Allow filtering via masking

Nobody has claimed this yet.

I wanted to reproduce the following pandas workflow (all_forced_sources_w19 is flat pandas df with sources):

> '''Identify all columns that contain 'flag' '''
> flag_cols = [col for col in all_forced_sources_w19.columns if 'flag' in col.lower()]
>
> '''Exclude rows where any flag column is True'''
> flag_mask = ~(all_forced_sources_w19[flag_cols].any(axis=1)) # True where all flags are False
> all_forced_sources_w19_clean = all_forced_sources_w19[flag_mask]

The solution we found via `.query` was something like

> '''Identify flag columns'''
>flag_cols = [col for col in dia_object_lc_computed.diaObjectForcedSource.nest.fields if 'flag' in col.lower()]
>'''Build the condition string, e.g., "flag1 == False & flag2 == False & ..." '''
> query_str = " & ".join([f"diaObjectForcedSource.{col} == False" for col in flag_cols])
> '''Apply query directly to the nested column'''
> dia_object_lc_computed_filtered = dia_object_lc_computed.query(query_str)

I wish I was able to more directly mask, something like

> flag_mask = ~(dia_object_lc_computed.diaObjectForcedSource.nest.fields[flag_cols].any(axis=1))
> dia_object_lc_computed.diaObjectForcedSource = dia_object_lc_computed.diaObjectForcedSource[flag_mask]

Review the existing `.query` path and nested field access shown in the issue, then locate the entry point for masking nested data. Define how selecting `flag_cols` and applying a boolean row mask should behave, and verify that the direct masking workflow produces the same filtered rows as the demonstrated query workaround.

Written by the indexing model from the issue text.