How does Elasticsearch aggregate or weight scores from two sub queries ("bool query" and "decay function")

I have a complicated Elasticsearch query like the following example. This query has two sub queries: a weighted bool query and a decay function. I am trying to understand how Elasticsearch aggregrates the scores from each sub queries. If I run the first sub query alone (the weighted bool query), my top score is 20. If I run the second sub query alone (the decay function), my score is 1. However, if I run both sub queries together, my top score is 15. Can someone explain this?

My second related question is how to weight the scores from the two sub queries?

query = { "function_score": {

    "query": {
      "bool": {
        "should": [
          {'match': {'title': {'query': 'Quantum computing', 'boost': 1}}}, 
          {'match': {'author': {'query': 'Richard Feynman', 'boost': 2}}}
        ]
      },
    },

    "functions": [
      { "exp":  # a built-in exponential decay function 
        { 
          "publication_date": {
            "origin": "2000-01-01",
            "offset": "7d",
            "scale":  "180d",
            "decay": 0.5
          },
        },
     }]

}}

I found the answer myself by reading the elasticsearch document on the usage of function_score. function_score has a parameter boost_mode that specifies how query score and function score are combined. By default, boost_mode is set to multiply.

Besides the default multiply method, we could also set boost_mode to avg, and add a parameter weight to the above decay function exp, then the combined score will be: ( the_bool_query_score + the_decay_function_score * weight ) / ( 1 + weight ).