Generate plots corresponding to where recent (mid-2020+) research on a given topic / related to a given paper has appeared on ArXiv.

The examples below use the pre-trained astro-ph-GA-23May2021 model along with a compilation of author affiliations from ADS to find relevant papers and from that, use author affiliations to find how strongly a certain place/institute contributes to research on the given topic/paper. This extension to the project was undertaken largely to be useful for prospective grad students and postdocs to help better find places to apply to.

Available options are:

• return_n: to specify how deep the search should go. ~3000 is the full dataset, generally numbers in the 3-100 range return useful results depending on how broad you want the search to be.

• doc_id and input_type: can be keywords, or an ArXiv id (see examples below for usage)

• plt_radius: sets the radius of circles corresponding to each point. change in concert with return_n.

Note

This tutorial uses a very small list of affiliations (corresponding to ~2500 recent papers) for this exercise, so the results may not necessarily generalise well beyond that. If you’re interested in expanding this, please get in touch with me.

import chaotic_neural as cn

#mapper_model_data = cn.load_trained_doc2vec_model('galaxies_all', cn_dir = '../../chaotic_neural/')

model_data = cn.load_trained_doc2vec_model('astro-ph-GA-23May2021', cn_dir = '../../chaotic_neural/')
model, all_titles, all_abstracts, all_authors, train_corpus, test_corpus = model_data

with open("../../chaotic_neural/data/astro-ph-GA-23May2021_recent_affils.pkl", "rb") as fp:   #Pickling
        recent_affils = cn.pickle.load(fp)

with open("../../chaotic_neural/data/astro-ph-GA-23May2021_recent_latlon.pkl", "rb") as fp:   #Pickling
    [place_names, place_locs, all_ids] = cn.pickle.load(fp)

mapper_model_data = [model, all_titles, all_abstracts, all_authors, all_ids, train_corpus, test_corpus, recent_affils, place_names, place_locs]

keyword search example

cn.list_similar_locations(mapper_model_data, doc_id = ['sed','fitting'],
                       input_type='keywords',
                       return_n=100)

Keyword(s):  ['sed', 'fitting']
multi-keyword
----

ArXiv ID search example

cn.list_similar_locations(mapper_model_data, doc_id = 2001.00952,
                       input_type='arxiv_id',
                       return_n=10)

ArXiv id:  2001.00952
Title: The First Habitable Zone Earth-sized Planet from TESS. I: Validation of
  the TOI-700 System
----

Showing (roughly) the full sample, to get an idea of the implicit prior.

cn.list_similar_locations(mapper_model_data, doc_id = ['galaxy'],
                       input_type='keywords',
                       return_n=3000, plt_radius = 3)

Keyword(s):  ['galaxy']
----