found, either by a MIP heuristic (H) or by branching (*).
The Current Node section provides information on the specific node that was explored at that point in the branch-and-cut tree. It shows the objective of the associated relaxation, the depth of that node in the branch-and-cut tree, and the number of integer variables that have non-integral values in the associated relaxation.
The Objective Bounds section provides information on the best known objective value for a feasible solution (i.e., the objective value of the current incumbent), and the current objective bound provided by leaf nodes of the search tree. The optimal objective value is always between these two values. The third column in this section (Gap) shows the relative gap between the two objective bounds. When this gap is smaller than the MIPGap parameter, optimization terminates.
The Work section of the log provides information on how much work has been performed to that point. The first column shows the average number of simplex iterations performed per node in the branch-and-cut tree. The final column shows the elapsed time since the solve began.
Looking at your log you are approaching very quickly to optimality and you get the Gap=0.01% solution in probably half minute or so.